Insect_Detection.ipynb

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  "metadata": {
    "colab": {
      "name": "Insect Detection.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/siddhant3646/AceWithPace2_rrsst/blob/master/Insect_Detection.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dvN2-COJ9w4k"
      },
      "source": [
        "# Introduction\n",
        "\n",
        "In this notebook, we train [YOLOv4 tiny](https://github.com/AlexeyAB/darknet/issues/6067) on custom data. We will convert this to a TensorFlow representation and finally TensorFlow Lite file to use on device.\n",
        "\n",
        "We also recommend reading our blog post on [How To Train YOLOv4 And Convert It To TensorFlow (And TensorFlow Lite!)](https://blog.roboflow.ai/how-to-train-yolov4-and-convert-it-to-tensorflow) side by side.\n",
        "\n",
        "We will take the following steps to get YOLOv4 from training on custom data to a TensorFlow (and TensorFlow Lite) representation:\n",
        "\n",
        "\n",
        "1.   Set up the Custom Dataset\n",
        "2.   Train the Model with Darknet\n",
        "3.   Convert the weights to TensorFlow's .pb representation\n",
        "4.   Convert the weights to TensorFlow Lite's .tflite representation\n",
        "\n",
        "\n",
        "When you are done you will have a custom detector that you can use. It will make inference like this:\n",
        "\n",
        "#### ![Roboflow Workmark](https://i.imgur.com/L0n564N.png)\n",
        "\n",
        "### **Reach out for support**\n",
        "\n",
        "If you run into any hurdles on your own data set or just want to share some cool results in your own domain, [reach out!](https://roboflow.ai) \n",
        "\n",
        "\n",
        "\n",
        "#### ![Roboflow Workmark](https://i.imgur.com/WHFqYSJ.png)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ef1_2DwjF9Cy"
      },
      "source": [
        "#1. Set up the Custom Dataset\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zbniFj-eSimL"
      },
      "source": [
        "We'll use Roboflow to convert our dataset from any format to the YOLO Darknet format. \n",
        "\n",
        "1. To do so, create a free [Roboflow account](https://app.roboflow.ai).\n",
        "2. Upload your images and their annotations (in any format: VOC XML, COCO JSON, TensorFlow CSV, etc).\n",
        "3. Apply preprocessing and augmentation steps you may like. We recommend at least `auto-orient` and a `resize` to 416x416. Generate your dataset.\n",
        "4. Export your dataset in the **YOLO Darknet format**.\n",
        "5. Copy your download link, and paste it below.\n",
        "\n",
        "See our [blog post](https://blog.roboflow.ai/training-yolov4-on-a-custom-dataset/) for greater detail.\n",
        "\n",
        "In this example, I used the open source [BCCD Dataset](https://public.roboflow.ai/object-detection/bccd). (You can `fork` it to your Roboflow account to follow along.)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Cdj4tmT5Cmdl",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7b2705cf-5355-4042-ed12-d9e7cecf918d"
      },
      "source": [
        "#if you already have YOLO darknet format, you can skip this step\n",
        "#otherwise we recommend formatting in Roboflow\n",
        "%cd /content\n",
        "%mkdir dataset\n",
        "%cd ./dataset\n",
        "!curl -L \"https://app.roboflow.com/ds/OXo2HjjgzK?key=u26Wz9utfC\" > roboflow.zip; unzip roboflow.zip; rm roboflow.zip"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content\n",
            "/content/dataset\n",
            "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
            "                                 Dload  Upload   Total   Spent    Left  Speed\n",
            "100   890  100   890    0     0   1047      0 --:--:-- --:--:-- --:--:--  1047\n",
            "100 8237k  100 8237k    0     0  4885k      0  0:00:01  0:00:01 --:--:-- 12.7M\n",
            "Archive:  roboflow.zip\n",
            " extracting: README.roboflow.txt     \n",
            "   creating: test/\n",
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            "   creating: train/\n",
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            " extracting: train/GundhiBug (12)_jpg.rf.89b163858e4cda3ebd86699d331dd6e0.jpg  \n",
            " extracting: train/GundhiBug (12)_jpg.rf.89b163858e4cda3ebd86699d331dd6e0.txt  \n",
            " extracting: train/GundhiBug (12)_jpg.rf.d5bf0c252127c234589efb6b08abc683.jpg  \n",
            " extracting: train/GundhiBug (12)_jpg.rf.d5bf0c252127c234589efb6b08abc683.txt  \n",
            " extracting: train/GundhiBug (13)_jpg.rf.3c11c4116ebc1a82d008e68442205e8c.jpg  \n",
            " extracting: train/GundhiBug (13)_jpg.rf.3c11c4116ebc1a82d008e68442205e8c.txt  \n",
            " extracting: train/GundhiBug (13)_jpg.rf.addb97967fef8a4f03c0bff1b94b6a1f.jpg  \n",
            " extracting: train/GundhiBug (13)_jpg.rf.addb97967fef8a4f03c0bff1b94b6a1f.txt  \n",
            " extracting: train/GundhiBug (13)_jpg.rf.f8aca274b2ce20beeb370064b395fec9.jpg  \n",
            " extracting: train/GundhiBug (13)_jpg.rf.f8aca274b2ce20beeb370064b395fec9.txt  \n",
            " extracting: train/GundhiBug (14)_jpg.rf.22adffebca1c4a952ef47faac2c893a0.jpg  \n",
            " extracting: train/GundhiBug (14)_jpg.rf.22adffebca1c4a952ef47faac2c893a0.txt  \n",
            " extracting: train/GundhiBug (14)_jpg.rf.4a7da45de03a9a037aa6ad0855d85280.jpg  \n",
            " extracting: train/GundhiBug (14)_jpg.rf.4a7da45de03a9a037aa6ad0855d85280.txt  \n",
            " extracting: train/GundhiBug (14)_jpg.rf.c0f8c7944437c933211da9eabd14a7e3.jpg  \n",
            " extracting: train/GundhiBug (14)_jpg.rf.c0f8c7944437c933211da9eabd14a7e3.txt  \n",
            " extracting: train/GundhiBug (15)_jpg.rf.31b7807045eb7f2a61955a9fc18f1f96.jpg  \n",
            " extracting: train/GundhiBug (15)_jpg.rf.31b7807045eb7f2a61955a9fc18f1f96.txt  \n",
            " extracting: train/GundhiBug (15)_jpg.rf.53203bb6e076ea501f5ba73b259641a5.jpg  \n",
            " extracting: train/GundhiBug (15)_jpg.rf.53203bb6e076ea501f5ba73b259641a5.txt  \n",
            " extracting: train/GundhiBug (15)_jpg.rf.6c1a93f7d74e4f9ee99c22db97114cc3.jpg  \n",
            " extracting: train/GundhiBug (15)_jpg.rf.6c1a93f7d74e4f9ee99c22db97114cc3.txt  \n",
            " extracting: train/GundhiBug (16)_jpg.rf.20ce63d43a7fa52d0f0a2f6805f4e05f.jpg  \n",
            " extracting: train/GundhiBug (16)_jpg.rf.20ce63d43a7fa52d0f0a2f6805f4e05f.txt  \n",
            " extracting: train/GundhiBug (16)_jpg.rf.2520a03a88aca4cdeb8e62591970256f.jpg  \n",
            " extracting: train/GundhiBug (16)_jpg.rf.2520a03a88aca4cdeb8e62591970256f.txt  \n",
            " extracting: train/GundhiBug (16)_jpg.rf.64a4dc9898966a3a2cc8f1020af2f7a9.jpg  \n",
            " extracting: train/GundhiBug (16)_jpg.rf.64a4dc9898966a3a2cc8f1020af2f7a9.txt  \n",
            " extracting: train/GundhiBug (17)_jpg.rf.1bd7c50c68a944427bee353b36a13b7e.jpg  \n",
            " extracting: train/GundhiBug (17)_jpg.rf.1bd7c50c68a944427bee353b36a13b7e.txt  \n",
            " extracting: train/GundhiBug (17)_jpg.rf.b92bb0b16fea26124fce552e6ca2b400.jpg  \n",
            " extracting: train/GundhiBug (17)_jpg.rf.b92bb0b16fea26124fce552e6ca2b400.txt  \n",
            " extracting: train/GundhiBug (17)_jpg.rf.e4efdfa71466600e8ae4944f75772f5e.jpg  \n",
            " extracting: train/GundhiBug (17)_jpg.rf.e4efdfa71466600e8ae4944f75772f5e.txt  \n",
            " extracting: train/GundhiBug (18)_jpg.rf.0f708e06b03c564a89252c70630ae357.jpg  \n",
            " extracting: train/GundhiBug (18)_jpg.rf.0f708e06b03c564a89252c70630ae357.txt  \n",
            " extracting: train/GundhiBug (18)_jpg.rf.83a40264eb16911873908618a10ac2fc.jpg  \n",
            " extracting: train/GundhiBug (18)_jpg.rf.83a40264eb16911873908618a10ac2fc.txt  \n",
            " extracting: train/GundhiBug (18)_jpg.rf.cbdafcb0b218b642aab4d4d76f264de8.jpg  \n",
            " extracting: train/GundhiBug (18)_jpg.rf.cbdafcb0b218b642aab4d4d76f264de8.txt  \n",
            " extracting: train/GundhiBug (19)_jpg.rf.815e02f1c6d8f179edf500f531ff03fc.jpg  \n",
            " extracting: train/GundhiBug (19)_jpg.rf.815e02f1c6d8f179edf500f531ff03fc.txt  \n",
            " extracting: train/GundhiBug (19)_jpg.rf.d47fbe4f5129a6ede26dcaf436511ff4.jpg  \n",
            " extracting: train/GundhiBug (19)_jpg.rf.d47fbe4f5129a6ede26dcaf436511ff4.txt  \n",
            " extracting: train/GundhiBug (19)_jpg.rf.ea7122256e885a486f24a7df89532060.jpg  \n",
            " extracting: train/GundhiBug (19)_jpg.rf.ea7122256e885a486f24a7df89532060.txt  \n",
            " extracting: train/GundhiBug (1a)_jpeg.rf.16900b454f6aea98713a921990776629.jpg  \n",
            " extracting: train/GundhiBug (1a)_jpeg.rf.16900b454f6aea98713a921990776629.txt  \n",
            " extracting: train/GundhiBug (1a)_jpeg.rf.61557d6a052c85baefc41e94476f561b.jpg  \n",
            " extracting: train/GundhiBug (1a)_jpeg.rf.61557d6a052c85baefc41e94476f561b.txt  \n",
            " extracting: train/GundhiBug (1a)_jpeg.rf.c5662f31f0977acabd582ba596b650c4.jpg  \n",
            " extracting: train/GundhiBug (1a)_jpeg.rf.c5662f31f0977acabd582ba596b650c4.txt  \n",
            " extracting: train/GundhiBug (2)_jpeg.rf.2bd4c1594fc015911f0cde54de120287.jpg  \n",
            " extracting: train/GundhiBug (2)_jpeg.rf.2bd4c1594fc015911f0cde54de120287.txt  \n",
            " extracting: train/GundhiBug (2)_jpeg.rf.66497aa19ff5e2ea7d420a8b374c00b2.jpg  \n",
            " extracting: train/GundhiBug (2)_jpeg.rf.66497aa19ff5e2ea7d420a8b374c00b2.txt  \n",
            " extracting: train/GundhiBug (2)_jpeg.rf.a46cacd41fac1e2190cf1e2e87bbbcf0.jpg  \n",
            " extracting: train/GundhiBug (2)_jpeg.rf.a46cacd41fac1e2190cf1e2e87bbbcf0.txt  \n",
            " extracting: train/GundhiBug (20)_jpg.rf.4cf0565e34a5ae8c2a869b7cbbefaa0f.jpg  \n",
            " extracting: train/GundhiBug (20)_jpg.rf.4cf0565e34a5ae8c2a869b7cbbefaa0f.txt  \n",
            " extracting: train/GundhiBug (20)_jpg.rf.8d0db99a9a8adee70e9a65706962d279.jpg  \n",
            " extracting: train/GundhiBug (20)_jpg.rf.8d0db99a9a8adee70e9a65706962d279.txt  \n",
            " extracting: train/GundhiBug (20)_jpg.rf.9c10f9fa1812c06b2a3a92204b412221.jpg  \n",
            " extracting: train/GundhiBug (20)_jpg.rf.9c10f9fa1812c06b2a3a92204b412221.txt  \n",
            " extracting: train/GundhiBug (21)_jpg.rf.1052ef0530234de7b31cc12b65a8bac0.jpg  \n",
            " extracting: train/GundhiBug (21)_jpg.rf.1052ef0530234de7b31cc12b65a8bac0.txt  \n",
            " extracting: train/GundhiBug (21)_jpg.rf.3632a193546479bab18310c9a1264d82.jpg  \n",
            " extracting: train/GundhiBug (21)_jpg.rf.3632a193546479bab18310c9a1264d82.txt  \n",
            " extracting: train/GundhiBug (21)_jpg.rf.92d9500482284bdb1c4298f56a32b7fa.jpg  \n",
            " extracting: train/GundhiBug (21)_jpg.rf.92d9500482284bdb1c4298f56a32b7fa.txt  \n",
            " extracting: train/GundhiBug (22)_jpg.rf.6c0e9e24e253f05ac303fe357537ab00.jpg  \n",
            " extracting: train/GundhiBug (22)_jpg.rf.6c0e9e24e253f05ac303fe357537ab00.txt  \n",
            " extracting: train/GundhiBug (22)_jpg.rf.93525c6f43fb6ad16490564cf5a085e7.jpg  \n",
            " extracting: train/GundhiBug (22)_jpg.rf.93525c6f43fb6ad16490564cf5a085e7.txt  \n",
            " extracting: train/GundhiBug (22)_jpg.rf.fe06fc1dda360dc19462c82732d13499.jpg  \n",
            " extracting: train/GundhiBug (22)_jpg.rf.fe06fc1dda360dc19462c82732d13499.txt  \n",
            " extracting: train/GundhiBug (23)_jpg.rf.3f3863be951d4e813e0dad923fce9e88.jpg  \n",
            " extracting: train/GundhiBug (23)_jpg.rf.3f3863be951d4e813e0dad923fce9e88.txt  \n",
            " extracting: train/GundhiBug (23)_jpg.rf.72536d863369cee52c3ad4c6f971f6a8.jpg  \n",
            " extracting: train/GundhiBug (23)_jpg.rf.72536d863369cee52c3ad4c6f971f6a8.txt  \n",
            " extracting: train/GundhiBug (23)_jpg.rf.75c093114473a25385498bc0573ab86d.jpg  \n",
            " extracting: train/GundhiBug (23)_jpg.rf.75c093114473a25385498bc0573ab86d.txt  \n",
            " extracting: train/GundhiBug (24)_jpg.rf.26f7b5b6a743163a869fc39289181715.jpg  \n",
            " extracting: train/GundhiBug (24)_jpg.rf.26f7b5b6a743163a869fc39289181715.txt  \n",
            " extracting: train/GundhiBug (24)_jpg.rf.5c788e1ae577f987194e3897e86804f4.jpg  \n",
            " extracting: train/GundhiBug (24)_jpg.rf.5c788e1ae577f987194e3897e86804f4.txt  \n",
            " extracting: train/GundhiBug (24)_jpg.rf.8be583b3d0a7fa1ae42895cad9caaaf8.jpg  \n",
            " extracting: train/GundhiBug (24)_jpg.rf.8be583b3d0a7fa1ae42895cad9caaaf8.txt  \n",
            " extracting: train/GundhiBug (25)_jpg.rf.5d64a5012f23e2709cb826bf1232a7c6.jpg  \n",
            " extracting: train/GundhiBug (25)_jpg.rf.5d64a5012f23e2709cb826bf1232a7c6.txt  \n",
            " extracting: train/GundhiBug (25)_jpg.rf.67820672fd4416b594de5b1a6cb93981.jpg  \n",
            " extracting: train/GundhiBug (25)_jpg.rf.67820672fd4416b594de5b1a6cb93981.txt  \n",
            " extracting: train/GundhiBug (25)_jpg.rf.a52d93170d4e9b0f64e989b23805907b.jpg  \n",
            " extracting: train/GundhiBug (25)_jpg.rf.a52d93170d4e9b0f64e989b23805907b.txt  \n",
            " extracting: train/GundhiBug (27)_jpg.rf.4b8b6ef0bef8ec156e1fcc8f392eebc3.jpg  \n",
            " extracting: train/GundhiBug (27)_jpg.rf.4b8b6ef0bef8ec156e1fcc8f392eebc3.txt  \n",
            " extracting: train/GundhiBug (27)_jpg.rf.7d541f0b2dd34b44c5da6a88ae8f7f3d.jpg  \n",
            " extracting: train/GundhiBug (27)_jpg.rf.7d541f0b2dd34b44c5da6a88ae8f7f3d.txt  \n",
            " extracting: train/GundhiBug (27)_jpg.rf.ecb8948204edeeb59627ff441c0e12b1.jpg  \n",
            " extracting: train/GundhiBug (27)_jpg.rf.ecb8948204edeeb59627ff441c0e12b1.txt  \n",
            " extracting: train/GundhiBug (29)_jpg.rf.185d90763ac8fb3a672809df06badd10.jpg  \n",
            " extracting: train/GundhiBug (29)_jpg.rf.185d90763ac8fb3a672809df06badd10.txt  \n",
            " extracting: train/GundhiBug (29)_jpg.rf.b0a04fe301789107b41a8be159287c29.jpg  \n",
            " extracting: train/GundhiBug (29)_jpg.rf.b0a04fe301789107b41a8be159287c29.txt  \n",
            " extracting: train/GundhiBug (29)_jpg.rf.db2cab3b620226e31b4faf599155e7df.jpg  \n",
            " extracting: train/GundhiBug (29)_jpg.rf.db2cab3b620226e31b4faf599155e7df.txt  \n",
            " extracting: train/GundhiBug (2b)_jpg.rf.1e948b7313434cc6549e50ccca7af41b.jpg  \n",
            " extracting: train/GundhiBug (2b)_jpg.rf.1e948b7313434cc6549e50ccca7af41b.txt  \n",
            " extracting: train/GundhiBug (2b)_jpg.rf.88d7fc7bd0036b2c29da146b7b3562c6.jpg  \n",
            " extracting: train/GundhiBug (2b)_jpg.rf.88d7fc7bd0036b2c29da146b7b3562c6.txt  \n",
            " extracting: train/GundhiBug (2b)_jpg.rf.b448114fdfe5f962ff0d88b612404c30.jpg  \n",
            " extracting: train/GundhiBug (2b)_jpg.rf.b448114fdfe5f962ff0d88b612404c30.txt  \n",
            " extracting: train/GundhiBug (3)_jpeg.rf.28dca3b373028fc9529b030ad5fa145d.jpg  \n",
            " extracting: train/GundhiBug (3)_jpeg.rf.28dca3b373028fc9529b030ad5fa145d.txt  \n",
            " extracting: train/GundhiBug (3)_jpeg.rf.6c922f641c28e831ad95159f674e9ab6.jpg  \n",
            " extracting: train/GundhiBug (3)_jpeg.rf.6c922f641c28e831ad95159f674e9ab6.txt  \n",
            " extracting: train/GundhiBug (3)_jpeg.rf.b7953855350b21d10481270c8867c421.jpg  \n",
            " extracting: train/GundhiBug (3)_jpeg.rf.b7953855350b21d10481270c8867c421.txt  \n",
            " extracting: train/GundhiBug (30)_jpg.rf.0c7e801a2159b4d03a6c6d085943680c.jpg  \n",
            " extracting: train/GundhiBug (30)_jpg.rf.0c7e801a2159b4d03a6c6d085943680c.txt  \n",
            " extracting: train/GundhiBug (30)_jpg.rf.b5bfce0951cdf8fcb00391194352222b.jpg  \n",
            " extracting: train/GundhiBug (30)_jpg.rf.b5bfce0951cdf8fcb00391194352222b.txt  \n",
            " extracting: train/GundhiBug (30)_jpg.rf.e97c3a426707041589b6caf0be778cf1.jpg  \n",
            " extracting: train/GundhiBug (30)_jpg.rf.e97c3a426707041589b6caf0be778cf1.txt  \n",
            " extracting: train/GundhiBug (31)_jpg.rf.207c65a3ee824d8804195b551951ef4f.jpg  \n",
            " extracting: train/GundhiBug (31)_jpg.rf.207c65a3ee824d8804195b551951ef4f.txt  \n",
            " extracting: train/GundhiBug (31)_jpg.rf.309693a93ebc335faa9e57a6fd5c427d.jpg  \n",
            " extracting: train/GundhiBug (31)_jpg.rf.309693a93ebc335faa9e57a6fd5c427d.txt  \n",
            " extracting: train/GundhiBug (31)_jpg.rf.d061531506120741d34ecacbc0698555.jpg  \n",
            " extracting: train/GundhiBug (31)_jpg.rf.d061531506120741d34ecacbc0698555.txt  \n",
            " extracting: train/GundhiBug (32)_jpg.rf.2ee6b1a6dd78554d99c9d75a88769ae2.jpg  \n",
            " extracting: train/GundhiBug (32)_jpg.rf.2ee6b1a6dd78554d99c9d75a88769ae2.txt  \n",
            " extracting: train/GundhiBug (32)_jpg.rf.7ef92096b5cf2ce339d01037d0f6c92a.jpg  \n",
            " extracting: train/GundhiBug (32)_jpg.rf.7ef92096b5cf2ce339d01037d0f6c92a.txt  \n",
            " extracting: train/GundhiBug (32)_jpg.rf.87e7303c10a756db7a6bbe59736bd6bc.jpg  \n",
            " extracting: train/GundhiBug (32)_jpg.rf.87e7303c10a756db7a6bbe59736bd6bc.txt  \n",
            " extracting: train/GundhiBug (33)_jpg.rf.87c7812513e089a9ad849a67ee70967f.jpg  \n",
            " extracting: train/GundhiBug (33)_jpg.rf.87c7812513e089a9ad849a67ee70967f.txt  \n",
            " extracting: train/GundhiBug (33)_jpg.rf.b03f20e7ef4b5647e6e8d7a8da45143a.jpg  \n",
            " extracting: train/GundhiBug (33)_jpg.rf.b03f20e7ef4b5647e6e8d7a8da45143a.txt  \n",
            " extracting: train/GundhiBug (33)_jpg.rf.dbcd5126473f3b32041b12582031c448.jpg  \n",
            " extracting: train/GundhiBug (33)_jpg.rf.dbcd5126473f3b32041b12582031c448.txt  \n",
            " extracting: train/GundhiBug (34)_jpg.rf.41a56d5ca77513a7c866ce9f55d39f47.jpg  \n",
            " extracting: train/GundhiBug (34)_jpg.rf.41a56d5ca77513a7c866ce9f55d39f47.txt  \n",
            " extracting: train/GundhiBug (34)_jpg.rf.4f41aa72b7b094527f70ff69e8b2167d.jpg  \n",
            " extracting: train/GundhiBug (34)_jpg.rf.4f41aa72b7b094527f70ff69e8b2167d.txt  \n",
            " extracting: train/GundhiBug (34)_jpg.rf.927e0c297ff76f73548ea85f05ef5b65.jpg  \n",
            " extracting: train/GundhiBug (34)_jpg.rf.927e0c297ff76f73548ea85f05ef5b65.txt  \n",
            " extracting: train/GundhiBug (35)_jpg.rf.7f9b574b2f800a23b68eed73c9035e60.jpg  \n",
            " extracting: train/GundhiBug (35)_jpg.rf.7f9b574b2f800a23b68eed73c9035e60.txt  \n",
            " extracting: train/GundhiBug (35)_jpg.rf.92ed34bcc84e75d202d2d3026db52c8e.jpg  \n",
            " extracting: train/GundhiBug (35)_jpg.rf.92ed34bcc84e75d202d2d3026db52c8e.txt  \n",
            " extracting: train/GundhiBug (35)_jpg.rf.fa93913eef6fad112488036786d8b0b1.jpg  \n",
            " extracting: train/GundhiBug (35)_jpg.rf.fa93913eef6fad112488036786d8b0b1.txt  \n",
            " extracting: train/GundhiBug (36)_jpg.rf.7e133b14caa10cfcb21a180254626ce9.jpg  \n",
            " extracting: train/GundhiBug (36)_jpg.rf.7e133b14caa10cfcb21a180254626ce9.txt  \n",
            " extracting: train/GundhiBug (36)_jpg.rf.c9060cea076ec01d94ff93af1abe89ff.jpg  \n",
            " extracting: train/GundhiBug (36)_jpg.rf.c9060cea076ec01d94ff93af1abe89ff.txt  \n",
            " extracting: train/GundhiBug (36)_jpg.rf.f544a3f12090c696c983760f8818a5b1.jpg  \n",
            " extracting: train/GundhiBug (36)_jpg.rf.f544a3f12090c696c983760f8818a5b1.txt  \n",
            " extracting: train/GundhiBug (38)_jpg.rf.579ea628f1d4a8a3797015cbeb9e26f4.jpg  \n",
            " extracting: train/GundhiBug (38)_jpg.rf.579ea628f1d4a8a3797015cbeb9e26f4.txt  \n",
            " extracting: train/GundhiBug (38)_jpg.rf.8e227e4ea08d5e5c14e06dc5e6ff0662.jpg  \n",
            " extracting: train/GundhiBug (38)_jpg.rf.8e227e4ea08d5e5c14e06dc5e6ff0662.txt  \n",
            " extracting: train/GundhiBug (38)_jpg.rf.b7fce4020c1baa78dd769487d86d0a6d.jpg  \n",
            " extracting: train/GundhiBug (38)_jpg.rf.b7fce4020c1baa78dd769487d86d0a6d.txt  \n",
            " extracting: train/GundhiBug (39)_jpg.rf.242b59ea2d22e3204bef1afc191d2d07.jpg  \n",
            " extracting: train/GundhiBug (39)_jpg.rf.242b59ea2d22e3204bef1afc191d2d07.txt  \n",
            " extracting: train/GundhiBug (39)_jpg.rf.28b3fc9a6ab843d2b1548f64eac1e037.jpg  \n",
            " extracting: train/GundhiBug (39)_jpg.rf.28b3fc9a6ab843d2b1548f64eac1e037.txt  \n",
            " extracting: train/GundhiBug (39)_jpg.rf.cea77f46b49182d5d8011f7b63044b74.jpg  \n",
            " extracting: train/GundhiBug (39)_jpg.rf.cea77f46b49182d5d8011f7b63044b74.txt  \n",
            " extracting: train/GundhiBug (3b)_jpg.rf.27230fee72d71d83a3f8111133de5549.jpg  \n",
            " extracting: train/GundhiBug (3b)_jpg.rf.27230fee72d71d83a3f8111133de5549.txt  \n",
            " extracting: train/GundhiBug (3b)_jpg.rf.a64269b33e1abbcdbe005ac710e99a32.jpg  \n",
            " extracting: train/GundhiBug (3b)_jpg.rf.a64269b33e1abbcdbe005ac710e99a32.txt  \n",
            " extracting: train/GundhiBug (3b)_jpg.rf.b4a067f55f5ed43c906f0408d35e8022.jpg  \n",
            " extracting: train/GundhiBug (3b)_jpg.rf.b4a067f55f5ed43c906f0408d35e8022.txt  \n",
            " extracting: train/GundhiBug (4)_jpeg.rf.63c1bd844ee07235b3150acc60e64e9d.jpg  \n",
            " extracting: train/GundhiBug (4)_jpeg.rf.63c1bd844ee07235b3150acc60e64e9d.txt  \n",
            " extracting: train/GundhiBug (4)_jpeg.rf.c0f5fb3189e69b13b856d76f6d663acd.jpg  \n",
            " extracting: train/GundhiBug (4)_jpeg.rf.c0f5fb3189e69b13b856d76f6d663acd.txt  \n",
            " extracting: train/GundhiBug (4)_jpeg.rf.cd70fa0d4254009b0b83355af2c97191.jpg  \n",
            " extracting: train/GundhiBug (4)_jpeg.rf.cd70fa0d4254009b0b83355af2c97191.txt  \n",
            " extracting: train/GundhiBug (4b)_jpg.rf.ab85c7ca10d7c07a7742f4de7ee6d342.jpg  \n",
            " extracting: train/GundhiBug (4b)_jpg.rf.ab85c7ca10d7c07a7742f4de7ee6d342.txt  \n",
            " extracting: train/GundhiBug (4b)_jpg.rf.b65e53c8ce67aa220555d882fa0df850.jpg  \n",
            " extracting: train/GundhiBug (4b)_jpg.rf.b65e53c8ce67aa220555d882fa0df850.txt  \n",
            " extracting: train/GundhiBug (4b)_jpg.rf.e677102132bbbd3df0d1a29adc031494.jpg  \n",
            " extracting: train/GundhiBug (4b)_jpg.rf.e677102132bbbd3df0d1a29adc031494.txt  \n",
            " extracting: train/GundhiBug (5)_jpg.rf.01e987b1475d36860b8c4f9025762baa.jpg  \n",
            " extracting: train/GundhiBug (5)_jpg.rf.01e987b1475d36860b8c4f9025762baa.txt  \n",
            " extracting: train/GundhiBug (5)_jpg.rf.812d16ab014ebb552df3c798edc1ba30.jpg  \n",
            " extracting: train/GundhiBug (5)_jpg.rf.812d16ab014ebb552df3c798edc1ba30.txt  \n",
            " extracting: train/GundhiBug (5)_jpg.rf.9f8486bdf6c54710000c50adf554e816.jpg  \n",
            " extracting: train/GundhiBug (5)_jpg.rf.9f8486bdf6c54710000c50adf554e816.txt  \n",
            " extracting: train/GundhiBug (6)_jpg.rf.0c8e535118b8962d721c46030e20d6ab.jpg  \n",
            " extracting: train/GundhiBug (6)_jpg.rf.0c8e535118b8962d721c46030e20d6ab.txt  \n",
            " extracting: train/GundhiBug (6)_jpg.rf.85d185f0a96f8d30c2e7d5660fa9bcc6.jpg  \n",
            " extracting: train/GundhiBug (6)_jpg.rf.85d185f0a96f8d30c2e7d5660fa9bcc6.txt  \n",
            " extracting: train/GundhiBug (6)_jpg.rf.b76cfb7d39c0e0d99585c284f7a1561e.jpg  \n",
            " extracting: train/GundhiBug (6)_jpg.rf.b76cfb7d39c0e0d99585c284f7a1561e.txt  \n",
            " extracting: train/GundhiBug (7)_jpg.rf.2d0c78ce9e763c4e5e117a0237ea5831.jpg  \n",
            " extracting: train/GundhiBug (7)_jpg.rf.2d0c78ce9e763c4e5e117a0237ea5831.txt  \n",
            " extracting: train/GundhiBug (7)_jpg.rf.4121bad4b5ac9c8abd5739a4cf45b387.jpg  \n",
            " extracting: train/GundhiBug (7)_jpg.rf.4121bad4b5ac9c8abd5739a4cf45b387.txt  \n",
            " extracting: train/GundhiBug (7)_jpg.rf.cd950a32ac36c88dd016a32ee7ab40f6.jpg  \n",
            " extracting: train/GundhiBug (7)_jpg.rf.cd950a32ac36c88dd016a32ee7ab40f6.txt  \n",
            " extracting: train/GundhiBug (8)_jpg.rf.69b3e29174a3a2903021b44b2fac44fb.jpg  \n",
            " extracting: train/GundhiBug (8)_jpg.rf.69b3e29174a3a2903021b44b2fac44fb.txt  \n",
            " extracting: train/GundhiBug (8)_jpg.rf.a6df6cb4665d4a660a1924de0d9fba3e.jpg  \n",
            " extracting: train/GundhiBug (8)_jpg.rf.a6df6cb4665d4a660a1924de0d9fba3e.txt  \n",
            " extracting: train/GundhiBug (8)_jpg.rf.cf3ce91d9d4416176bbff8e255431ebf.jpg  \n",
            " extracting: train/GundhiBug (8)_jpg.rf.cf3ce91d9d4416176bbff8e255431ebf.txt  \n",
            " extracting: train/GundhiBug (9)_jpg.rf.34ef7633911858ef561af1c653bd8190.jpg  \n",
            " extracting: train/GundhiBug (9)_jpg.rf.34ef7633911858ef561af1c653bd8190.txt  \n",
            " extracting: train/GundhiBug (9)_jpg.rf.5dcc1b223a819a53f3e274b2b77d5c2d.jpg  \n",
            " extracting: train/GundhiBug (9)_jpg.rf.5dcc1b223a819a53f3e274b2b77d5c2d.txt  \n",
            " extracting: train/GundhiBug (9)_jpg.rf.d957fe9acd620f0815388be3f0167810.jpg  \n",
            " extracting: train/GundhiBug (9)_jpg.rf.d957fe9acd620f0815388be3f0167810.txt  \n",
            " extracting: train/GundhiBug(40)_png.rf.03676103254b14ef1158dacf1571183c.jpg  \n",
            " extracting: train/GundhiBug(40)_png.rf.03676103254b14ef1158dacf1571183c.txt  \n",
            " extracting: train/GundhiBug(40)_png.rf.11cb98a00d827fb077209609a86eb112.jpg  \n",
            " extracting: train/GundhiBug(40)_png.rf.11cb98a00d827fb077209609a86eb112.txt  \n",
            " extracting: train/GundhiBug(40)_png.rf.f12cc65e60616cab5099e784169e61a9.jpg  \n",
            " extracting: train/GundhiBug(40)_png.rf.f12cc65e60616cab5099e784169e61a9.txt  \n",
            " extracting: train/Moth 1_jpg.rf.22b614bc6769e7a2a9fb9f6baba93cde.jpg  \n",
            " extracting: train/Moth 1_jpg.rf.22b614bc6769e7a2a9fb9f6baba93cde.txt  \n",
            " extracting: train/Moth 1_jpg.rf.4f11118cc715e5011fc64af61e1f4368.jpg  \n",
            " extracting: train/Moth 1_jpg.rf.4f11118cc715e5011fc64af61e1f4368.txt  \n",
            " extracting: train/Moth 1_jpg.rf.b77700fd038dd04326374f3baa3d5d27.jpg  \n",
            " extracting: train/Moth 1_jpg.rf.b77700fd038dd04326374f3baa3d5d27.txt  \n",
            " extracting: train/OIP (1)_jpg.rf.5b9de98b6b5599b59c49561edf9cf577.jpg  \n",
            " extracting: train/OIP (1)_jpg.rf.5b9de98b6b5599b59c49561edf9cf577.txt  \n",
            " extracting: train/OIP (1)_jpg.rf.a72b76c2780112ec11ee0790d718a065.jpg  \n",
            " extracting: train/OIP (1)_jpg.rf.a72b76c2780112ec11ee0790d718a065.txt  \n",
            " extracting: train/OIP (1)_jpg.rf.d39422879744df1450abe3fe3641851c.jpg  \n",
            " extracting: train/OIP (1)_jpg.rf.d39422879744df1450abe3fe3641851c.txt  \n",
            " extracting: train/OIP (23)_jpg.rf.52f579c653315114990569f2a4d1330f.jpg  \n",
            " extracting: train/OIP (23)_jpg.rf.52f579c653315114990569f2a4d1330f.txt  \n",
            " extracting: train/OIP (23)_jpg.rf.620cf822654b98c5f551da1a40a4afc4.jpg  \n",
            " extracting: train/OIP (23)_jpg.rf.620cf822654b98c5f551da1a40a4afc4.txt  \n",
            " extracting: train/OIP (23)_jpg.rf.eeb04a43d8df6bb003f81b87b051c401.jpg  \n",
            " extracting: train/OIP (23)_jpg.rf.eeb04a43d8df6bb003f81b87b051c401.txt  \n",
            " extracting: train/OIP (27)_jpg.rf.1a0e1865fa50a5377868ed273db629d1.jpg  \n",
            " extracting: train/OIP (27)_jpg.rf.1a0e1865fa50a5377868ed273db629d1.txt  \n",
            " extracting: train/OIP (27)_jpg.rf.5ac3afac7d22117a03307e63fd047376.jpg  \n",
            " extracting: train/OIP (27)_jpg.rf.5ac3afac7d22117a03307e63fd047376.txt  \n",
            " extracting: train/OIP (27)_jpg.rf.af33abb3fbaa72f598d40d8aa2f61d7e.jpg  \n",
            " extracting: train/OIP (27)_jpg.rf.af33abb3fbaa72f598d40d8aa2f61d7e.txt  \n",
            " extracting: train/OIP (3)_jpg.rf.1174054c291bd2189a370d669c967347.jpg  \n",
            " extracting: train/OIP (3)_jpg.rf.1174054c291bd2189a370d669c967347.txt  \n",
            " extracting: train/OIP (3)_jpg.rf.6b03fc764ea5504e22faeffb87ff7585.jpg  \n",
            " extracting: train/OIP (3)_jpg.rf.6b03fc764ea5504e22faeffb87ff7585.txt  \n",
            " extracting: train/OIP (3)_jpg.rf.aae85a0b716f69df3092054c5fd5ddfe.jpg  \n",
            " extracting: train/OIP (3)_jpg.rf.aae85a0b716f69df3092054c5fd5ddfe.txt  \n",
            " extracting: train/OIP (30)_jpg.rf.3110828cf92700cda43b846327e84c20.jpg  \n",
            " extracting: train/OIP (30)_jpg.rf.3110828cf92700cda43b846327e84c20.txt  \n",
            " extracting: train/OIP (30)_jpg.rf.82c0ff33ca501bd18e6fc59a778337f6.jpg  \n",
            " extracting: train/OIP (30)_jpg.rf.82c0ff33ca501bd18e6fc59a778337f6.txt  \n",
            " extracting: train/OIP (30)_jpg.rf.f2febd049c1533d409f171420c27e860.jpg  \n",
            " extracting: train/OIP (30)_jpg.rf.f2febd049c1533d409f171420c27e860.txt  \n",
            " extracting: train/OIP (32)_jpg.rf.2871e05557e6c2dddf6ae5ca17f81926.jpg  \n",
            " extracting: train/OIP (32)_jpg.rf.2871e05557e6c2dddf6ae5ca17f81926.txt  \n",
            " extracting: train/OIP (32)_jpg.rf.95d05e126c9905a6269dce9ae7df2041.jpg  \n",
            " extracting: train/OIP (32)_jpg.rf.95d05e126c9905a6269dce9ae7df2041.txt  \n",
            " extracting: train/OIP (32)_jpg.rf.e8b7d652dd830cdd01e5bac52745e367.jpg  \n",
            " extracting: train/OIP (32)_jpg.rf.e8b7d652dd830cdd01e5bac52745e367.txt  \n",
            " extracting: train/OIP (33)_jpg.rf.58a3694ee4500ca9a1a2b729cecb7a48.jpg  \n",
            " extracting: train/OIP (33)_jpg.rf.58a3694ee4500ca9a1a2b729cecb7a48.txt  \n",
            " extracting: train/OIP (33)_jpg.rf.d96a97009fa5e617be610628d31a4dbf.jpg  \n",
            " extracting: train/OIP (33)_jpg.rf.d96a97009fa5e617be610628d31a4dbf.txt  \n",
            " extracting: train/OIP (33)_jpg.rf.ead5163a1e6e7e3886f501cf20e3b130.jpg  \n",
            " extracting: train/OIP (33)_jpg.rf.ead5163a1e6e7e3886f501cf20e3b130.txt  \n",
            " extracting: train/OIP (34)_jpg.rf.42ef891fb02cda1a648212dbe8792634.jpg  \n",
            " extracting: train/OIP (34)_jpg.rf.42ef891fb02cda1a648212dbe8792634.txt  \n",
            " extracting: train/OIP (34)_jpg.rf.72f1e7644f7f7fba01b5a4c35ccdd0c2.jpg  \n",
            " extracting: train/OIP (34)_jpg.rf.72f1e7644f7f7fba01b5a4c35ccdd0c2.txt  \n",
            " extracting: train/OIP (34)_jpg.rf.dbe176226e716cc9d9ebd7bcd235e444.jpg  \n",
            " extracting: train/OIP (34)_jpg.rf.dbe176226e716cc9d9ebd7bcd235e444.txt  \n",
            " extracting: train/OIP (36)_jpg.rf.3674f493d8f3016c1828c74e972d1b54.jpg  \n",
            " extracting: train/OIP (36)_jpg.rf.3674f493d8f3016c1828c74e972d1b54.txt  \n",
            " extracting: train/OIP (36)_jpg.rf.438e59485bbe825bdfbd18abd6d474bd.jpg  \n",
            " extracting: train/OIP (36)_jpg.rf.438e59485bbe825bdfbd18abd6d474bd.txt  \n",
            " extracting: train/OIP (36)_jpg.rf.62b3499f061852043c91abba007b0738.jpg  \n",
            " extracting: train/OIP (36)_jpg.rf.62b3499f061852043c91abba007b0738.txt  \n",
            " extracting: train/OIP (44)_jpg.rf.63612c82a4b66d8f462b5ce332f7a831.jpg  \n",
            " extracting: train/OIP (44)_jpg.rf.63612c82a4b66d8f462b5ce332f7a831.txt  \n",
            " extracting: train/OIP (44)_jpg.rf.702d5a14379e9002461fd1ce81cebd4b.jpg  \n",
            " extracting: train/OIP (44)_jpg.rf.702d5a14379e9002461fd1ce81cebd4b.txt  \n",
            " extracting: train/OIP (44)_jpg.rf.8141deb4c6aff1ee257e807c514c0218.jpg  \n",
            " extracting: train/OIP (44)_jpg.rf.8141deb4c6aff1ee257e807c514c0218.txt  \n",
            " extracting: train/OIP (46)_jpg.rf.325df89489d3a86c7bf6b7e4b207fe79.jpg  \n",
            " extracting: train/OIP (46)_jpg.rf.325df89489d3a86c7bf6b7e4b207fe79.txt  \n",
            " extracting: train/OIP (46)_jpg.rf.33b606d27cff0403f6b2c977192bd797.jpg  \n",
            " extracting: train/OIP (46)_jpg.rf.33b606d27cff0403f6b2c977192bd797.txt  \n",
            " extracting: train/OIP (46)_jpg.rf.784f837a4152892dcef6ad5d04098d0a.jpg  \n",
            " extracting: train/OIP (46)_jpg.rf.784f837a4152892dcef6ad5d04098d0a.txt  \n",
            " extracting: train/OIP (48)_jpg.rf.4664310a2e82812bfe739060cde9fecb.jpg  \n",
            " extracting: train/OIP (48)_jpg.rf.4664310a2e82812bfe739060cde9fecb.txt  \n",
            " extracting: train/OIP (48)_jpg.rf.6b2209e4d49df65c0fda908d385958a6.jpg  \n",
            " extracting: train/OIP (48)_jpg.rf.6b2209e4d49df65c0fda908d385958a6.txt  \n",
            " extracting: train/OIP (48)_jpg.rf.aac506ea7dfa48d153b8d00d4c1699f8.jpg  \n",
            " extracting: train/OIP (48)_jpg.rf.aac506ea7dfa48d153b8d00d4c1699f8.txt  \n",
            " extracting: train/OIP (49)_jpg.rf.513ef571a33fb27d0cd6d9d9c31a3dc9.jpg  \n",
            " extracting: train/OIP (49)_jpg.rf.513ef571a33fb27d0cd6d9d9c31a3dc9.txt  \n",
            " extracting: train/OIP (49)_jpg.rf.690dec1d00c4081f4d8ebe08ee348502.jpg  \n",
            " extracting: train/OIP (49)_jpg.rf.690dec1d00c4081f4d8ebe08ee348502.txt  \n",
            " extracting: train/OIP (49)_jpg.rf.b57cdb94b1bbc297359dc780658acfe7.jpg  \n",
            " extracting: train/OIP (49)_jpg.rf.b57cdb94b1bbc297359dc780658acfe7.txt  \n",
            " extracting: train/OIP (53)_jpg.rf.800fdcb81b18c2c6fa9d51d75f67e90d.jpg  \n",
            " extracting: train/OIP (53)_jpg.rf.800fdcb81b18c2c6fa9d51d75f67e90d.txt  \n",
            " extracting: train/OIP (53)_jpg.rf.89fa802c3d8a4205813edc0c6cbdff8a.jpg  \n",
            " extracting: train/OIP (53)_jpg.rf.89fa802c3d8a4205813edc0c6cbdff8a.txt  \n",
            " extracting: train/OIP (53)_jpg.rf.d4f7c0b93c6c3af4b41e13125f5d9aa1.jpg  \n",
            " extracting: train/OIP (53)_jpg.rf.d4f7c0b93c6c3af4b41e13125f5d9aa1.txt  \n",
            " extracting: train/OIP (56)_jpg.rf.33d74c9237893cc901b7d71c55c0b91b.jpg  \n",
            " extracting: train/OIP (56)_jpg.rf.33d74c9237893cc901b7d71c55c0b91b.txt  \n",
            " extracting: train/OIP (56)_jpg.rf.38c2a0e3b20035afc1aa40c248a72e53.jpg  \n",
            " extracting: train/OIP (56)_jpg.rf.38c2a0e3b20035afc1aa40c248a72e53.txt  \n",
            " extracting: train/OIP (56)_jpg.rf.eac90f33286db62e8c70e16952e479c9.jpg  \n",
            " extracting: train/OIP (56)_jpg.rf.eac90f33286db62e8c70e16952e479c9.txt  \n",
            " extracting: train/OIP (59)_jpg.rf.012e2f7da6c53bd472a2f5ff2ee91a3c.jpg  \n",
            " extracting: train/OIP (59)_jpg.rf.012e2f7da6c53bd472a2f5ff2ee91a3c.txt  \n",
            " extracting: train/OIP (59)_jpg.rf.34a39726c998671c2d9bc248e2914d51.jpg  \n",
            " extracting: train/OIP (59)_jpg.rf.34a39726c998671c2d9bc248e2914d51.txt  \n",
            " extracting: train/OIP (59)_jpg.rf.f6bd01428c9044d5686d67456b924505.jpg  \n",
            " extracting: train/OIP (59)_jpg.rf.f6bd01428c9044d5686d67456b924505.txt  \n",
            " extracting: train/OIP (8)_jpg.rf.02866c9086785387a5af89e2fe6de889.jpg  \n",
            " extracting: train/OIP (8)_jpg.rf.02866c9086785387a5af89e2fe6de889.txt  \n",
            " extracting: train/OIP (8)_jpg.rf.0b73db5ce069d1a902ff83afbdb64b20.jpg  \n",
            " extracting: train/OIP (8)_jpg.rf.0b73db5ce069d1a902ff83afbdb64b20.txt  \n",
            " extracting: train/OIP (8)_jpg.rf.ea2f964ebf3c6eca80a2e2e529f89f14.jpg  \n",
            " extracting: train/OIP (8)_jpg.rf.ea2f964ebf3c6eca80a2e2e529f89f14.txt  \n",
            " extracting: train/Tuneer1_jpg.rf.21381d69146f42793d22e6cab8eb9831.jpg  \n",
            " extracting: train/Tuneer1_jpg.rf.21381d69146f42793d22e6cab8eb9831.txt  \n",
            " extracting: train/Tuneer1_jpg.rf.63b999f486e90251723e775afb4f6977.jpg  \n",
            " extracting: train/Tuneer1_jpg.rf.63b999f486e90251723e775afb4f6977.txt  \n",
            " extracting: train/Tuneer1_jpg.rf.848dd44a0d9b4485d6495af5e41801af.jpg  \n",
            " extracting: train/Tuneer1_jpg.rf.848dd44a0d9b4485d6495af5e41801af.txt  \n",
            " extracting: train/Z (2)_jpg.rf.7b1c63d4b8a22c239619a56d370cda4a.jpg  \n",
            " extracting: train/Z (2)_jpg.rf.7b1c63d4b8a22c239619a56d370cda4a.txt  \n",
            " extracting: train/Z (2)_jpg.rf.875d000826974f4fa9ac4af4817df6ba.jpg  \n",
            " extracting: train/Z (2)_jpg.rf.875d000826974f4fa9ac4af4817df6ba.txt  \n",
            " extracting: train/Z (2)_jpg.rf.f624ba305bbf081a079d13b0bc5f3935.jpg  \n",
            " extracting: train/Z (2)_jpg.rf.f624ba305bbf081a079d13b0bc5f3935.txt  \n",
            " extracting: train/Z_jpg.rf.1dcca0ced861a952e3fc0fb93d7184ca.jpg  \n",
            " extracting: train/Z_jpg.rf.1dcca0ced861a952e3fc0fb93d7184ca.txt  \n",
            " extracting: train/Z_jpg.rf.2add302d3b3e4c4904be86075ae3fbd4.jpg  \n",
            " extracting: train/Z_jpg.rf.2add302d3b3e4c4904be86075ae3fbd4.txt  \n",
            " extracting: train/Z_jpg.rf.8d5d5fc215ab21cea2824372a179199a.jpg  \n",
            " extracting: train/Z_jpg.rf.8d5d5fc215ab21cea2824372a179199a.txt  \n",
            " extracting: train/_darknet.labels   \n",
            " extracting: train/aastem_bor_moth-jpg_jpeg.rf.2564daf1ee936c2136ff57ce5c875444.jpg  \n",
            " extracting: train/aastem_bor_moth-jpg_jpeg.rf.2564daf1ee936c2136ff57ce5c875444.txt  \n",
            " extracting: train/aastem_bor_moth-jpg_jpeg.rf.2b8796d3acbe656724a9f18ae967b8cc.jpg  \n",
            " extracting: train/aastem_bor_moth-jpg_jpeg.rf.2b8796d3acbe656724a9f18ae967b8cc.txt  \n",
            " extracting: train/aastem_bor_moth-jpg_jpeg.rf.9784c81a9b4a23b6015ef9f028926aad.jpg  \n",
            " extracting: train/aastem_bor_moth-jpg_jpeg.rf.9784c81a9b4a23b6015ef9f028926aad.txt  \n",
            " extracting: train/astem_bor_lar-jpg_jpeg.rf.a02cbb7e3b4a133cca1e6c1b23268e2f.jpg  \n",
            " extracting: train/astem_bor_lar-jpg_jpeg.rf.a02cbb7e3b4a133cca1e6c1b23268e2f.txt  \n",
            " extracting: train/astem_bor_lar-jpg_jpeg.rf.b9bb995bca7246c8ea763c95de5d03b5.jpg  \n",
            " extracting: train/astem_bor_lar-jpg_jpeg.rf.b9bb995bca7246c8ea763c95de5d03b5.txt  \n",
            " extracting: train/astem_bor_lar-jpg_jpeg.rf.d0e82089031702e1ef1fead5b3ccec86.jpg  \n",
            " extracting: train/astem_bor_lar-jpg_jpeg.rf.d0e82089031702e1ef1fead5b3ccec86.txt  \n",
            " extracting: train/bhispaa_insect-jpg_jpeg.rf.40c74008047fc6dff6c9eac6092c953c.jpg  \n",
            " extracting: train/bhispaa_insect-jpg_jpeg.rf.40c74008047fc6dff6c9eac6092c953c.txt  \n",
            " extracting: train/bhispaa_insect-jpg_jpeg.rf.b7ac3df051f8f06db0bc71bce494d8ed.jpg  \n",
            " extracting: train/bhispaa_insect-jpg_jpeg.rf.b7ac3df051f8f06db0bc71bce494d8ed.txt  \n",
            " extracting: train/bhispaa_insect-jpg_jpeg.rf.c1a295d6a8ba8eb36d9b60e69644c065.jpg  \n",
            " extracting: train/bhispaa_insect-jpg_jpeg.rf.c1a295d6a8ba8eb36d9b60e69644c065.txt  \n",
            " extracting: train/cdscsdcedc_jpg.rf.d177099f772d5a9242e7aebe77afa7e4.jpg  \n",
            " extracting: train/cdscsdcedc_jpg.rf.d177099f772d5a9242e7aebe77afa7e4.txt  \n",
            " extracting: train/cdscsdcedc_jpg.rf.d91696ac3c03ff724035e0de077fbcb0.jpg  \n",
            " extracting: train/cdscsdcedc_jpg.rf.d91696ac3c03ff724035e0de077fbcb0.txt  \n",
            " extracting: train/cdscsdcedc_jpg.rf.f1e3403bd81968484592cf944b280ac8.jpg  \n",
            " extracting: train/cdscsdcedc_jpg.rf.f1e3403bd81968484592cf944b280ac8.txt  \n",
            " extracting: train/dscsdcsdcsdcsdcsdc_jpg.rf.0496d2ee6022e3e425d637c1ab4ea4ea.jpg  \n",
            " extracting: train/dscsdcsdcsdcsdcsdc_jpg.rf.0496d2ee6022e3e425d637c1ab4ea4ea.txt  \n",
            " extracting: train/dscsdcsdcsdcsdcsdc_jpg.rf.087ce61ea24266a103dfd5f0bb6e6995.jpg  \n",
            " extracting: train/dscsdcsdcsdcsdcsdc_jpg.rf.087ce61ea24266a103dfd5f0bb6e6995.txt  \n",
            " extracting: train/dscsdcsdcsdcsdcsdc_jpg.rf.3303eac6cfbb35d794cf4283cd267cd7.jpg  \n",
            " extracting: train/dscsdcsdcsdcsdcsdc_jpg.rf.3303eac6cfbb35d794cf4283cd267cd7.txt  \n",
            " extracting: train/dwqdqwdqdasas_jpg.rf.23884a70e7b424c6ce52b4bf544b7568.jpg  \n",
            " extracting: train/dwqdqwdqdasas_jpg.rf.23884a70e7b424c6ce52b4bf544b7568.txt  \n",
            " extracting: train/dwqdqwdqdasas_jpg.rf.5b5b9339abca86777647614ee034c36d.jpg  \n",
            " extracting: train/dwqdqwdqdasas_jpg.rf.5b5b9339abca86777647614ee034c36d.txt  \n",
            " extracting: train/dwqdqwdqdasas_jpg.rf.b595f6e84d426146613913b0ca06617a.jpg  \n",
            " extracting: train/dwqdqwdqdasas_jpg.rf.b595f6e84d426146613913b0ca06617a.txt  \n",
            " extracting: train/hgrthrhrth_jpg.rf.1af626cee363db1f6b99f9f77a2d5b49.jpg  \n",
            " extracting: train/hgrthrhrth_jpg.rf.1af626cee363db1f6b99f9f77a2d5b49.txt  \n",
            " extracting: train/hgrthrhrth_jpg.rf.7fc562d06137c7755cd94e8590a30a86.jpg  \n",
            " extracting: train/hgrthrhrth_jpg.rf.7fc562d06137c7755cd94e8590a30a86.txt  \n",
            " extracting: train/hgrthrhrth_jpg.rf.b466bffd2662947d2f2b6b2a7d750e23.jpg  \n",
            " extracting: train/hgrthrhrth_jpg.rf.b466bffd2662947d2f2b6b2a7d750e23.txt  \n",
            " extracting: train/images (1)_jpg.rf.38717a5f585463b1f2765820ecbe7aec.jpg  \n",
            " extracting: train/images (1)_jpg.rf.38717a5f585463b1f2765820ecbe7aec.txt  \n",
            " extracting: train/images (1)_jpg.rf.5469065b148e7874733c939972f944c9.jpg  \n",
            " extracting: train/images (1)_jpg.rf.5469065b148e7874733c939972f944c9.txt  \n",
            " extracting: train/images (1)_jpg.rf.e3d50313ce5bb566ac021a8148ef7a88.jpg  \n",
            " extracting: train/images (1)_jpg.rf.e3d50313ce5bb566ac021a8148ef7a88.txt  \n",
            " extracting: train/images (11)_jpg.rf.509ca76b78fc10b5d86006113c6f29df.jpg  \n",
            " extracting: train/images (11)_jpg.rf.509ca76b78fc10b5d86006113c6f29df.txt  \n",
            " extracting: train/images (11)_jpg.rf.68734aee9ebcd35eb7960b2fb41a8d0c.jpg  \n",
            " extracting: train/images (11)_jpg.rf.68734aee9ebcd35eb7960b2fb41a8d0c.txt  \n",
            " extracting: train/images (11)_jpg.rf.f1c15c6816fc603eda901efd250ca0b1.jpg  \n",
            " extracting: train/images (11)_jpg.rf.f1c15c6816fc603eda901efd250ca0b1.txt  \n",
            " extracting: train/images (14)_jpg.rf.02818b93dc185691058db55dae495603.jpg  \n",
            " extracting: train/images (14)_jpg.rf.02818b93dc185691058db55dae495603.txt  \n",
            " extracting: train/images (14)_jpg.rf.3c6f25053bd67fe549edd0a39de8d05f.jpg  \n",
            " extracting: train/images (14)_jpg.rf.3c6f25053bd67fe549edd0a39de8d05f.txt  \n",
            " extracting: train/images (14)_jpg.rf.a189f8138f8aa53e1f12d679238499e0.jpg  \n",
            " extracting: train/images (14)_jpg.rf.a189f8138f8aa53e1f12d679238499e0.txt  \n",
            " extracting: train/images (15)_jpg.rf.68eaf13683859aab470425710f650279.jpg  \n",
            " extracting: train/images (15)_jpg.rf.68eaf13683859aab470425710f650279.txt  \n",
            " extracting: train/images (15)_jpg.rf.beaec5af37785872fadac8959e344de6.jpg  \n",
            " extracting: train/images (15)_jpg.rf.beaec5af37785872fadac8959e344de6.txt  \n",
            " extracting: train/images (15)_jpg.rf.f91fd6a81f82b7311c8a074997e9cccd.jpg  \n",
            " extracting: train/images (15)_jpg.rf.f91fd6a81f82b7311c8a074997e9cccd.txt  \n",
            " extracting: train/images (16)_jpg.rf.07eec2f44e2172715ce11510b4dba7d0.jpg  \n",
            " extracting: train/images (16)_jpg.rf.07eec2f44e2172715ce11510b4dba7d0.txt  \n",
            " extracting: train/images (16)_jpg.rf.847124af7013611bc02240ba52cc81c1.jpg  \n",
            " extracting: train/images (16)_jpg.rf.847124af7013611bc02240ba52cc81c1.txt  \n",
            " extracting: train/images (16)_jpg.rf.cd99a62f80fab209e44b242d36168207.jpg  \n",
            " extracting: train/images (16)_jpg.rf.cd99a62f80fab209e44b242d36168207.txt  \n",
            " extracting: train/images (17)_jpg.rf.a67012abdad753f0349c7eb300584d0a.jpg  \n",
            " extracting: train/images (17)_jpg.rf.a67012abdad753f0349c7eb300584d0a.txt  \n",
            " extracting: train/images (17)_jpg.rf.bae332d782110572bb396fbd70cea84c.jpg  \n",
            " extracting: train/images (17)_jpg.rf.bae332d782110572bb396fbd70cea84c.txt  \n",
            " extracting: train/images (17)_jpg.rf.de96c97d3be1f05aad3f5eeba27c944b.jpg  \n",
            " extracting: train/images (17)_jpg.rf.de96c97d3be1f05aad3f5eeba27c944b.txt  \n",
            " extracting: train/images (18)_jpg.rf.5081378276fb0d2b4c64352776cfbbd2.jpg  \n",
            " extracting: train/images (18)_jpg.rf.5081378276fb0d2b4c64352776cfbbd2.txt  \n",
            " extracting: train/images (18)_jpg.rf.7e3512437ddb9e13691d1be1edbc734c.jpg  \n",
            " extracting: train/images (18)_jpg.rf.7e3512437ddb9e13691d1be1edbc734c.txt  \n",
            " extracting: train/images (18)_jpg.rf.c5f31f66e7f020cda957f9f3f5da52a8.jpg  \n",
            " extracting: train/images (18)_jpg.rf.c5f31f66e7f020cda957f9f3f5da52a8.txt  \n",
            " extracting: train/images (19)_jpg.rf.4c55810b36cb14cfde6aba190187efd3.jpg  \n",
            " extracting: train/images (19)_jpg.rf.4c55810b36cb14cfde6aba190187efd3.txt  \n",
            " extracting: train/images (19)_jpg.rf.951c521569df2a06783c2ca360958020.jpg  \n",
            " extracting: train/images (19)_jpg.rf.951c521569df2a06783c2ca360958020.txt  \n",
            " extracting: train/images (19)_jpg.rf.e569ca2defeb8bef1c58d1efeba0180d.jpg  \n",
            " extracting: train/images (19)_jpg.rf.e569ca2defeb8bef1c58d1efeba0180d.txt  \n",
            " extracting: train/images (21)_jpg.rf.2aaea2e76db9349ba09914307d3a1c99.jpg  \n",
            " extracting: train/images (21)_jpg.rf.2aaea2e76db9349ba09914307d3a1c99.txt  \n",
            " extracting: train/images (21)_jpg.rf.65ff22cb878b81fd3ea0523697d033ac.jpg  \n",
            " extracting: train/images (21)_jpg.rf.65ff22cb878b81fd3ea0523697d033ac.txt  \n",
            " extracting: train/images (21)_jpg.rf.e244943d4691093ae3aa4cd8f6a8affa.jpg  \n",
            " extracting: train/images (21)_jpg.rf.e244943d4691093ae3aa4cd8f6a8affa.txt  \n",
            " extracting: train/images (26)_jpg.rf.2848c0c92b66008958054be7452fff45.jpg  \n",
            " extracting: train/images (26)_jpg.rf.2848c0c92b66008958054be7452fff45.txt  \n",
            " extracting: train/images (26)_jpg.rf.7b71c85bcda6fcac5c31b0b114ed68b8.jpg  \n",
            " extracting: train/images (26)_jpg.rf.7b71c85bcda6fcac5c31b0b114ed68b8.txt  \n",
            " extracting: train/images (26)_jpg.rf.91523986406294f0bb3c6b3c76ce6e4d.jpg  \n",
            " extracting: train/images (26)_jpg.rf.91523986406294f0bb3c6b3c76ce6e4d.txt  \n",
            " extracting: train/images (28)_jpg.rf.5aca493b2422333dde06d09ec4a96465.jpg  \n",
            " extracting: train/images (28)_jpg.rf.5aca493b2422333dde06d09ec4a96465.txt  \n",
            " extracting: train/images (28)_jpg.rf.9990903ac40276e4c158db987d85700a.jpg  \n",
            " extracting: train/images (28)_jpg.rf.9990903ac40276e4c158db987d85700a.txt  \n",
            " extracting: train/images (28)_jpg.rf.df8d9926091c4dc9c62515f469202212.jpg  \n",
            " extracting: train/images (28)_jpg.rf.df8d9926091c4dc9c62515f469202212.txt  \n",
            " extracting: train/images (32)_jpg.rf.b2bf3ced8276c0d35e246294cf4e487f.jpg  \n",
            " extracting: train/images (32)_jpg.rf.b2bf3ced8276c0d35e246294cf4e487f.txt  \n",
            " extracting: train/images (32)_jpg.rf.d41b97e1cfb48c983976a0299b86ee5c.jpg  \n",
            " extracting: train/images (32)_jpg.rf.d41b97e1cfb48c983976a0299b86ee5c.txt  \n",
            " extracting: train/images (32)_jpg.rf.f4686c64ab3c807c19637adc6865efa6.jpg  \n",
            " extracting: train/images (32)_jpg.rf.f4686c64ab3c807c19637adc6865efa6.txt  \n",
            " extracting: train/images (33)_jpg.rf.72275b12a6b2c1a6462f9439feb4d8d3.jpg  \n",
            " extracting: train/images (33)_jpg.rf.72275b12a6b2c1a6462f9439feb4d8d3.txt  \n",
            " extracting: train/images (33)_jpg.rf.a0d78ab64f9d33b315b79c12bff13e8b.jpg  \n",
            " extracting: train/images (33)_jpg.rf.a0d78ab64f9d33b315b79c12bff13e8b.txt  \n",
            " extracting: train/images (33)_jpg.rf.b79d37e2ce8ec6931d8ab359f8d22b77.jpg  \n",
            " extracting: train/images (33)_jpg.rf.b79d37e2ce8ec6931d8ab359f8d22b77.txt  \n",
            " extracting: train/images (36)_jpg.rf.799602ce463ed16466d6ed803972fe3b.jpg  \n",
            " extracting: train/images (36)_jpg.rf.799602ce463ed16466d6ed803972fe3b.txt  \n",
            " extracting: train/images (36)_jpg.rf.90ab6f86d1617c79d1aaf0c3511001e9.jpg  \n",
            " extracting: train/images (36)_jpg.rf.90ab6f86d1617c79d1aaf0c3511001e9.txt  \n",
            " extracting: train/images (36)_jpg.rf.c4449f75e7029fe9009bbe8d1c46cbea.jpg  \n",
            " extracting: train/images (36)_jpg.rf.c4449f75e7029fe9009bbe8d1c46cbea.txt  \n",
            " extracting: train/images (38)_jpg.rf.10fd156ea78c7f40564f9da65cfed63c.jpg  \n",
            " extracting: train/images (38)_jpg.rf.10fd156ea78c7f40564f9da65cfed63c.txt  \n",
            " extracting: train/images (38)_jpg.rf.36d431d1f8624db86ad62e0f422f34d0.jpg  \n",
            " extracting: train/images (38)_jpg.rf.36d431d1f8624db86ad62e0f422f34d0.txt  \n",
            " extracting: train/images (38)_jpg.rf.a9751a93386bc9048e91324b1e3e7b13.jpg  \n",
            " extracting: train/images (38)_jpg.rf.a9751a93386bc9048e91324b1e3e7b13.txt  \n",
            " extracting: train/images (4)_jpg.rf.1debcb91406b7a55b515530ce3dda96f.jpg  \n",
            " extracting: train/images (4)_jpg.rf.1debcb91406b7a55b515530ce3dda96f.txt  \n",
            " extracting: train/images (4)_jpg.rf.66729b786bdc180f3d64c595259c7fd1.jpg  \n",
            " extracting: train/images (4)_jpg.rf.66729b786bdc180f3d64c595259c7fd1.txt  \n",
            " extracting: train/images (4)_jpg.rf.7ddb1046331617883ba7cb2b60388e3a.jpg  \n",
            " extracting: train/images (4)_jpg.rf.7ddb1046331617883ba7cb2b60388e3a.txt  \n",
            " extracting: train/images (40)_jpg.rf.3a480cdce5eba47e0a07f101d943c84e.jpg  \n",
            " extracting: train/images (40)_jpg.rf.3a480cdce5eba47e0a07f101d943c84e.txt  \n",
            " extracting: train/images (40)_jpg.rf.5f1469ca91480c6de82aed52ad957a6d.jpg  \n",
            " extracting: train/images (40)_jpg.rf.5f1469ca91480c6de82aed52ad957a6d.txt  \n",
            " extracting: train/images (40)_jpg.rf.e6e966aa2b58985d8b5c5dca2e3cb3e2.jpg  \n",
            " extracting: train/images (40)_jpg.rf.e6e966aa2b58985d8b5c5dca2e3cb3e2.txt  \n",
            " extracting: train/images (42)_jpg.rf.a8a5f17e0aa29b51a90f030a3660ae0d.jpg  \n",
            " extracting: train/images (42)_jpg.rf.a8a5f17e0aa29b51a90f030a3660ae0d.txt  \n",
            " extracting: train/images (42)_jpg.rf.bcfbedf7684c6934b52731ce63dc1aff.jpg  \n",
            " extracting: train/images (42)_jpg.rf.bcfbedf7684c6934b52731ce63dc1aff.txt  \n",
            " extracting: train/images (42)_jpg.rf.d17d5fc857778eeda66a51f42dae21cb.jpg  \n",
            " extracting: train/images (42)_jpg.rf.d17d5fc857778eeda66a51f42dae21cb.txt  \n",
            " extracting: train/images (49)_jpg.rf.1d7135951b3289858d0beb8d3410362a.jpg  \n",
            " extracting: train/images (49)_jpg.rf.1d7135951b3289858d0beb8d3410362a.txt  \n",
            " extracting: train/images (49)_jpg.rf.2b58b291acf045fc19fe1719a4a89b17.jpg  \n",
            " extracting: train/images (49)_jpg.rf.2b58b291acf045fc19fe1719a4a89b17.txt  \n",
            " extracting: train/images (49)_jpg.rf.a722f6e6aaaf3ceccdd70b0da0b7c725.jpg  \n",
            " extracting: train/images (49)_jpg.rf.a722f6e6aaaf3ceccdd70b0da0b7c725.txt  \n",
            " extracting: train/images (5)_jpg.rf.7274c29cb3c4fe66d359276fa7102a59.jpg  \n",
            " extracting: train/images (5)_jpg.rf.7274c29cb3c4fe66d359276fa7102a59.txt  \n",
            " extracting: train/images (5)_jpg.rf.a892c8754a2eadd0c2a37214247b2840.jpg  \n",
            " extracting: train/images (5)_jpg.rf.a892c8754a2eadd0c2a37214247b2840.txt  \n",
            " extracting: train/images (5)_jpg.rf.f0b9671de01b6df427ff69fa895bf98d.jpg  \n",
            " extracting: train/images (5)_jpg.rf.f0b9671de01b6df427ff69fa895bf98d.txt  \n",
            " extracting: train/images (50)_jpg.rf.00adfa45ea23db1c44e6884de706eef4.jpg  \n",
            " extracting: train/images (50)_jpg.rf.00adfa45ea23db1c44e6884de706eef4.txt  \n",
            " extracting: train/images (50)_jpg.rf.05db95000e4b6af62ccd69bd2c528038.jpg  \n",
            " extracting: train/images (50)_jpg.rf.05db95000e4b6af62ccd69bd2c528038.txt  \n",
            " extracting: train/images (50)_jpg.rf.b79bb9b448dc8fa24c066d5e663016bb.jpg  \n",
            " extracting: train/images (50)_jpg.rf.b79bb9b448dc8fa24c066d5e663016bb.txt  \n",
            " extracting: train/images (57)_jpg.rf.3be579ba248b885378d71cf3e345b5e6.jpg  \n",
            " extracting: train/images (57)_jpg.rf.3be579ba248b885378d71cf3e345b5e6.txt  \n",
            " extracting: train/images (57)_jpg.rf.75763fb3877839c46e52764a9d62e8c4.jpg  \n",
            " extracting: train/images (57)_jpg.rf.75763fb3877839c46e52764a9d62e8c4.txt  \n",
            " extracting: train/images (57)_jpg.rf.d6b691d93746783226f0e7ad9f93e38d.jpg  \n",
            " extracting: train/images (57)_jpg.rf.d6b691d93746783226f0e7ad9f93e38d.txt  \n",
            " extracting: train/images (58)_jpg.rf.9c3372f1779b9542ee3d0b6d19346f3c.jpg  \n",
            " extracting: train/images (58)_jpg.rf.9c3372f1779b9542ee3d0b6d19346f3c.txt  \n",
            " extracting: train/images (58)_jpg.rf.cb31903a20012ff5b3f71cb3a2711d23.jpg  \n",
            " extracting: train/images (58)_jpg.rf.cb31903a20012ff5b3f71cb3a2711d23.txt  \n",
            " extracting: train/images (58)_jpg.rf.fb286397dafbf744644e7ae60f7e4e44.jpg  \n",
            " extracting: train/images (58)_jpg.rf.fb286397dafbf744644e7ae60f7e4e44.txt  \n",
            " extracting: train/images (63)_jpg.rf.62263c12b40897474c52921f52b5fe4b.jpg  \n",
            " extracting: train/images (63)_jpg.rf.62263c12b40897474c52921f52b5fe4b.txt  \n",
            " extracting: train/images (63)_jpg.rf.732316f680ff179527bb91e1b5ddaea5.jpg  \n",
            " extracting: train/images (63)_jpg.rf.732316f680ff179527bb91e1b5ddaea5.txt  \n",
            " extracting: train/images (63)_jpg.rf.a99943c897f6c0c4ee3bfea8c43ad41a.jpg  \n",
            " extracting: train/images (63)_jpg.rf.a99943c897f6c0c4ee3bfea8c43ad41a.txt  \n",
            " extracting: train/images (65)_jpg.rf.77b6273822c263f34272ac6d422c0c7f.jpg  \n",
            " extracting: train/images (65)_jpg.rf.77b6273822c263f34272ac6d422c0c7f.txt  \n",
            " extracting: train/images (65)_jpg.rf.9e2d640ed47665c22276ada183375228.jpg  \n",
            " extracting: train/images (65)_jpg.rf.9e2d640ed47665c22276ada183375228.txt  \n",
            " extracting: train/images (65)_jpg.rf.cafe0dd9320b1c04bbc78c46876624f4.jpg  \n",
            " extracting: train/images (65)_jpg.rf.cafe0dd9320b1c04bbc78c46876624f4.txt  \n",
            " extracting: train/images (7)_jpg.rf.1a2d1bd658297d2ced422a9e47f2b23f.jpg  \n",
            " extracting: train/images (7)_jpg.rf.1a2d1bd658297d2ced422a9e47f2b23f.txt  \n",
            " extracting: train/images (7)_jpg.rf.46659c2c8618b239120f75278f72d8a7.jpg  \n",
            " extracting: train/images (7)_jpg.rf.46659c2c8618b239120f75278f72d8a7.txt  \n",
            " extracting: train/images (7)_jpg.rf.87a0f8810faab5fa5ffaddd758a445ac.jpg  \n",
            " extracting: train/images (7)_jpg.rf.87a0f8810faab5fa5ffaddd758a445ac.txt  \n",
            " extracting: train/images (71)_jpg.rf.67923c4f13460ec90b66c98999b3f01b.jpg  \n",
            " extracting: train/images (71)_jpg.rf.67923c4f13460ec90b66c98999b3f01b.txt  \n",
            " extracting: train/images (71)_jpg.rf.b44bdce3692e4dea43cdcb76fb2197f1.jpg  \n",
            " extracting: train/images (71)_jpg.rf.b44bdce3692e4dea43cdcb76fb2197f1.txt  \n",
            " extracting: train/images (71)_jpg.rf.e4c88627da95b51ce2b905b6a03c3d92.jpg  \n",
            " extracting: train/images (71)_jpg.rf.e4c88627da95b51ce2b905b6a03c3d92.txt  \n",
            " extracting: train/images (72)_jpg.rf.1c72b5658cafdf264e40e055a89519ba.jpg  \n",
            " extracting: train/images (72)_jpg.rf.1c72b5658cafdf264e40e055a89519ba.txt  \n",
            " extracting: train/images (72)_jpg.rf.ac006b754dcd1a370a58ad34a4037f94.jpg  \n",
            " extracting: train/images (72)_jpg.rf.ac006b754dcd1a370a58ad34a4037f94.txt  \n",
            " extracting: train/images (72)_jpg.rf.b5a0c3ef95784c5f32f4a263ccfcc8ca.jpg  \n",
            " extracting: train/images (72)_jpg.rf.b5a0c3ef95784c5f32f4a263ccfcc8ca.txt  \n",
            " extracting: train/images (75)_jpg.rf.3a9e61328c374e669b05effd5e09f878.jpg  \n",
            " extracting: train/images (75)_jpg.rf.3a9e61328c374e669b05effd5e09f878.txt  \n",
            " extracting: train/images (75)_jpg.rf.9f01b94d861552722a717146cc2eeaca.jpg  \n",
            " extracting: train/images (75)_jpg.rf.9f01b94d861552722a717146cc2eeaca.txt  \n",
            " extracting: train/images (75)_jpg.rf.b0c170f9d2244cf6af8314f5337fe597.jpg  \n",
            " extracting: train/images (75)_jpg.rf.b0c170f9d2244cf6af8314f5337fe597.txt  \n",
            " extracting: train/images (8)_jpg.rf.12477348808a68fcf8855ee147844368.jpg  \n",
            " extracting: train/images (8)_jpg.rf.12477348808a68fcf8855ee147844368.txt  \n",
            " extracting: train/images (8)_jpg.rf.250399a6c4e69c10007adec905c8cfa9.jpg  \n",
            " extracting: train/images (8)_jpg.rf.250399a6c4e69c10007adec905c8cfa9.txt  \n",
            " extracting: train/images (8)_jpg.rf.694afa134080d14aea39568a8885358e.jpg  \n",
            " extracting: train/images (8)_jpg.rf.694afa134080d14aea39568a8885358e.txt  \n",
            " extracting: train/images (84)_jpg.rf.9d04d857a01181a7632c566c844d0a21.jpg  \n",
            " extracting: train/images (84)_jpg.rf.9d04d857a01181a7632c566c844d0a21.txt  \n",
            " extracting: train/images (84)_jpg.rf.bfc6beeb01a567d231ff9af1e4191971.jpg  \n",
            " extracting: train/images (84)_jpg.rf.bfc6beeb01a567d231ff9af1e4191971.txt  \n",
            " extracting: train/images (84)_jpg.rf.e750f54680d4f1b36eed9f9c5968f855.jpg  \n",
            " extracting: train/images (84)_jpg.rf.e750f54680d4f1b36eed9f9c5968f855.txt  \n",
            " extracting: train/images (86)_jpg.rf.7f1e20a4b69acfc557516e4e931b8077.jpg  \n",
            " extracting: train/images (86)_jpg.rf.7f1e20a4b69acfc557516e4e931b8077.txt  \n",
            " extracting: train/images (86)_jpg.rf.d44e06d6e7c9f855770fa36b9d6b292e.jpg  \n",
            " extracting: train/images (86)_jpg.rf.d44e06d6e7c9f855770fa36b9d6b292e.txt  \n",
            " extracting: train/images (86)_jpg.rf.fa2dd014a3d91f3d85d82ccdcc2f1f26.jpg  \n",
            " extracting: train/images (86)_jpg.rf.fa2dd014a3d91f3d85d82ccdcc2f1f26.txt  \n",
            " extracting: train/images (87)_jpg.rf.078cade90eae143818e17ed60dc3b913.jpg  \n",
            " extracting: train/images (87)_jpg.rf.078cade90eae143818e17ed60dc3b913.txt  \n",
            " extracting: train/images (87)_jpg.rf.56deb9ed025b50c99b13ed8d0cc3db08.jpg  \n",
            " extracting: train/images (87)_jpg.rf.56deb9ed025b50c99b13ed8d0cc3db08.txt  \n",
            " extracting: train/images (87)_jpg.rf.9abdd7f845e1725f5278a52c0fe0218a.jpg  \n",
            " extracting: train/images (87)_jpg.rf.9abdd7f845e1725f5278a52c0fe0218a.txt  \n",
            " extracting: train/images (9)_jpg.rf.2046253750bda88e44c62b91558de051.jpg  \n",
            " extracting: train/images (9)_jpg.rf.2046253750bda88e44c62b91558de051.txt  \n",
            " extracting: train/images (9)_jpg.rf.2908823e9131e1c8a73494f7f52b111d.jpg  \n",
            " extracting: train/images (9)_jpg.rf.2908823e9131e1c8a73494f7f52b111d.txt  \n",
            " extracting: train/images (9)_jpg.rf.32be7954bdbf365382fcc55c93d55083.jpg  \n",
            " extracting: train/images (9)_jpg.rf.32be7954bdbf365382fcc55c93d55083.txt  \n",
            " extracting: train/images (91)_jpg.rf.638831b78a4a9be3e794c5f3eb778335.jpg  \n",
            " extracting: train/images (91)_jpg.rf.638831b78a4a9be3e794c5f3eb778335.txt  \n",
            " extracting: train/images (91)_jpg.rf.82593037af9e4e1ff796056208b98167.jpg  \n",
            " extracting: train/images (91)_jpg.rf.82593037af9e4e1ff796056208b98167.txt  \n",
            " extracting: train/images (91)_jpg.rf.f08e0620b69b9b3182f5715e374ce07b.jpg  \n",
            " extracting: train/images (91)_jpg.rf.f08e0620b69b9b3182f5715e374ce07b.txt  \n",
            " extracting: train/images - 2020-10-19T184634-406_jpg.rf.557963309c5976aec5808d7299854775.jpg  \n",
            " extracting: train/images - 2020-10-19T184634-406_jpg.rf.557963309c5976aec5808d7299854775.txt  \n",
            " extracting: train/images - 2020-10-19T184634-406_jpg.rf.aa89ea6bd98783823b056ad9ee7663c8.jpg  \n",
            " extracting: train/images - 2020-10-19T184634-406_jpg.rf.aa89ea6bd98783823b056ad9ee7663c8.txt  \n",
            " extracting: train/images - 2020-10-19T184634-406_jpg.rf.dfd6b4501857831b4dd1bf21a408ba85.jpg  \n",
            " extracting: train/images - 2020-10-19T184634-406_jpg.rf.dfd6b4501857831b4dd1bf21a408ba85.txt  \n",
            " extracting: train/images - 2020-10-19T184634-937_jpg.rf.105faf7b51d5ffad978f4281470812d8.jpg  \n",
            " extracting: train/images - 2020-10-19T184634-937_jpg.rf.105faf7b51d5ffad978f4281470812d8.txt  \n",
            " extracting: train/images - 2020-10-19T184634-937_jpg.rf.6daa44c7efa643564e882e680c9b98dc.jpg  \n",
            " extracting: train/images - 2020-10-19T184634-937_jpg.rf.6daa44c7efa643564e882e680c9b98dc.txt  \n",
            " extracting: train/images - 2020-10-19T184634-937_jpg.rf.78544f58ca68d76718851970c6c9639e.jpg  \n",
            " extracting: train/images - 2020-10-19T184634-937_jpg.rf.78544f58ca68d76718851970c6c9639e.txt  \n",
            " extracting: train/imagesT(16)_jpg.rf.7b5bafe1e4b9f6f87425b1e7573ad1bd.jpg  \n",
            " extracting: train/imagesT(16)_jpg.rf.7b5bafe1e4b9f6f87425b1e7573ad1bd.txt  \n",
            " extracting: train/imagesT(16)_jpg.rf.c4605b675117fed6231a509fbbafebe1.jpg  \n",
            " extracting: train/imagesT(16)_jpg.rf.c4605b675117fed6231a509fbbafebe1.txt  \n",
            " extracting: train/imagesT(16)_jpg.rf.ddadc53f307939fb6ee70f02e9b3d890.jpg  \n",
            " extracting: train/imagesT(16)_jpg.rf.ddadc53f307939fb6ee70f02e9b3d890.txt  \n",
            " extracting: train/images_jpg.rf.3e43960a3064542cfc70f1d1662aa1eb.jpg  \n",
            " extracting: train/images_jpg.rf.3e43960a3064542cfc70f1d1662aa1eb.txt  \n",
            " extracting: train/images_jpg.rf.67f9890a082648aed459ffa35296cce3.jpg  \n",
            " extracting: train/images_jpg.rf.67f9890a082648aed459ffa35296cce3.txt  \n",
            " extracting: train/images_jpg.rf.9306fd28901808a4e8d4d8742ab8e3fd.jpg  \n",
            " extracting: train/images_jpg.rf.9306fd28901808a4e8d4d8742ab8e3fd.txt  \n",
            " extracting: train/sdcsdcscwewef_jpg.rf.3387df2b7a6991c7472ea3df6f4e7846.jpg  \n",
            " extracting: train/sdcsdcscwewef_jpg.rf.3387df2b7a6991c7472ea3df6f4e7846.txt  \n",
            " extracting: train/sdcsdcscwewef_jpg.rf.7ade1c2c5c75f92dee0d85f5f24762d0.jpg  \n",
            " extracting: train/sdcsdcscwewef_jpg.rf.7ade1c2c5c75f92dee0d85f5f24762d0.txt  \n",
            " extracting: train/sdcsdcscwewef_jpg.rf.a3c8c5064f06f85a26a901a080e8300f.jpg  \n",
            " extracting: train/sdcsdcscwewef_jpg.rf.a3c8c5064f06f85a26a901a080e8300f.txt  \n",
            " extracting: train/siddhant_jpeg.rf.2d5a5033dbbd44baab578d59b6dca22e.jpg  \n",
            " extracting: train/siddhant_jpeg.rf.2d5a5033dbbd44baab578d59b6dca22e.txt  \n",
            " extracting: train/siddhant_jpeg.rf.73cfbf0bc25d2d49051ff08b55474c1c.jpg  \n",
            " extracting: train/siddhant_jpeg.rf.73cfbf0bc25d2d49051ff08b55474c1c.txt  \n",
            " extracting: train/siddhant_jpeg.rf.96e3c758eee50ef39013c2aaf84cd496.jpg  \n",
            " extracting: train/siddhant_jpeg.rf.96e3c758eee50ef39013c2aaf84cd496.txt  \n",
            " extracting: train/wfeewefwefwefwefweefwefw_jpg.rf.8b823c28d4e1ccf629e1cc952a24dd85.jpg  \n",
            " extracting: train/wfeewefwefwefwefweefwefw_jpg.rf.8b823c28d4e1ccf629e1cc952a24dd85.txt  \n",
            " extracting: train/wfeewefwefwefwefweefwefw_jpg.rf.9e76ce74f7282838303fc1a4f86f9dac.jpg  \n",
            " extracting: train/wfeewefwefwefwefweefwefw_jpg.rf.9e76ce74f7282838303fc1a4f86f9dac.txt  \n",
            " extracting: train/wfeewefwefwefwefweefwefw_jpg.rf.f2ee1c1dec780b02e3fe11a12ab0ef97.jpg  \n",
            " extracting: train/wfeewefwefwefwefweefwefw_jpg.rf.f2ee1c1dec780b02e3fe11a12ab0ef97.txt  \n",
            " extracting: train/wwww_jpg.rf.03d7acd6cdc56f5dc0845e620aa357e7.jpg  \n",
            " extracting: train/wwww_jpg.rf.03d7acd6cdc56f5dc0845e620aa357e7.txt  \n",
            " extracting: train/wwww_jpg.rf.46549223b0cd99697b24bf1b1cea5e0d.jpg  \n",
            " extracting: train/wwww_jpg.rf.46549223b0cd99697b24bf1b1cea5e0d.txt  \n",
            " extracting: train/wwww_jpg.rf.7ffe443a756f53584200ff41a58956d0.jpg  \n",
            " extracting: train/wwww_jpg.rf.7ffe443a756f53584200ff41a58956d0.txt  \n",
            "   creating: valid/\n",
            " extracting: valid/0amoth_jpg.rf.a5c1a1d82f149c7c282b2035acf7315f.jpg  \n",
            " extracting: valid/0amoth_jpg.rf.a5c1a1d82f149c7c282b2035acf7315f.txt  \n",
            " extracting: valid/11111111111111111111111_jpg.rf.1d687989803e961cfadea20404573a8d.jpg  \n",
            " extracting: valid/11111111111111111111111_jpg.rf.1d687989803e961cfadea20404573a8d.txt  \n",
            " extracting: valid/111111111111111111_jpg.rf.90ad5727ee7f106a34bda7a7d7813102.jpg  \n",
            " extracting: valid/111111111111111111_jpg.rf.90ad5727ee7f106a34bda7a7d7813102.txt  \n",
            " extracting: valid/OIP (42)_jpg.rf.862fdf5a0e5497b8f8d1b48e18fdf020.jpg  \n",
            " extracting: valid/OIP (42)_jpg.rf.862fdf5a0e5497b8f8d1b48e18fdf020.txt  \n",
            " extracting: valid/OIP (45)_jpg.rf.2dc000695f7974d487abb7339572d930.jpg  \n",
            " extracting: valid/OIP (45)_jpg.rf.2dc000695f7974d487abb7339572d930.txt  \n",
            " extracting: valid/Siddhant-_jpeg.rf.c323ee9cc7bcf96dba43ec98bb2b11f0.jpg  \n",
            " extracting: valid/Siddhant-_jpeg.rf.c323ee9cc7bcf96dba43ec98bb2b11f0.txt  \n",
            " extracting: valid/_darknet.labels   \n",
            " extracting: valid/images - 2020-10-19T184635-513_jpg.rf.3bb0074f52573c9a74f8d1b69557d2b4.jpg  \n",
            " extracting: valid/images - 2020-10-19T184635-513_jpg.rf.3bb0074f52573c9a74f8d1b69557d2b4.txt  \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nKlSotFT9Zv-"
      },
      "source": [
        "#2. Train a Custom Model on DarkNet"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZefjbxBqAZ10"
      },
      "source": [
        "***Since we already have a [notebook](https://colab.research.google.com/drive/1PWOwg038EOGNddf6SXDG5AsC8PIcAe-G#scrollTo=NjKzw2TvZrOQ) on how to train YOLOv4 with Darknet, we have simply included the contents here as well.***"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GNVU7eu9CQj3"
      },
      "source": [
        "## Introduction\n",
        "\n",
        "\n",
        "In this notebook, we implement the tiny version of [YOLOv4](https://arxiv.org/pdf/2004.10934.pdf) for training on your own dataset, [YOLOv4 tiny](https://github.com/AlexeyAB/darknet/issues/6067).\n",
        "\n",
        "We also recommend reading our blog post on [Training YOLOv4 on custom data](https://blog.roboflow.ai/training-yolov4-on-a-custom-dataset/) side by side.\n",
        "\n",
        "We will take the following steps to implement YOLOv4 on our custom data:\n",
        "* Configure our GPU environment on Google Colab\n",
        "* Install the Darknet YOLOv4 training environment\n",
        "* Download our custom dataset for YOLOv4 and set up directories\n",
        "* Configure a custom YOLOv4 training config file for Darknet\n",
        "* Train our custom YOLOv4 object detector\n",
        "* Reload YOLOv4 trained weights and make inference on test images\n",
        "\n",
        "When you are done you will have a custom detector that you can use. It will make inference like this:\n",
        "\n",
        "#### ![Roboflow Workmark](https://i.imgur.com/L0n564N.png)\n",
        "\n",
        "### **Reach out for support**\n",
        "\n",
        "If you run into any hurdles on your own data set or just want to share some cool results in your own domain, [reach out!](https://roboflow.ai) \n",
        "\n",
        "\n",
        "\n",
        "#### ![Roboflow Workmark](https://i.imgur.com/WHFqYSJ.png)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mDTvGt2zt7cm"
      },
      "source": [
        "## Configuring CUDA on Colab for YOLOv4\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u-bguKWgtxSx",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3d2655eb-8bb9-414f-c45c-b8ccf0d1e0d4"
      },
      "source": [
        "# CUDA: Let's check that Nvidia CUDA drivers are already pre-installed and which version is it. This can be helpful for debugging.\n",
        "!/usr/local/cuda/bin/nvcc --version"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "nvcc: NVIDIA (R) Cuda compiler driver\n",
            "Copyright (c) 2005-2019 NVIDIA Corporation\n",
            "Built on Sun_Jul_28_19:07:16_PDT_2019\n",
            "Cuda compilation tools, release 10.1, V10.1.243\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "id9hqvGjGdFf"
      },
      "source": [
        "**IMPORTANT!** If you're not training on a Tesla P100 GPU, we will need to tweak our Darknet configuration later based on what type of GPU we have. Let's set that now while we're inspecting the GPU."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o6BRAVo182G5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "776cfbc7-16f8-4f50-93e1-424729aca397"
      },
      "source": [
        "#take a look at the kind of GPU we have\n",
        "!nvidia-smi"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Tue Nov 17 10:31:19 2020       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 455.38       Driver Version: 418.67       CUDA Version: 10.1     |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                               |                      |               MIG M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  Tesla P100-PCIE...  Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   47C    P0    30W / 250W |      0MiB / 16280MiB |      0%      Default |\n",
            "|                               |                      |                 ERR! |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                                  |\n",
            "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
            "|        ID   ID                                                   Usage      |\n",
            "|=============================================================================|\n",
            "|  No running processes found                                                 |\n",
            "+-----------------------------------------------------------------------------+\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z5FkXxGmGK4Q",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "47be524e-d0e4-4f72-d242-54e4eab4a9a2"
      },
      "source": [
        "# Change the number depending on what GPU is listed above, under NVIDIA-SMI > Name.\n",
        "# Tesla K80: 30\n",
        "# Tesla P100: 60\n",
        "# Tesla T4: 75\n",
        "%env compute_capability=60"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "env: compute_capability=60\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "16pvdFMa1FEe"
      },
      "source": [
        "## Installing Darknet for YOLOv4 on Colab\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K9uY-38P93oz",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "556adb51-0532-40a7-8f88-ab5385cad09b"
      },
      "source": [
        "%cd /content/\n",
        "%rm -rf darknet"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HQEktcfj9y9O",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "476b29ab-1fff-468d-b9ce-e60e6f355629"
      },
      "source": [
        "#we clone the fork of darknet maintained by roboflow\n",
        "#small changes have been made to configure darknet for training\n",
        "!git clone https://github.com/roboflow-ai/darknet.git"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cloning into 'darknet'...\n",
            "remote: Enumerating objects: 13289, done.\u001b[K\n",
            "remote: Total 13289 (delta 0), reused 0 (delta 0), pack-reused 13289\u001b[K\n",
            "Receiving objects: 100% (13289/13289), 12.13 MiB | 1.24 MiB/s, done.\n",
            "Resolving deltas: 100% (9106/9106), done.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7FS9Fd4-Yi8-"
      },
      "source": [
        "**IMPORTANT! If you're not using a Tesla P100 GPU**, then uncomment the sed command and replace the arch and code with that matching your GPU. A list can be found [here](http://arnon.dk/matching-sm-architectures-arch-and-gencode-for-various-nvidia-cards/). You can check with the command nvidia-smi (should be run above)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QyMBDkaL-Aep",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d54ea8d8-126b-463c-f2a1-e6bc56e089b9"
      },
      "source": [
        "#install environment from the Makefile. Changes to mitigate CUDA error.\n",
        "%cd darknet/\n",
        "!sed -i 's/OPENCV=0/OPENCV=1/g' Makefile\n",
        "!sed -i 's/GPU=0/GPU=1/g' Makefile\n",
        "!sed -i 's/CUDNN=0/CUDNN=1/g' Makefile\n",
        "!sed -i \"s/ARCH= -gencode arch=compute_60,code=sm_60/ARCH= -gencode arch=compute_${compute_capability},code=sm_${compute_capability}/g\" Makefile\n",
        "!make"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/darknet\n",
            "mkdir -p ./obj/\n",
            "mkdir -p backup\n",
            "chmod +x *.sh\n",
            "g++ -std=c++11 -std=c++11 -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/image_opencv.cpp -o obj/image_opencv.o\n",
            "\u001b[01m\u001b[K./src/image_opencv.cpp:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kvoid draw_detections_cv_v3(void**, detection*, int, float, char**, image**, int, int)\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/image_opencv.cpp:910:23:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kvariable ‘\u001b[01m\u001b[Krgb\u001b[m\u001b[K’ set but not used [\u001b[01;35m\u001b[K-Wunused-but-set-variable\u001b[m\u001b[K]\n",
            "                 float \u001b[01;35m\u001b[Krgb\u001b[m\u001b[K[3];\n",
            "                       \u001b[01;35m\u001b[K^~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/image_opencv.cpp:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kvoid cv_draw_object(image, float*, int, int, int*, float*, int*, int, char**)\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/image_opencv.cpp:1391:14:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kbuff\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "         char \u001b[01;35m\u001b[Kbuff\u001b[m\u001b[K[100];\n",
            "              \u001b[01;35m\u001b[K^~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/image_opencv.cpp:1367:9:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kit_tb_res\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "     int \u001b[01;35m\u001b[Kit_tb_res\u001b[m\u001b[K = cv::createTrackbar(it_trackbar_name, window_name, &it_trackbar_value, 1000);\n",
            "         \u001b[01;35m\u001b[K^~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/image_opencv.cpp:1371:9:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Klr_tb_res\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "     int \u001b[01;35m\u001b[Klr_tb_res\u001b[m\u001b[K = cv::createTrackbar(lr_trackbar_name, window_name, &lr_trackbar_value, 20);\n",
            "         \u001b[01;35m\u001b[K^~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/image_opencv.cpp:1375:9:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kcl_tb_res\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "     int \u001b[01;35m\u001b[Kcl_tb_res\u001b[m\u001b[K = cv::createTrackbar(cl_trackbar_name, window_name, &cl_trackbar_value, classes-1);\n",
            "         \u001b[01;35m\u001b[K^~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/image_opencv.cpp:1378:9:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kbo_tb_res\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "     int \u001b[01;35m\u001b[Kbo_tb_res\u001b[m\u001b[K = cv::createTrackbar(bo_trackbar_name, window_name, boxonly, 1);\n",
            "         \u001b[01;35m\u001b[K^~~~~~~~~\u001b[m\u001b[K\n",
            "g++ -std=c++11 -std=c++11 -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/http_stream.cpp -o obj/http_stream.o\n",
            "In file included from \u001b[01m\u001b[K./src/http_stream.cpp:580:0\u001b[m\u001b[K:\n",
            "\u001b[01m\u001b[K./src/httplib.h:129:0:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[K\"INVALID_SOCKET\" redefined\n",
            " #define INVALID_SOCKET (-1)\n",
            " \n",
            "\u001b[01m\u001b[K./src/http_stream.cpp:73:0:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[Kthis is the location of the previous definition\n",
            " #define INVALID_SOCKET -1\n",
            " \n",
            "\u001b[01m\u001b[K./src/http_stream.cpp:\u001b[m\u001b[K In member function ‘\u001b[01m\u001b[Kbool JSON_sender::write(const char*)\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/http_stream.cpp:249:21:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kn\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "                 int \u001b[01;35m\u001b[Kn\u001b[m\u001b[K = _write(client, outputbuf, outlen);\n",
            "                     \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/http_stream.cpp:\u001b[m\u001b[K In member function ‘\u001b[01m\u001b[Kbool MJPG_sender::write(const cv::Mat&)\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/http_stream.cpp:507:113:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kformat ‘\u001b[01m\u001b[K%zu\u001b[m\u001b[K’ expects argument of type ‘\u001b[01m\u001b[Ksize_t\u001b[m\u001b[K’, but argument 3 has type ‘\u001b[01m\u001b[Kint\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wformat=\u001b[m\u001b[K]\n",
            "                 sprintf(head, \"--mjpegstream\\r\\nContent-Type: image/jpeg\\r\\nContent-Length: %zu\\r\\n\\r\\n\", outlen\u001b[01;35m\u001b[K)\u001b[m\u001b[K;\n",
            "                                                                                                                 \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/gemm.c -o obj/gemm.o\n",
            "\u001b[01m\u001b[K./src/gemm.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kconvolution_2d\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/gemm.c:2039:15:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kout_w\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "     const int \u001b[01;35m\u001b[Kout_w\u001b[m\u001b[K = (w + 2 * pad - ksize) / stride + 1;    // output_width=input_width for stride=1 and pad=1\n",
            "               \u001b[01;35m\u001b[K^~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/gemm.c:2038:15:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kout_h\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "     const int \u001b[01;35m\u001b[Kout_h\u001b[m\u001b[K = (h + 2 * pad - ksize) / stride + 1;    // output_height=input_height for stride=1 and pad=1\n",
            "               \u001b[01;35m\u001b[K^~~~~\u001b[m\u001b[K\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/utils.c -o obj/utils.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/dark_cuda.c -o obj/dark_cuda.o\n",
            "\u001b[01m\u001b[K./src/dark_cuda.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kcudnn_check_error_extended\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/dark_cuda.c:224:20:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kcomparison between ‘\u001b[01m\u001b[KcudaError_t {aka enum cudaError}\u001b[m\u001b[K’ and ‘\u001b[01m\u001b[Kenum <anonymous>\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wenum-compare\u001b[m\u001b[K]\n",
            "         if (status \u001b[01;35m\u001b[K!=\u001b[m\u001b[K CUDNN_STATUS_SUCCESS)\n",
            "                    \u001b[01;35m\u001b[K^~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/dark_cuda.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kpre_allocate_pinned_memory\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/dark_cuda.c:276:40:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kformat ‘\u001b[01m\u001b[K%u\u001b[m\u001b[K’ expects argument of type ‘\u001b[01m\u001b[Kunsigned int\u001b[m\u001b[K’, but argument 2 has type ‘\u001b[01m\u001b[Klong unsigned int\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wformat=\u001b[m\u001b[K]\n",
            "         printf(\"pre_allocate: size = \u001b[01;35m\u001b[K%Iu\u001b[m\u001b[K MB, num_of_blocks = %Iu, block_size = %Iu MB \\n\",\n",
            "                                      \u001b[01;35m\u001b[K~~^\u001b[m\u001b[K\n",
            "                                      \u001b[32m\u001b[K%Ilu\u001b[m\u001b[K\n",
            "             \u001b[32m\u001b[Ksize / (1024*1024)\u001b[m\u001b[K, num_of_blocks, pinned_block_size / (1024 * 1024));\n",
            "             \u001b[32m\u001b[K~~~~~~~~~~~~~~~~~~\u001b[m\u001b[K          \n",
            "\u001b[01m\u001b[K./src/dark_cuda.c:276:64:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kformat ‘\u001b[01m\u001b[K%u\u001b[m\u001b[K’ expects argument of type ‘\u001b[01m\u001b[Kunsigned int\u001b[m\u001b[K’, but argument 3 has type ‘\u001b[01m\u001b[Ksize_t {aka const long unsigned int}\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wformat=\u001b[m\u001b[K]\n",
            "         printf(\"pre_allocate: size = %Iu MB, num_of_blocks = \u001b[01;35m\u001b[K%Iu\u001b[m\u001b[K, block_size = %Iu MB \\n\",\n",
            "                                                              \u001b[01;35m\u001b[K~~^\u001b[m\u001b[K\n",
            "                                                              \u001b[32m\u001b[K%Ilu\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/dark_cuda.c:276:82:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kformat ‘\u001b[01m\u001b[K%u\u001b[m\u001b[K’ expects argument of type ‘\u001b[01m\u001b[Kunsigned int\u001b[m\u001b[K’, but argument 4 has type ‘\u001b[01m\u001b[Klong unsigned int\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wformat=\u001b[m\u001b[K]\n",
            "         printf(\"pre_allocate: size = %Iu MB, num_of_blocks = %Iu, block_size = \u001b[01;35m\u001b[K%Iu\u001b[m\u001b[K MB \\n\",\n",
            "                                                                                \u001b[01;35m\u001b[K~~^\u001b[m\u001b[K\n",
            "                                                                                \u001b[32m\u001b[K%Ilu\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/dark_cuda.c:286:37:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kformat ‘\u001b[01m\u001b[K%d\u001b[m\u001b[K’ expects argument of type ‘\u001b[01m\u001b[Kint\u001b[m\u001b[K’, but argument 2 has type ‘\u001b[01m\u001b[Ksize_t {aka const long unsigned int}\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wformat=\u001b[m\u001b[K]\n",
            "                 printf(\" Allocated \u001b[01;35m\u001b[K%d\u001b[m\u001b[K pinned block \\n\", pinned_block_size);\n",
            "                                    \u001b[01;35m\u001b[K~^\u001b[m\u001b[K\n",
            "                                    \u001b[32m\u001b[K%ld\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/dark_cuda.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kcuda_make_array_pinned_preallocated\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/dark_cuda.c:307:43:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kformat ‘\u001b[01m\u001b[K%d\u001b[m\u001b[K’ expects argument of type ‘\u001b[01m\u001b[Kint\u001b[m\u001b[K’, but argument 2 has type ‘\u001b[01m\u001b[Ksize_t {aka long unsigned int}\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wformat=\u001b[m\u001b[K]\n",
            "             printf(\"\\n Pinned block_id = \u001b[01;35m\u001b[K%d\u001b[m\u001b[K, filled = %f %% \\n\", pinned_block_id, filled);\n",
            "                                          \u001b[01;35m\u001b[K~^\u001b[m\u001b[K\n",
            "                                          \u001b[32m\u001b[K%ld\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/dark_cuda.c:322:64:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kformat ‘\u001b[01m\u001b[K%d\u001b[m\u001b[K’ expects argument of type ‘\u001b[01m\u001b[Kint\u001b[m\u001b[K’, but argument 2 has type ‘\u001b[01m\u001b[Klong unsigned int\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wformat=\u001b[m\u001b[K]\n",
            "             printf(\"Try to allocate new pinned memory, size = \u001b[01;35m\u001b[K%d\u001b[m\u001b[K MB \\n\", \u001b[32m\u001b[Ksize / (1024 * 1024)\u001b[m\u001b[K);\n",
            "                                                               \u001b[01;35m\u001b[K~^\u001b[m\u001b[K         \u001b[32m\u001b[K~~~~~~~~~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "                                                               \u001b[32m\u001b[K%ld\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/dark_cuda.c:328:63:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kformat ‘\u001b[01m\u001b[K%d\u001b[m\u001b[K’ expects argument of type ‘\u001b[01m\u001b[Kint\u001b[m\u001b[K’, but argument 2 has type ‘\u001b[01m\u001b[Klong unsigned int\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wformat=\u001b[m\u001b[K]\n",
            "             printf(\"Try to allocate new pinned BLOCK, size = \u001b[01;35m\u001b[K%d\u001b[m\u001b[K MB \\n\", \u001b[32m\u001b[Ksize / (1024 * 1024)\u001b[m\u001b[K);\n",
            "                                                              \u001b[01;35m\u001b[K~^\u001b[m\u001b[K         \u001b[32m\u001b[K~~~~~~~~~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "                                                              \u001b[32m\u001b[K%ld\u001b[m\u001b[K\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/convolutional_layer.c -o obj/convolutional_layer.o\n",
            "\u001b[01m\u001b[K./src/convolutional_layer.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kforward_convolutional_layer\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/convolutional_layer.c:1204:32:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kt_intput_size\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "                         size_t \u001b[01;35m\u001b[Kt_intput_size\u001b[m\u001b[K = binary_transpose_align_input(k, n, state.workspace, &l.t_bit_input, ldb_align, l.bit_align);\n",
            "                                \u001b[01;35m\u001b[K^~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/list.c -o obj/list.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/image.c -o obj/image.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/activations.c -o obj/activations.o\n",
            "\u001b[01m\u001b[K./src/activations.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kactivate\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/activations.c:77:5:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kenumeration value ‘\u001b[01m\u001b[KRELU6\u001b[m\u001b[K’ not handled in switch [\u001b[01;35m\u001b[K-Wswitch\u001b[m\u001b[K]\n",
            "     \u001b[01;35m\u001b[Kswitch\u001b[m\u001b[K(a){\n",
            "     \u001b[01;35m\u001b[K^~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/activations.c:77:5:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kenumeration value ‘\u001b[01m\u001b[KSWISH\u001b[m\u001b[K’ not handled in switch [\u001b[01;35m\u001b[K-Wswitch\u001b[m\u001b[K]\n",
            "\u001b[01m\u001b[K./src/activations.c:77:5:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kenumeration value ‘\u001b[01m\u001b[KMISH\u001b[m\u001b[K’ not handled in switch [\u001b[01;35m\u001b[K-Wswitch\u001b[m\u001b[K]\n",
            "\u001b[01m\u001b[K./src/activations.c:77:5:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kenumeration value ‘\u001b[01m\u001b[KNORM_CHAN\u001b[m\u001b[K’ not handled in switch [\u001b[01;35m\u001b[K-Wswitch\u001b[m\u001b[K]\n",
            "\u001b[01m\u001b[K./src/activations.c:77:5:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kenumeration value ‘\u001b[01m\u001b[KNORM_CHAN_SOFTMAX\u001b[m\u001b[K’ not handled in switch [\u001b[01;35m\u001b[K-Wswitch\u001b[m\u001b[K]\n",
            "\u001b[01m\u001b[K./src/activations.c:77:5:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kenumeration value ‘\u001b[01m\u001b[KNORM_CHAN_SOFTMAX_MAXVAL\u001b[m\u001b[K’ not handled in switch [\u001b[01;35m\u001b[K-Wswitch\u001b[m\u001b[K]\n",
            "\u001b[01m\u001b[K./src/activations.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kgradient\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/activations.c:287:5:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kenumeration value ‘\u001b[01m\u001b[KSWISH\u001b[m\u001b[K’ not handled in switch [\u001b[01;35m\u001b[K-Wswitch\u001b[m\u001b[K]\n",
            "     \u001b[01;35m\u001b[Kswitch\u001b[m\u001b[K(a){\n",
            "     \u001b[01;35m\u001b[K^~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/activations.c:287:5:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kenumeration value ‘\u001b[01m\u001b[KMISH\u001b[m\u001b[K’ not handled in switch [\u001b[01;35m\u001b[K-Wswitch\u001b[m\u001b[K]\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/im2col.c -o obj/im2col.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/col2im.c -o obj/col2im.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/blas.c -o obj/blas.o\n",
            "\u001b[01m\u001b[K./src/blas.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kbackward_shortcut_multilayer_cpu\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/blas.c:207:21:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kout_index\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "                 int \u001b[01;35m\u001b[Kout_index\u001b[m\u001b[K = id;\n",
            "                     \u001b[01;35m\u001b[K^~~~~~~~~\u001b[m\u001b[K\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/crop_layer.c -o obj/crop_layer.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/dropout_layer.c -o obj/dropout_layer.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/maxpool_layer.c -o obj/maxpool_layer.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/softmax_layer.c -o obj/softmax_layer.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/data.c -o obj/data.o\n",
            "\u001b[01m\u001b[K./src/data.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kload_data_detection\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/data.c:1134:24:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kx\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "                 int k, \u001b[01;35m\u001b[Kx\u001b[m\u001b[K, y;\n",
            "                        \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/data.c:984:43:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kvariable ‘\u001b[01m\u001b[Kr_scale\u001b[m\u001b[K’ set but not used [\u001b[01;35m\u001b[K-Wunused-but-set-variable\u001b[m\u001b[K]\n",
            "     float r1 = 0, r2 = 0, r3 = 0, r4 = 0, \u001b[01;35m\u001b[Kr_scale\u001b[m\u001b[K = 0;\n",
            "                                           \u001b[01;35m\u001b[K^~~~~~~\u001b[m\u001b[K\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/matrix.c -o obj/matrix.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/network.c -o obj/network.o\n",
            "\u001b[01m\u001b[K./src/network.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kresize_network\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/network.c:610:42:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kpassing argument 1 of ‘\u001b[01m\u001b[KcudaHostAlloc\u001b[m\u001b[K’ from incompatible pointer type [\u001b[01;35m\u001b[K-Wincompatible-pointer-types\u001b[m\u001b[K]\n",
            "         if (cudaSuccess == cudaHostAlloc(\u001b[01;35m\u001b[K&\u001b[m\u001b[Knet->input_pinned_cpu, size * sizeof(float), cudaHostRegisterMapped))\n",
            "                                          \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
            "In file included from \u001b[01m\u001b[K/usr/local/cuda/include/cuda_runtime.h:96:0\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[Kinclude/darknet.h:41\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/network.c:1\u001b[m\u001b[K:\n",
            "\u001b[01m\u001b[K/usr/local/cuda/include/cuda_runtime_api.h:4391:39:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[Kexpected ‘\u001b[01m\u001b[Kvoid **\u001b[m\u001b[K’ but argument is of type ‘\u001b[01m\u001b[Kfloat **\u001b[m\u001b[K’\n",
            " extern __host__ cudaError_t CUDARTAPI \u001b[01;36m\u001b[KcudaHostAlloc\u001b[m\u001b[K(void **pHost, size_t size, unsigned int flags);\n",
            "                                       \u001b[01;36m\u001b[K^~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/connected_layer.c -o obj/connected_layer.o\n",
            "\u001b[01m\u001b[K./src/connected_layer.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kforward_connected_layer_gpu\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/connected_layer.c:346:11:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kone\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "     float \u001b[01;35m\u001b[Kone\u001b[m\u001b[K = 1;    // alpha[0], beta[0]\n",
            "           \u001b[01;35m\u001b[K^~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/connected_layer.c:344:13:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kc\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "     float * \u001b[01;35m\u001b[Kc\u001b[m\u001b[K = l.output_gpu;\n",
            "             \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/connected_layer.c:343:13:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kb\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "     float * \u001b[01;35m\u001b[Kb\u001b[m\u001b[K = l.weights_gpu;\n",
            "             \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/connected_layer.c:342:13:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Ka\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "     float * \u001b[01;35m\u001b[Ka\u001b[m\u001b[K = state.input;\n",
            "             \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/connected_layer.c:341:9:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kn\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "     int \u001b[01;35m\u001b[Kn\u001b[m\u001b[K = l.outputs;\n",
            "         \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/connected_layer.c:340:9:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kk\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "     int \u001b[01;35m\u001b[Kk\u001b[m\u001b[K = l.inputs;\n",
            "         \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/connected_layer.c:339:9:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Km\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "     int \u001b[01;35m\u001b[Km\u001b[m\u001b[K = l.batch;\n",
            "         \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/cost_layer.c -o obj/cost_layer.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/parser.c -o obj/parser.o\n",
            "\u001b[01m\u001b[K./src/parser.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kparse_network_cfg_custom\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/parser.c:1573:42:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kpassing argument 1 of ‘\u001b[01m\u001b[KcudaHostAlloc\u001b[m\u001b[K’ from incompatible pointer type [\u001b[01;35m\u001b[K-Wincompatible-pointer-types\u001b[m\u001b[K]\n",
            "         if (cudaSuccess == cudaHostAlloc(\u001b[01;35m\u001b[K&\u001b[m\u001b[Knet.input_pinned_cpu, size * sizeof(float), cudaHostRegisterMapped)) net.input_pinned_cpu_flag = 1;\n",
            "                                          \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
            "In file included from \u001b[01m\u001b[K/usr/local/cuda/include/cuda_runtime.h:96:0\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[Kinclude/darknet.h:41\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/activations.h:3\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/activation_layer.h:4\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/parser.c:6\u001b[m\u001b[K:\n",
            "\u001b[01m\u001b[K/usr/local/cuda/include/cuda_runtime_api.h:4391:39:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[Kexpected ‘\u001b[01m\u001b[Kvoid **\u001b[m\u001b[K’ but argument is of type ‘\u001b[01m\u001b[Kfloat **\u001b[m\u001b[K’\n",
            " extern __host__ cudaError_t CUDARTAPI \u001b[01;36m\u001b[KcudaHostAlloc\u001b[m\u001b[K(void **pHost, size_t size, unsigned int flags);\n",
            "                                       \u001b[01;36m\u001b[K^~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/option_list.c -o obj/option_list.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/darknet.c -o obj/darknet.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/detection_layer.c -o obj/detection_layer.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/captcha.c -o obj/captcha.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/route_layer.c -o obj/route_layer.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/writing.c -o obj/writing.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/box.c -o obj/box.o\n",
            "\u001b[01m\u001b[K./src/box.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kbox_iou_kind\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/box.c:154:5:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kenumeration value ‘\u001b[01m\u001b[KMSE\u001b[m\u001b[K’ not handled in switch [\u001b[01;35m\u001b[K-Wswitch\u001b[m\u001b[K]\n",
            "     \u001b[01;35m\u001b[Kswitch\u001b[m\u001b[K(iou_kind) {\n",
            "     \u001b[01;35m\u001b[K^~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/box.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kdiounms_sort\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/box.c:898:27:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kbeta_prob\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "                     float \u001b[01;35m\u001b[Kbeta_prob\u001b[m\u001b[K = pow(dets[j].prob[k], 2) / sum_prob;\n",
            "                           \u001b[01;35m\u001b[K^~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/box.c:897:27:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kalpha_prob\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "                     float \u001b[01;35m\u001b[Kalpha_prob\u001b[m\u001b[K = pow(dets[i].prob[k], 2) / sum_prob;\n",
            "                           \u001b[01;35m\u001b[K^~~~~~~~~~\u001b[m\u001b[K\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/nightmare.c -o obj/nightmare.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/normalization_layer.c -o obj/normalization_layer.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/avgpool_layer.c -o obj/avgpool_layer.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/coco.c -o obj/coco.o\n",
            "\u001b[01m\u001b[K./src/coco.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kvalidate_coco_recall\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/coco.c:248:11:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kbase\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "     char *\u001b[01;35m\u001b[Kbase\u001b[m\u001b[K = \"results/comp4_det_test_\";\n",
            "           \u001b[01;35m\u001b[K^~~~\u001b[m\u001b[K\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/dice.c -o obj/dice.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/yolo.c -o obj/yolo.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/detector.c -o obj/detector.o\n",
            "\u001b[01m\u001b[K./src/detector.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kprint_cocos\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/detector.c:451:29:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kformat not a string literal and no format arguments [\u001b[01;35m\u001b[K-Wformat-security\u001b[m\u001b[K]\n",
            "                 fprintf(fp, \u001b[01;35m\u001b[Kbuff\u001b[m\u001b[K);\n",
            "                             \u001b[01;35m\u001b[K^~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/detector.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Keliminate_bdd\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/detector.c:544:21:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kstatement with no effect [\u001b[01;35m\u001b[K-Wunused-value\u001b[m\u001b[K]\n",
            "                     \u001b[01;35m\u001b[Kfor\u001b[m\u001b[K (k; buf[k + n] != '\\0'; k++)\n",
            "                     \u001b[01;35m\u001b[K^~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/detector.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kvalidate_detector\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/detector.c:657:13:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kmkd2\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "         int \u001b[01;35m\u001b[Kmkd2\u001b[m\u001b[K = make_directory(buff2, 0777);\n",
            "             \u001b[01;35m\u001b[K^~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/detector.c:655:13:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kmkd\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "         int \u001b[01;35m\u001b[Kmkd\u001b[m\u001b[K = make_directory(buff, 0777);\n",
            "             \u001b[01;35m\u001b[K^~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/detector.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kvalidate_detector_map\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/detector.c:1276:15:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kclass_recall\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "         float \u001b[01;35m\u001b[Kclass_recall\u001b[m\u001b[K = (float)tp_for_thresh_per_class[i] / ((float)tp_for_thresh_per_class[i] + (float)(truth_classes_count[i] - tp_for_thresh_per_class[i]));\n",
            "               \u001b[01;35m\u001b[K^~~~~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/detector.c:1275:15:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kclass_precision\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "         float \u001b[01;35m\u001b[Kclass_precision\u001b[m\u001b[K = (float)tp_for_thresh_per_class[i] / ((float)tp_for_thresh_per_class[i] + (float)fp_for_thresh_per_class[i]);\n",
            "               \u001b[01;35m\u001b[K^~~~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/detector.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kdraw_object\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/detector.c:1792:19:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kinv_loss\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "             float \u001b[01;35m\u001b[Kinv_loss\u001b[m\u001b[K = 1.0 / max_val_cmp(0.01, avg_loss);\n",
            "                   \u001b[01;35m\u001b[K^~~~~~~~\u001b[m\u001b[K\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/layer.c -o obj/layer.o\n",
            "\u001b[01m\u001b[K./src/layer.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kfree_layer_custom\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/layer.c:191:9:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kthis ‘\u001b[01m\u001b[Kif\u001b[m\u001b[K’ clause does not guard... [\u001b[01;35m\u001b[K-Wmisleading-indentation\u001b[m\u001b[K]\n",
            "         \u001b[01;35m\u001b[Kif\u001b[m\u001b[K (l.x_gpu)                   cuda_free(l.x_gpu);  l.x_gpu = NULL;\n",
            "         \u001b[01;35m\u001b[K^~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/layer.c:191:61:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[K...this statement, but the latter is misleadingly indented as if it were guarded by the ‘\u001b[01m\u001b[Kif\u001b[m\u001b[K’\n",
            "         if (l.x_gpu)                   cuda_free(l.x_gpu);  \u001b[01;36m\u001b[Kl\u001b[m\u001b[K.x_gpu = NULL;\n",
            "                                                             \u001b[01;36m\u001b[K^\u001b[m\u001b[K\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/compare.c -o obj/compare.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/classifier.c -o obj/classifier.o\n",
            "\u001b[01m\u001b[K./src/classifier.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Ktrain_classifier\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/classifier.c:138:9:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kcount\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "     int \u001b[01;35m\u001b[Kcount\u001b[m\u001b[K = 0;\n",
            "         \u001b[01;35m\u001b[K^~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/classifier.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kpredict_classifier\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/classifier.c:831:13:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Ktime\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "     clock_t \u001b[01;35m\u001b[Ktime\u001b[m\u001b[K;\n",
            "             \u001b[01;35m\u001b[K^~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/classifier.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kdemo_classifier\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/classifier.c:1261:49:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Ktval_result\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "         struct timeval tval_before, tval_after, \u001b[01;35m\u001b[Ktval_result\u001b[m\u001b[K;\n",
            "                                                 \u001b[01;35m\u001b[K^~~~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/classifier.c:1261:37:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Ktval_after\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "         struct timeval tval_before, \u001b[01;35m\u001b[Ktval_after\u001b[m\u001b[K, tval_result;\n",
            "                                     \u001b[01;35m\u001b[K^~~~~~~~~~\u001b[m\u001b[K\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/local_layer.c -o obj/local_layer.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/swag.c -o obj/swag.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/shortcut_layer.c -o obj/shortcut_layer.o\n",
            "\u001b[01m\u001b[K./src/shortcut_layer.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kmake_shortcut_layer\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/shortcut_layer.c:55:15:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kscale\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "         float \u001b[01;35m\u001b[Kscale\u001b[m\u001b[K = sqrt(2. / l.nweights);\n",
            "               \u001b[01;35m\u001b[K^~~~~\u001b[m\u001b[K\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/activation_layer.c -o obj/activation_layer.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/rnn_layer.c -o obj/rnn_layer.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/gru_layer.c -o obj/gru_layer.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/rnn.c -o obj/rnn.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/rnn_vid.c -o obj/rnn_vid.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/crnn_layer.c -o obj/crnn_layer.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/demo.c -o obj/demo.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/tag.c -o obj/tag.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/cifar.c -o obj/cifar.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/go.c -o obj/go.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/batchnorm_layer.c -o obj/batchnorm_layer.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/art.c -o obj/art.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/region_layer.c -o obj/region_layer.o\n",
            "\u001b[01m\u001b[K./src/region_layer.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kresize_region_layer\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/region_layer.c:58:9:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kold_h\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "     int \u001b[01;35m\u001b[Kold_h\u001b[m\u001b[K = l->h;\n",
            "         \u001b[01;35m\u001b[K^~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/region_layer.c:57:9:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kold_w\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
            "     int \u001b[01;35m\u001b[Kold_w\u001b[m\u001b[K = l->w;\n",
            "         \u001b[01;35m\u001b[K^~~~~\u001b[m\u001b[K\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/reorg_layer.c -o obj/reorg_layer.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/reorg_old_layer.c -o obj/reorg_old_layer.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/super.c -o obj/super.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/voxel.c -o obj/voxel.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/tree.c -o obj/tree.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/yolo_layer.c -o obj/yolo_layer.o\n",
            "\u001b[01m\u001b[K./src/yolo_layer.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kmake_yolo_layer\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/yolo_layer.c:61:38:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kpassing argument 1 of ‘\u001b[01m\u001b[KcudaHostAlloc\u001b[m\u001b[K’ from incompatible pointer type [\u001b[01;35m\u001b[K-Wincompatible-pointer-types\u001b[m\u001b[K]\n",
            "     if (cudaSuccess == cudaHostAlloc(\u001b[01;35m\u001b[K&\u001b[m\u001b[Kl.output, batch*l.outputs*sizeof(float), cudaHostRegisterMapped)) l.output_pinned = 1;\n",
            "                                      \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
            "In file included from \u001b[01m\u001b[K/usr/local/cuda/include/cuda_runtime.h:96:0\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[Kinclude/darknet.h:41\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/activations.h:3\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/layer.h:4\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/yolo_layer.h:5\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/yolo_layer.c:1\u001b[m\u001b[K:\n",
            "\u001b[01m\u001b[K/usr/local/cuda/include/cuda_runtime_api.h:4391:39:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[Kexpected ‘\u001b[01m\u001b[Kvoid **\u001b[m\u001b[K’ but argument is of type ‘\u001b[01m\u001b[Kfloat **\u001b[m\u001b[K’\n",
            " extern __host__ cudaError_t CUDARTAPI \u001b[01;36m\u001b[KcudaHostAlloc\u001b[m\u001b[K(void **pHost, size_t size, unsigned int flags);\n",
            "                                       \u001b[01;36m\u001b[K^~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/yolo_layer.c:68:38:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kpassing argument 1 of ‘\u001b[01m\u001b[KcudaHostAlloc\u001b[m\u001b[K’ from incompatible pointer type [\u001b[01;35m\u001b[K-Wincompatible-pointer-types\u001b[m\u001b[K]\n",
            "     if (cudaSuccess == cudaHostAlloc(\u001b[01;35m\u001b[K&\u001b[m\u001b[Kl.delta, batch*l.outputs*sizeof(float), cudaHostRegisterMapped)) l.delta_pinned = 1;\n",
            "                                      \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
            "In file included from \u001b[01m\u001b[K/usr/local/cuda/include/cuda_runtime.h:96:0\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[Kinclude/darknet.h:41\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/activations.h:3\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/layer.h:4\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/yolo_layer.h:5\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/yolo_layer.c:1\u001b[m\u001b[K:\n",
            "\u001b[01m\u001b[K/usr/local/cuda/include/cuda_runtime_api.h:4391:39:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[Kexpected ‘\u001b[01m\u001b[Kvoid **\u001b[m\u001b[K’ but argument is of type ‘\u001b[01m\u001b[Kfloat **\u001b[m\u001b[K’\n",
            " extern __host__ cudaError_t CUDARTAPI \u001b[01;36m\u001b[KcudaHostAlloc\u001b[m\u001b[K(void **pHost, size_t size, unsigned int flags);\n",
            "                                       \u001b[01;36m\u001b[K^~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/yolo_layer.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kresize_yolo_layer\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/yolo_layer.c:95:42:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kpassing argument 1 of ‘\u001b[01m\u001b[KcudaHostAlloc\u001b[m\u001b[K’ from incompatible pointer type [\u001b[01;35m\u001b[K-Wincompatible-pointer-types\u001b[m\u001b[K]\n",
            "         if (cudaSuccess != cudaHostAlloc(\u001b[01;35m\u001b[K&\u001b[m\u001b[Kl->output, l->batch*l->outputs * sizeof(float), cudaHostRegisterMapped)) {\n",
            "                                          \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
            "In file included from \u001b[01m\u001b[K/usr/local/cuda/include/cuda_runtime.h:96:0\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[Kinclude/darknet.h:41\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/activations.h:3\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/layer.h:4\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/yolo_layer.h:5\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/yolo_layer.c:1\u001b[m\u001b[K:\n",
            "\u001b[01m\u001b[K/usr/local/cuda/include/cuda_runtime_api.h:4391:39:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[Kexpected ‘\u001b[01m\u001b[Kvoid **\u001b[m\u001b[K’ but argument is of type ‘\u001b[01m\u001b[Kfloat **\u001b[m\u001b[K’\n",
            " extern __host__ cudaError_t CUDARTAPI \u001b[01;36m\u001b[KcudaHostAlloc\u001b[m\u001b[K(void **pHost, size_t size, unsigned int flags);\n",
            "                                       \u001b[01;36m\u001b[K^~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/yolo_layer.c:104:42:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kpassing argument 1 of ‘\u001b[01m\u001b[KcudaHostAlloc\u001b[m\u001b[K’ from incompatible pointer type [\u001b[01;35m\u001b[K-Wincompatible-pointer-types\u001b[m\u001b[K]\n",
            "         if (cudaSuccess != cudaHostAlloc(\u001b[01;35m\u001b[K&\u001b[m\u001b[Kl->delta, l->batch*l->outputs * sizeof(float), cudaHostRegisterMapped)) {\n",
            "                                          \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
            "In file included from \u001b[01m\u001b[K/usr/local/cuda/include/cuda_runtime.h:96:0\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[Kinclude/darknet.h:41\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/activations.h:3\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/layer.h:4\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/yolo_layer.h:5\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/yolo_layer.c:1\u001b[m\u001b[K:\n",
            "\u001b[01m\u001b[K/usr/local/cuda/include/cuda_runtime_api.h:4391:39:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[Kexpected ‘\u001b[01m\u001b[Kvoid **\u001b[m\u001b[K’ but argument is of type ‘\u001b[01m\u001b[Kfloat **\u001b[m\u001b[K’\n",
            " extern __host__ cudaError_t CUDARTAPI \u001b[01;36m\u001b[KcudaHostAlloc\u001b[m\u001b[K(void **pHost, size_t size, unsigned int flags);\n",
            "                                       \u001b[01;36m\u001b[K^~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/yolo_layer.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kforward_yolo_layer\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/yolo_layer.c:371:25:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kvariable ‘\u001b[01m\u001b[Kbest_match_t\u001b[m\u001b[K’ set but not used [\u001b[01;35m\u001b[K-Wunused-but-set-variable\u001b[m\u001b[K]\n",
            "                     int \u001b[01;35m\u001b[Kbest_match_t\u001b[m\u001b[K = 0;\n",
            "                         \u001b[01;35m\u001b[K^~~~~~~~~~~~\u001b[m\u001b[K\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/gaussian_yolo_layer.c -o obj/gaussian_yolo_layer.o\n",
            "\u001b[01m\u001b[K./src/gaussian_yolo_layer.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kmake_gaussian_yolo_layer\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/gaussian_yolo_layer.c:71:38:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kpassing argument 1 of ‘\u001b[01m\u001b[KcudaHostAlloc\u001b[m\u001b[K’ from incompatible pointer type [\u001b[01;35m\u001b[K-Wincompatible-pointer-types\u001b[m\u001b[K]\n",
            "     if (cudaSuccess == cudaHostAlloc(\u001b[01;35m\u001b[K&\u001b[m\u001b[Kl.output, batch*l.outputs * sizeof(float), cudaHostRegisterMapped)) l.output_pinned = 1;\n",
            "                                      \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
            "In file included from \u001b[01m\u001b[K/usr/local/cuda/include/cuda_runtime.h:96:0\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[Kinclude/darknet.h:41\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/gaussian_yolo_layer.h:5\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/gaussian_yolo_layer.c:7\u001b[m\u001b[K:\n",
            "\u001b[01m\u001b[K/usr/local/cuda/include/cuda_runtime_api.h:4391:39:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[Kexpected ‘\u001b[01m\u001b[Kvoid **\u001b[m\u001b[K’ but argument is of type ‘\u001b[01m\u001b[Kfloat **\u001b[m\u001b[K’\n",
            " extern __host__ cudaError_t CUDARTAPI \u001b[01;36m\u001b[KcudaHostAlloc\u001b[m\u001b[K(void **pHost, size_t size, unsigned int flags);\n",
            "                                       \u001b[01;36m\u001b[K^~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/gaussian_yolo_layer.c:78:38:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kpassing argument 1 of ‘\u001b[01m\u001b[KcudaHostAlloc\u001b[m\u001b[K’ from incompatible pointer type [\u001b[01;35m\u001b[K-Wincompatible-pointer-types\u001b[m\u001b[K]\n",
            "     if (cudaSuccess == cudaHostAlloc(\u001b[01;35m\u001b[K&\u001b[m\u001b[Kl.delta, batch*l.outputs * sizeof(float), cudaHostRegisterMapped)) l.delta_pinned = 1;\n",
            "                                      \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
            "In file included from \u001b[01m\u001b[K/usr/local/cuda/include/cuda_runtime.h:96:0\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[Kinclude/darknet.h:41\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/gaussian_yolo_layer.h:5\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/gaussian_yolo_layer.c:7\u001b[m\u001b[K:\n",
            "\u001b[01m\u001b[K/usr/local/cuda/include/cuda_runtime_api.h:4391:39:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[Kexpected ‘\u001b[01m\u001b[Kvoid **\u001b[m\u001b[K’ but argument is of type ‘\u001b[01m\u001b[Kfloat **\u001b[m\u001b[K’\n",
            " extern __host__ cudaError_t CUDARTAPI \u001b[01;36m\u001b[KcudaHostAlloc\u001b[m\u001b[K(void **pHost, size_t size, unsigned int flags);\n",
            "                                       \u001b[01;36m\u001b[K^~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/gaussian_yolo_layer.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kresize_gaussian_yolo_layer\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/gaussian_yolo_layer.c:110:42:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kpassing argument 1 of ‘\u001b[01m\u001b[KcudaHostAlloc\u001b[m\u001b[K’ from incompatible pointer type [\u001b[01;35m\u001b[K-Wincompatible-pointer-types\u001b[m\u001b[K]\n",
            "         if (cudaSuccess != cudaHostAlloc(\u001b[01;35m\u001b[K&\u001b[m\u001b[Kl->output, l->batch*l->outputs * sizeof(float), cudaHostRegisterMapped)) {\n",
            "                                          \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
            "In file included from \u001b[01m\u001b[K/usr/local/cuda/include/cuda_runtime.h:96:0\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[Kinclude/darknet.h:41\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/gaussian_yolo_layer.h:5\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/gaussian_yolo_layer.c:7\u001b[m\u001b[K:\n",
            "\u001b[01m\u001b[K/usr/local/cuda/include/cuda_runtime_api.h:4391:39:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[Kexpected ‘\u001b[01m\u001b[Kvoid **\u001b[m\u001b[K’ but argument is of type ‘\u001b[01m\u001b[Kfloat **\u001b[m\u001b[K’\n",
            " extern __host__ cudaError_t CUDARTAPI \u001b[01;36m\u001b[KcudaHostAlloc\u001b[m\u001b[K(void **pHost, size_t size, unsigned int flags);\n",
            "                                       \u001b[01;36m\u001b[K^~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/gaussian_yolo_layer.c:119:42:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kpassing argument 1 of ‘\u001b[01m\u001b[KcudaHostAlloc\u001b[m\u001b[K’ from incompatible pointer type [\u001b[01;35m\u001b[K-Wincompatible-pointer-types\u001b[m\u001b[K]\n",
            "         if (cudaSuccess != cudaHostAlloc(\u001b[01;35m\u001b[K&\u001b[m\u001b[Kl->delta, l->batch*l->outputs * sizeof(float), cudaHostRegisterMapped)) {\n",
            "                                          \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
            "In file included from \u001b[01m\u001b[K/usr/local/cuda/include/cuda_runtime.h:96:0\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[Kinclude/darknet.h:41\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/gaussian_yolo_layer.h:5\u001b[m\u001b[K,\n",
            "                 from \u001b[01m\u001b[K./src/gaussian_yolo_layer.c:7\u001b[m\u001b[K:\n",
            "\u001b[01m\u001b[K/usr/local/cuda/include/cuda_runtime_api.h:4391:39:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[Kexpected ‘\u001b[01m\u001b[Kvoid **\u001b[m\u001b[K’ but argument is of type ‘\u001b[01m\u001b[Kfloat **\u001b[m\u001b[K’\n",
            " extern __host__ cudaError_t CUDARTAPI \u001b[01;36m\u001b[KcudaHostAlloc\u001b[m\u001b[K(void **pHost, size_t size, unsigned int flags);\n",
            "                                       \u001b[01;36m\u001b[K^~~~~~~~~~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/gaussian_yolo_layer.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kforward_gaussian_yolo_layer\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/gaussian_yolo_layer.c:460:25:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kvariable ‘\u001b[01m\u001b[Kbest_match_t\u001b[m\u001b[K’ set but not used [\u001b[01;35m\u001b[K-Wunused-but-set-variable\u001b[m\u001b[K]\n",
            "                     int \u001b[01;35m\u001b[Kbest_match_t\u001b[m\u001b[K = 0;\n",
            "                         \u001b[01;35m\u001b[K^~~~~~~~~~~~\u001b[m\u001b[K\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/upsample_layer.c -o obj/upsample_layer.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/lstm_layer.c -o obj/lstm_layer.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/conv_lstm_layer.c -o obj/conv_lstm_layer.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/scale_channels_layer.c -o obj/scale_channels_layer.o\n",
            "gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/sam_layer.c -o obj/sam_layer.o\n",
            "nvcc -gencode arch=compute_60,code=sm_60 -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN --compiler-options \"-Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include\" -c ./src/convolutional_kernels.cu -o obj/convolutional_kernels.o\n",
            "nvcc -gencode arch=compute_60,code=sm_60 -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN --compiler-options \"-Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include\" -c ./src/activation_kernels.cu -o obj/activation_kernels.o\n",
            "nvcc -gencode arch=compute_60,code=sm_60 -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN --compiler-options \"-Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include\" -c ./src/im2col_kernels.cu -o obj/im2col_kernels.o\n",
            "\u001b[01m\u001b[K./src/im2col_kernels.cu:125:18:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[K\"/*\" within comment [\u001b[01;35m\u001b[K-Wcomment\u001b[m\u001b[K]\n",
            "                 //*data_col_ptr = (h >= 0 && w >= 0 && h < height && w < width) ?\n",
            "                   \n",
            "\u001b[01m\u001b[K./src/im2col_kernels.cu:1178:6:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[K\"/*\" within comment [\u001b[01;35m\u001b[K-Wcomment\u001b[m\u001b[K]\n",
            "     //*((uint64_t *)(A_s + (local_i*lda + k) / 8)) = *((uint64_t *)(A + (i_cur*lda + k) / 8));    // weights\n",
            "       \n",
            "\u001b[01m\u001b[K./src/im2col_kernels.cu:125:18:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[K\"/*\" within comment [\u001b[01;35m\u001b[K-Wcomment\u001b[m\u001b[K]\n",
            "                 //*data_col_ptr = (h >= 0 && w >= 0 && h < height && w < width) ?\n",
            "                   \n",
            "\u001b[01m\u001b[K./src/im2col_kernels.cu:1178:6:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[K\"/*\" within comment [\u001b[01;35m\u001b[K-Wcomment\u001b[m\u001b[K]\n",
            "     //*((uint64_t *)(A_s + (local_i*lda + k) / 8)) = *((uint64_t *)(A + (i_cur*lda + k) / 8));    // weights\n",
            "       \n",
            "nvcc -gencode arch=compute_60,code=sm_60 -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN --compiler-options \"-Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include\" -c ./src/col2im_kernels.cu -o obj/col2im_kernels.o\n",
            "nvcc -gencode arch=compute_60,code=sm_60 -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN --compiler-options \"-Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include\" -c ./src/blas_kernels.cu -o obj/blas_kernels.o\n",
            "./src/blas_kernels.cu(1038): warning: variable \"out_index\" was declared but never referenced\n",
            "\n",
            "./src/blas_kernels.cu(1082): warning: variable \"step\" was set but never used\n",
            "\n",
            "./src/blas_kernels.cu(1686): warning: variable \"stage_id\" was declared but never referenced\n",
            "\n",
            "\u001b[01m\u001b[K./src/blas_kernels.cu:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kvoid backward_shortcut_multilayer_gpu(int, int, int, int*, float**, float*, float*, float*, float*, int, float*, float**, WEIGHTS_NORMALIZATION_T)\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/blas_kernels.cu:1082:5:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kvariable ‘\u001b[01m\u001b[Kstep\u001b[m\u001b[K’ set but not used [\u001b[01;35m\u001b[K-Wunused-but-set-variable\u001b[m\u001b[K]\n",
            "     \u001b[01;35m\u001b[Kint \u001b[m\u001b[Kstep = 0;\n",
            "     \u001b[01;35m\u001b[K^~~~\u001b[m\u001b[K\n",
            "nvcc -gencode arch=compute_60,code=sm_60 -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN --compiler-options \"-Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include\" -c ./src/crop_layer_kernels.cu -o obj/crop_layer_kernels.o\n",
            "nvcc -gencode arch=compute_60,code=sm_60 -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN --compiler-options \"-Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include\" -c ./src/dropout_layer_kernels.cu -o obj/dropout_layer_kernels.o\n",
            "./src/dropout_layer_kernels.cu(140): warning: variable \"cur_scale\" was declared but never referenced\n",
            "\n",
            "./src/dropout_layer_kernels.cu(245): warning: variable \"cur_scale\" was declared but never referenced\n",
            "\n",
            "./src/dropout_layer_kernels.cu(262): warning: variable \"block_prob\" was declared but never referenced\n",
            "\n",
            "nvcc -gencode arch=compute_60,code=sm_60 -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN --compiler-options \"-Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include\" -c ./src/maxpool_layer_kernels.cu -o obj/maxpool_layer_kernels.o\n",
            "nvcc -gencode arch=compute_60,code=sm_60 -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN --compiler-options \"-Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include\" -c ./src/network_kernels.cu -o obj/network_kernels.o\n",
            "./src/network_kernels.cu(359): warning: variable \"l\" was declared but never referenced\n",
            "\n",
            "./src/network_kernels.cu(368): warning: variable \"im\" was set but never used\n",
            "\n",
            "\u001b[01m\u001b[K./src/network_kernels.cu:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kfloat train_network_datum_gpu(network, float*, float*)\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K./src/network_kernels.cu:359:7:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kvariable ‘\u001b[01m\u001b[Kl\u001b[m\u001b[K’ set but not used [\u001b[01;35m\u001b[K-Wunused-but-set-variable\u001b[m\u001b[K]\n",
            "       \u001b[01;35m\u001b[K \u001b[m\u001b[K layer l = net.layers[net.n - 1];\n",
            "       \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K./src/network_kernels.cu:368:7:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kvariable ‘\u001b[01m\u001b[Kim\u001b[m\u001b[K’ set but not used [\u001b[01;35m\u001b[K-Wunused-but-set-variable\u001b[m\u001b[K]\n",
            "       \u001b[01;35m\u001b[K  \u001b[m\u001b[Kimage im;\n",
            "       \u001b[01;35m\u001b[K^\u001b[m\u001b[K \n",
            "nvcc -gencode arch=compute_60,code=sm_60 -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN --compiler-options \"-Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include\" -c ./src/avgpool_layer_kernels.cu -o obj/avgpool_layer_kernels.o\n",
            "g++ -std=c++11 -std=c++11 -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include obj/image_opencv.o obj/http_stream.o obj/gemm.o obj/utils.o obj/dark_cuda.o obj/convolutional_layer.o obj/list.o obj/image.o obj/activations.o obj/im2col.o obj/col2im.o obj/blas.o obj/crop_layer.o obj/dropout_layer.o obj/maxpool_layer.o obj/softmax_layer.o obj/data.o obj/matrix.o obj/network.o obj/connected_layer.o obj/cost_layer.o obj/parser.o obj/option_list.o obj/darknet.o obj/detection_layer.o obj/captcha.o obj/route_layer.o obj/writing.o obj/box.o obj/nightmare.o obj/normalization_layer.o obj/avgpool_layer.o obj/coco.o obj/dice.o obj/yolo.o obj/detector.o obj/layer.o obj/compare.o obj/classifier.o obj/local_layer.o obj/swag.o obj/shortcut_layer.o obj/activation_layer.o obj/rnn_layer.o obj/gru_layer.o obj/rnn.o obj/rnn_vid.o obj/crnn_layer.o obj/demo.o obj/tag.o obj/cifar.o obj/go.o obj/batchnorm_layer.o obj/art.o obj/region_layer.o obj/reorg_layer.o obj/reorg_old_layer.o obj/super.o obj/voxel.o obj/tree.o obj/yolo_layer.o obj/gaussian_yolo_layer.o obj/upsample_layer.o obj/lstm_layer.o obj/conv_lstm_layer.o obj/scale_channels_layer.o obj/sam_layer.o obj/convolutional_kernels.o obj/activation_kernels.o obj/im2col_kernels.o obj/col2im_kernels.o obj/blas_kernels.o obj/crop_layer_kernels.o obj/dropout_layer_kernels.o obj/maxpool_layer_kernels.o obj/network_kernels.o obj/avgpool_layer_kernels.o -o darknet -lm -pthread `pkg-config --libs opencv4 2> /dev/null || pkg-config --libs opencv` -L/usr/local/cuda/lib64 -lcuda -lcudart -lcublas -lcurand -L/usr/local/cudnn/lib64 -lcudnn -lstdc++ \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LGPDEjfAALrQ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "669d2bd1-e471-4a06-daef-7bf47c0d7a38"
      },
      "source": [
        "#download the newly released yolov4-tiny weights\n",
        "#weights of already trained yolo model on COCO dataset. after thios we are going to refine it to train on our custom images\n",
        "%cd /content/darknet\n",
        "!wget https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v4_pre/yolov4-tiny.weights\n",
        "!wget https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v4_pre/yolov4-tiny.conv.29"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/darknet\n",
            "--2020-11-17 10:38:09--  https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v4_pre/yolov4-tiny.weights\n",
            "Resolving github.com (github.com)... 52.69.186.44\n",
            "Connecting to github.com (github.com)|52.69.186.44|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://github-production-release-asset-2e65be.s3.amazonaws.com/75388965/bc496b80-b701-11ea-817e-8c227b647432?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIWNJYAX4CSVEH53A%2F20201117%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20201117T103809Z&X-Amz-Expires=300&X-Amz-Signature=4c0b70dd9139dfc45636140064be621b31a3d8d1ff66fce5f8ee6006ecb5201c&X-Amz-SignedHeaders=host&actor_id=0&key_id=0&repo_id=75388965&response-content-disposition=attachment%3B%20filename%3Dyolov4-tiny.weights&response-content-type=application%2Foctet-stream [following]\n",
            "--2020-11-17 10:38:09--  https://github-production-release-asset-2e65be.s3.amazonaws.com/75388965/bc496b80-b701-11ea-817e-8c227b647432?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIWNJYAX4CSVEH53A%2F20201117%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20201117T103809Z&X-Amz-Expires=300&X-Amz-Signature=4c0b70dd9139dfc45636140064be621b31a3d8d1ff66fce5f8ee6006ecb5201c&X-Amz-SignedHeaders=host&actor_id=0&key_id=0&repo_id=75388965&response-content-disposition=attachment%3B%20filename%3Dyolov4-tiny.weights&response-content-type=application%2Foctet-stream\n",
            "Resolving github-production-release-asset-2e65be.s3.amazonaws.com (github-production-release-asset-2e65be.s3.amazonaws.com)... 52.216.177.251\n",
            "Connecting to github-production-release-asset-2e65be.s3.amazonaws.com (github-production-release-asset-2e65be.s3.amazonaws.com)|52.216.177.251|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 24251276 (23M) [application/octet-stream]\n",
            "Saving to: ‘yolov4-tiny.weights’\n",
            "\n",
            "yolov4-tiny.weights 100%[===================>]  23.13M  9.11MB/s    in 2.5s    \n",
            "\n",
            "2020-11-17 10:38:13 (9.11 MB/s) - ‘yolov4-tiny.weights’ saved [24251276/24251276]\n",
            "\n",
            "--2020-11-17 10:38:13--  https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v4_pre/yolov4-tiny.conv.29\n",
            "Resolving github.com (github.com)... 52.69.186.44\n",
            "Connecting to github.com (github.com)|52.69.186.44|:443... connected.\n",
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          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oh-vu9eWSyYw"
      },
      "source": [
        "## Configure from Custom Dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CFciBw3iSwqt",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "87e7892e-14d4-41b9-fe2d-8f26bbd30ce6"
      },
      "source": [
        "#Copy dataset\n",
        "%cp -r /content/dataset/. /content/darknet/\n",
        "#Set up training file directories for custom dataset\n",
        "%cd /content/darknet/\n",
        "%cp train/_darknet.labels data/obj.names\n",
        "%mkdir data/obj\n",
        "#copy image and labels\n",
        "%cp train/*.jpg data/obj/\n",
        "%cp valid/*.jpg data/obj/\n",
        "\n",
        "%cp train/*.txt data/obj/\n",
        "%cp valid/*.txt data/obj/\n",
        "\n",
        "with open('data/obj.data', 'w') as out:\n",
        "  out.write('classes = 3\\n')\n",
        "  out.write('train = data/train.txt\\n')\n",
        "  out.write('valid = data/valid.txt\\n')\n",
        "  out.write('names = data/obj.names\\n')\n",
        "  out.write('backup = backup/')\n",
        "\n",
        "#write train file (just the image list)\n",
        "import os\n",
        "\n",
        "with open('data/train.txt', 'w') as out:\n",
        "  for img in [f for f in os.listdir('train') if f.endswith('jpg')]:\n",
        "    out.write('data/obj/' + img + '\\n')\n",
        "\n",
        "#write the valid file (just the image list)\n",
        "import os\n",
        "\n",
        "with open('data/valid.txt', 'w') as out:\n",
        "  for img in [f for f in os.listdir('valid') if f.endswith('jpg')]:\n",
        "    out.write('data/obj/' + img + '\\n')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/darknet\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5HtRqO3QvjkP"
      },
      "source": [
        "## Write Custom Training Config for YOLOv4"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U_WJcqHhpeVr",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2a45bbe5-9fe9-490b-e0c1-ea56d263e879"
      },
      "source": [
        "#we build config dynamically based on number of classes\n",
        "#we build iteratively from base config files. This is the same file shape as cfg/yolo-obj.cfg\n",
        "def file_len(fname):\n",
        "  with open(fname) as f:\n",
        "    for i, l in enumerate(f):\n",
        "      pass\n",
        "  return i + 1\n",
        "\n",
        "num_classes = file_len('train/_darknet.labels')\n",
        "max_batches = num_classes*2000\n",
        "steps1 = .8 * max_batches\n",
        "steps2 = .9 * max_batches\n",
        "steps_str = str(steps1)+','+str(steps2)\n",
        "num_filters = (num_classes + 5) * 3\n",
        "\n",
        "\n",
        "print(\"writing config for a custom YOLOv4 detector detecting number of classes: \" + str(num_classes))\n",
        "\n",
        "#Instructions from the darknet repo\n",
        "#change line max_batches to (classes*2000 but not less than number of training images, and not less than 6000), f.e. max_batches=6000 if you train for 3 classes\n",
        "#change line steps to 80% and 90% of max_batches, f.e. steps=4800,5400\n",
        "if os.path.exists('./cfg/custom-yolov4-tiny-detector.cfg'): os.remove('./cfg/custom-yolov4-tiny-detector.cfg')\n",
        "\n",
        "\n",
        "#customize iPython writefile so we can write variables\n",
        "from IPython.core.magic import register_line_cell_magic\n",
        "\n",
        "@register_line_cell_magic\n",
        "def writetemplate(line, cell):\n",
        "    with open(line, 'w') as f:\n",
        "        f.write(cell.format(**globals()))\n",
        "\n",
        "  \n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "writing config for a custom YOLOv4 detector detecting number of classes: 4\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "03VuD4NHnxFx"
      },
      "source": [
        "%%writetemplate ./cfg/custom-yolov4-tiny-detector.cfg\n",
        "[net]\n",
        "# Testing\n",
        "#batch=1\n",
        "#subdivisions=1\n",
        "# Training\n",
        "batch=64\n",
        "subdivisions=16\n",
        "width=416\n",
        "height=416\n",
        "channels=3\n",
        "momentum=0.9\n",
        "decay=0.0005\n",
        "angle=0\n",
        "saturation = 1.5\n",
        "exposure = 1.5\n",
        "hue=.1\n",
        "\n",
        "learning_rate=0.00261\n",
        "burn_in=1000\n",
        "max_batches = {max_batches}\n",
        "policy=steps\n",
        "steps={steps_str}\n",
        "scales=.1,.1\n",
        "\n",
        "[convolutional]\n",
        "batch_normalize=1\n",
        "filters=32\n",
        "size=3\n",
        "stride=2\n",
        "pad=1\n",
        "activation=leaky\n",
        "\n",
        "[convolutional]\n",
        "batch_normalize=1\n",
        "filters=64\n",
        "size=3\n",
        "stride=2\n",
        "pad=1\n",
        "activation=leaky\n",
        "\n",
        "[convolutional]\n",
        "batch_normalize=1\n",
        "filters=64\n",
        "size=3\n",
        "stride=1\n",
        "pad=1\n",
        "activation=leaky\n",
        "\n",
        "[route]\n",
        "layers=-1\n",
        "groups=2\n",
        "group_id=1\n",
        "\n",
        "[convolutional]\n",
        "batch_normalize=1\n",
        "filters=32\n",
        "size=3\n",
        "stride=1\n",
        "pad=1\n",
        "activation=leaky\n",
        "\n",
        "[convolutional]\n",
        "batch_normalize=1\n",
        "filters=32\n",
        "size=3\n",
        "stride=1\n",
        "pad=1\n",
        "activation=leaky\n",
        "\n",
        "[route]\n",
        "layers = -1,-2\n",
        "\n",
        "[convolutional]\n",
        "batch_normalize=1\n",
        "filters=64\n",
        "size=1\n",
        "stride=1\n",
        "pad=1\n",
        "activation=leaky\n",
        "\n",
        "[route]\n",
        "layers = -6,-1\n",
        "\n",
        "[maxpool]\n",
        "size=2\n",
        "stride=2\n",
        "\n",
        "[convolutional]\n",
        "batch_normalize=1\n",
        "filters=128\n",
        "size=3\n",
        "stride=1\n",
        "pad=1\n",
        "activation=leaky\n",
        "\n",
        "[route]\n",
        "layers=-1\n",
        "groups=2\n",
        "group_id=1\n",
        "\n",
        "[convolutional]\n",
        "batch_normalize=1\n",
        "filters=64\n",
        "size=3\n",
        "stride=1\n",
        "pad=1\n",
        "activation=leaky\n",
        "\n",
        "[convolutional]\n",
        "batch_normalize=1\n",
        "filters=64\n",
        "size=3\n",
        "stride=1\n",
        "pad=1\n",
        "activation=leaky\n",
        "\n",
        "[route]\n",
        "layers = -1,-2\n",
        "\n",
        "[convolutional]\n",
        "batch_normalize=1\n",
        "filters=128\n",
        "size=1\n",
        "stride=1\n",
        "pad=1\n",
        "activation=leaky\n",
        "\n",
        "[route]\n",
        "layers = -6,-1\n",
        "\n",
        "[maxpool]\n",
        "size=2\n",
        "stride=2\n",
        "\n",
        "[convolutional]\n",
        "batch_normalize=1\n",
        "filters=256\n",
        "size=3\n",
        "stride=1\n",
        "pad=1\n",
        "activation=leaky\n",
        "\n",
        "[route]\n",
        "layers=-1\n",
        "groups=2\n",
        "group_id=1\n",
        "\n",
        "[convolutional]\n",
        "batch_normalize=1\n",
        "filters=128\n",
        "size=3\n",
        "stride=1\n",
        "pad=1\n",
        "activation=leaky\n",
        "\n",
        "[convolutional]\n",
        "batch_normalize=1\n",
        "filters=128\n",
        "size=3\n",
        "stride=1\n",
        "pad=1\n",
        "activation=leaky\n",
        "\n",
        "[route]\n",
        "layers = -1,-2\n",
        "\n",
        "[convolutional]\n",
        "batch_normalize=1\n",
        "filters=256\n",
        "size=1\n",
        "stride=1\n",
        "pad=1\n",
        "activation=leaky\n",
        "\n",
        "[route]\n",
        "layers = -6,-1\n",
        "\n",
        "[maxpool]\n",
        "size=2\n",
        "stride=2\n",
        "\n",
        "[convolutional]\n",
        "batch_normalize=1\n",
        "filters=512\n",
        "size=3\n",
        "stride=1\n",
        "pad=1\n",
        "activation=leaky\n",
        "\n",
        "##################################\n",
        "\n",
        "[convolutional]\n",
        "batch_normalize=1\n",
        "filters=256\n",
        "size=1\n",
        "stride=1\n",
        "pad=1\n",
        "activation=leaky\n",
        "\n",
        "[convolutional]\n",
        "batch_normalize=1\n",
        "filters=512\n",
        "size=3\n",
        "stride=1\n",
        "pad=1\n",
        "activation=leaky\n",
        "\n",
        "[convolutional]\n",
        "size=1\n",
        "stride=1\n",
        "pad=1\n",
        "filters={num_filters}\n",
        "activation=linear\n",
        "\n",
        "\n",
        "\n",
        "[yolo]\n",
        "mask = 3,4,5\n",
        "anchors = 10,14,  23,27,  37,58,  81,82,  135,169,  344,319\n",
        "classes={num_classes}\n",
        "num=6\n",
        "jitter=.3\n",
        "scale_x_y = 1.05\n",
        "cls_normalizer=1.0\n",
        "iou_normalizer=0.07\n",
        "iou_loss=ciou\n",
        "ignore_thresh = .7\n",
        "truth_thresh = 1\n",
        "random=0\n",
        "nms_kind=greedynms\n",
        "beta_nms=0.6\n",
        "\n",
        "[route]\n",
        "layers = -4\n",
        "\n",
        "[convolutional]\n",
        "batch_normalize=1\n",
        "filters=128\n",
        "size=1\n",
        "stride=1\n",
        "pad=1\n",
        "activation=leaky\n",
        "\n",
        "[upsample]\n",
        "stride=2\n",
        "\n",
        "[route]\n",
        "layers = -1, 23\n",
        "\n",
        "[convolutional]\n",
        "batch_normalize=1\n",
        "filters=256\n",
        "size=3\n",
        "stride=1\n",
        "pad=1\n",
        "activation=leaky\n",
        "\n",
        "[convolutional]\n",
        "size=1\n",
        "stride=1\n",
        "pad=1\n",
        "filters={num_filters}\n",
        "activation=linear\n",
        "\n",
        "[yolo]\n",
        "mask = 1,2,3\n",
        "anchors = 10,14,  23,27,  37,58,  81,82,  135,169,  344,319\n",
        "classes={num_classes}\n",
        "num=6\n",
        "jitter=.3\n",
        "scale_x_y = 1.05\n",
        "cls_normalizer=1.0\n",
        "iou_normalizer=0.07\n",
        "iou_loss=ciou\n",
        "ignore_thresh = .7\n",
        "truth_thresh = 1\n",
        "random=0\n",
        "nms_kind=greedynms\n",
        "beta_nms=0.6"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u2LAciMh4Cut",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a1b89fdc-c52d-4e4f-d97f-f9baf4523c34"
      },
      "source": [
        "#here is the file that was just written. \n",
        "#you may consider adjusting certain things\n",
        "\n",
        "#like the number of subdivisions 64 runs faster but Colab GPU may not be big enough\n",
        "#if Colab GPU memory is too small, you will need to adjust subdivisions to 16\n",
        "%cat cfg/custom-yolov4-tiny-detector.cfg"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[net]\n",
            "# Testing\n",
            "#batch=1\n",
            "#subdivisions=1\n",
            "# Training\n",
            "batch=64\n",
            "subdivisions=16\n",
            "width=416\n",
            "height=416\n",
            "channels=3\n",
            "momentum=0.9\n",
            "decay=0.0005\n",
            "angle=0\n",
            "saturation = 1.5\n",
            "exposure = 1.5\n",
            "hue=.1\n",
            "\n",
            "learning_rate=0.00261\n",
            "burn_in=1000\n",
            "max_batches = 8000\n",
            "policy=steps\n",
            "steps=6400.0,7200.0\n",
            "scales=.1,.1\n",
            "\n",
            "[convolutional]\n",
            "batch_normalize=1\n",
            "filters=32\n",
            "size=3\n",
            "stride=2\n",
            "pad=1\n",
            "activation=leaky\n",
            "\n",
            "[convolutional]\n",
            "batch_normalize=1\n",
            "filters=64\n",
            "size=3\n",
            "stride=2\n",
            "pad=1\n",
            "activation=leaky\n",
            "\n",
            "[convolutional]\n",
            "batch_normalize=1\n",
            "filters=64\n",
            "size=3\n",
            "stride=1\n",
            "pad=1\n",
            "activation=leaky\n",
            "\n",
            "[route]\n",
            "layers=-1\n",
            "groups=2\n",
            "group_id=1\n",
            "\n",
            "[convolutional]\n",
            "batch_normalize=1\n",
            "filters=32\n",
            "size=3\n",
            "stride=1\n",
            "pad=1\n",
            "activation=leaky\n",
            "\n",
            "[convolutional]\n",
            "batch_normalize=1\n",
            "filters=32\n",
            "size=3\n",
            "stride=1\n",
            "pad=1\n",
            "activation=leaky\n",
            "\n",
            "[route]\n",
            "layers = -1,-2\n",
            "\n",
            "[convolutional]\n",
            "batch_normalize=1\n",
            "filters=64\n",
            "size=1\n",
            "stride=1\n",
            "pad=1\n",
            "activation=leaky\n",
            "\n",
            "[route]\n",
            "layers = -6,-1\n",
            "\n",
            "[maxpool]\n",
            "size=2\n",
            "stride=2\n",
            "\n",
            "[convolutional]\n",
            "batch_normalize=1\n",
            "filters=128\n",
            "size=3\n",
            "stride=1\n",
            "pad=1\n",
            "activation=leaky\n",
            "\n",
            "[route]\n",
            "layers=-1\n",
            "groups=2\n",
            "group_id=1\n",
            "\n",
            "[convolutional]\n",
            "batch_normalize=1\n",
            "filters=64\n",
            "size=3\n",
            "stride=1\n",
            "pad=1\n",
            "activation=leaky\n",
            "\n",
            "[convolutional]\n",
            "batch_normalize=1\n",
            "filters=64\n",
            "size=3\n",
            "stride=1\n",
            "pad=1\n",
            "activation=leaky\n",
            "\n",
            "[route]\n",
            "layers = -1,-2\n",
            "\n",
            "[convolutional]\n",
            "batch_normalize=1\n",
            "filters=128\n",
            "size=1\n",
            "stride=1\n",
            "pad=1\n",
            "activation=leaky\n",
            "\n",
            "[route]\n",
            "layers = -6,-1\n",
            "\n",
            "[maxpool]\n",
            "size=2\n",
            "stride=2\n",
            "\n",
            "[convolutional]\n",
            "batch_normalize=1\n",
            "filters=256\n",
            "size=3\n",
            "stride=1\n",
            "pad=1\n",
            "activation=leaky\n",
            "\n",
            "[route]\n",
            "layers=-1\n",
            "groups=2\n",
            "group_id=1\n",
            "\n",
            "[convolutional]\n",
            "batch_normalize=1\n",
            "filters=128\n",
            "size=3\n",
            "stride=1\n",
            "pad=1\n",
            "activation=leaky\n",
            "\n",
            "[convolutional]\n",
            "batch_normalize=1\n",
            "filters=128\n",
            "size=3\n",
            "stride=1\n",
            "pad=1\n",
            "activation=leaky\n",
            "\n",
            "[route]\n",
            "layers = -1,-2\n",
            "\n",
            "[convolutional]\n",
            "batch_normalize=1\n",
            "filters=256\n",
            "size=1\n",
            "stride=1\n",
            "pad=1\n",
            "activation=leaky\n",
            "\n",
            "[route]\n",
            "layers = -6,-1\n",
            "\n",
            "[maxpool]\n",
            "size=2\n",
            "stride=2\n",
            "\n",
            "[convolutional]\n",
            "batch_normalize=1\n",
            "filters=512\n",
            "size=3\n",
            "stride=1\n",
            "pad=1\n",
            "activation=leaky\n",
            "\n",
            "##################################\n",
            "\n",
            "[convolutional]\n",
            "batch_normalize=1\n",
            "filters=256\n",
            "size=1\n",
            "stride=1\n",
            "pad=1\n",
            "activation=leaky\n",
            "\n",
            "[convolutional]\n",
            "batch_normalize=1\n",
            "filters=512\n",
            "size=3\n",
            "stride=1\n",
            "pad=1\n",
            "activation=leaky\n",
            "\n",
            "[convolutional]\n",
            "size=1\n",
            "stride=1\n",
            "pad=1\n",
            "filters=27\n",
            "activation=linear\n",
            "\n",
            "\n",
            "\n",
            "[yolo]\n",
            "mask = 3,4,5\n",
            "anchors = 10,14,  23,27,  37,58,  81,82,  135,169,  344,319\n",
            "classes=4\n",
            "num=6\n",
            "jitter=.3\n",
            "scale_x_y = 1.05\n",
            "cls_normalizer=1.0\n",
            "iou_normalizer=0.07\n",
            "iou_loss=ciou\n",
            "ignore_thresh = .7\n",
            "truth_thresh = 1\n",
            "random=0\n",
            "nms_kind=greedynms\n",
            "beta_nms=0.6\n",
            "\n",
            "[route]\n",
            "layers = -4\n",
            "\n",
            "[convolutional]\n",
            "batch_normalize=1\n",
            "filters=128\n",
            "size=1\n",
            "stride=1\n",
            "pad=1\n",
            "activation=leaky\n",
            "\n",
            "[upsample]\n",
            "stride=2\n",
            "\n",
            "[route]\n",
            "layers = -1, 23\n",
            "\n",
            "[convolutional]\n",
            "batch_normalize=1\n",
            "filters=256\n",
            "size=3\n",
            "stride=1\n",
            "pad=1\n",
            "activation=leaky\n",
            "\n",
            "[convolutional]\n",
            "size=1\n",
            "stride=1\n",
            "pad=1\n",
            "filters=27\n",
            "activation=linear\n",
            "\n",
            "[yolo]\n",
            "mask = 1,2,3\n",
            "anchors = 10,14,  23,27,  37,58,  81,82,  135,169,  344,319\n",
            "classes=4\n",
            "num=6\n",
            "jitter=.3\n",
            "scale_x_y = 1.05\n",
            "cls_normalizer=1.0\n",
            "iou_normalizer=0.07\n",
            "iou_loss=ciou\n",
            "ignore_thresh = .7\n",
            "truth_thresh = 1\n",
            "random=0\n",
            "nms_kind=greedynms\n",
            "beta_nms=0.6"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vWrG9EGamSpH"
      },
      "source": [
        "## Train Custom YOLOv4 Detector"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6miYFbvExqMd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "02258e2a-013a-44af-d03b-08c6d86d4d35"
      },
      "source": [
        "!./darknet detector train data/obj.data cfg/custom-yolov4-tiny-detector.cfg yolov4-tiny.conv.29 -dont_show -map\n",
        "#If you get CUDA out of memory adjust subdivisions above!\n",
        "#adjust max batches down for shorter training above"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n",
            " (next mAP calculation at 7100 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7047: 0.043331, 0.051206 avg loss, 0.000261 rate, 0.631614 seconds, 451008 images, 0.180930 hours left\n",
            "Loaded: 0.000076 seconds\n",
            "\n",
            " (next mAP calculation at 7100 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7048: 0.070003, 0.053086 avg loss, 0.000261 rate, 0.635095 seconds, 451072 images, 0.180793 hours left\n",
            "Loaded: 0.000085 seconds\n",
            "\n",
            " (next mAP calculation at 7100 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7049: 0.072752, 0.055053 avg loss, 0.000261 rate, 0.612662 seconds, 451136 images, 0.180665 hours left\n",
            "Loaded: 0.000061 seconds\n",
            "\n",
            " (next mAP calculation at 7100 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7050: 0.030293, 0.052577 avg loss, 0.000261 rate, 0.609793 seconds, 451200 images, 0.180477 hours left\n",
            "Loaded: 0.000103 seconds\n",
            "\n",
            " (next mAP calculation at 7100 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7051: 0.078016, 0.055120 avg loss, 0.000261 rate, 0.619119 seconds, 451264 images, 0.180281 hours left\n",
            "Loaded: 0.000066 seconds\n",
            "\n",
            " (next mAP calculation at 7100 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7052: 0.067623, 0.056371 avg loss, 0.000261 rate, 0.664566 seconds, 451328 images, 0.180111 hours left\n",
            "Loaded: 0.000067 seconds\n",
            "\n",
            " (next mAP calculation at 7100 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7053: 0.019630, 0.052697 avg loss, 0.000261 rate, 0.610223 seconds, 451392 images, 0.180060 hours left\n",
            "Loaded: 0.000067 seconds\n",
            "\n",
            " (next mAP calculation at 7100 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7054: 0.074024, 0.054829 avg loss, 0.000261 rate, 0.610590 seconds, 451456 images, 0.179865 hours left\n",
            "Loaded: 0.000046 seconds\n",
            "\n",
            " (next mAP calculation at 7100 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7055: 0.038419, 0.053188 avg loss, 0.000261 rate, 0.658860 seconds, 451520 images, 0.179671 hours left\n",
            "Loaded: 0.000063 seconds\n",
            "\n",
            " (next mAP calculation at 7100 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7056: 0.072531, 0.055123 avg loss, 0.000261 rate, 0.626760 seconds, 451584 images, 0.179604 hours left\n",
            "Loaded: 0.000046 seconds\n",
            "\n",
            " (next mAP calculation at 7100 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7057: 0.060721, 0.055683 avg loss, 0.000261 rate, 0.626375 seconds, 451648 images, 0.179451 hours left\n",
            "Loaded: 0.000070 seconds\n",
            "\n",
            " (next mAP calculation at 7100 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7058: 0.054599, 0.055574 avg loss, 0.000261 rate, 0.633197 seconds, 451712 images, 0.179298 hours left\n",
            "Loaded: 0.000094 seconds\n",
            "\n",
            " (next mAP calculation at 7100 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7059: 0.039576, 0.053974 avg loss, 0.000261 rate, 0.606640 seconds, 451776 images, 0.179162 hours left\n",
            "Loaded: 0.000067 seconds\n",
            "\n",
            " (next mAP calculation at 7100 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7060: 0.075611, 0.056138 avg loss, 0.000261 rate, 0.618281 seconds, 451840 images, 0.178956 hours left\n",
            "Loaded: 0.000066 seconds\n",
            "\n",
            " (next mAP calculation at 7100 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7061: 0.061824, 0.056707 avg loss, 0.000261 rate, 0.608891 seconds, 451904 images, 0.178781 hours left\n",
            "Loaded: 0.000043 seconds\n",
            "\n",
            " (next mAP calculation at 7100 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7062: 0.086762, 0.059712 avg loss, 0.000261 rate, 0.619507 seconds, 451968 images, 0.178582 hours left\n",
            "Loaded: 0.000037 seconds\n",
            "\n",
            " (next mAP calculation at 7100 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7063: 0.034947, 0.057236 avg loss, 0.000261 rate, 0.614128 seconds, 452032 images, 0.178410 hours left\n",
            "Loaded: 0.000043 seconds\n",
            "\n",
            " (next mAP calculation at 7100 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7064: 0.083002, 0.059812 avg loss, 0.000261 rate, 0.560254 seconds, 452096 images, 0.178225 hours left\n",
            "Loaded: 0.000061 seconds\n",
            "\n",
            " (next mAP calculation at 7100 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7065: 0.070931, 0.060924 avg loss, 0.000261 rate, 0.611328 seconds, 452160 images, 0.177899 hours left\n",
            "Loaded: 0.000040 seconds\n",
            "\n",
            " (next mAP calculation at 7100 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7066: 0.052740, 0.060106 avg loss, 0.000261 rate, 0.608689 seconds, 452224 images, 0.177708 hours left\n",
            "Loaded: 0.000044 seconds\n",
            "\n",
            " (next mAP calculation at 7100 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7067: 0.043109, 0.058406 avg loss, 0.000261 rate, 0.668679 seconds, 452288 images, 0.177511 hours left\n",
            "Loaded: 0.000045 seconds\n",
            "\n",
            " (next mAP calculation at 7100 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7068: 0.047233, 0.057289 avg loss, 0.000261 rate, 0.612276 seconds, 452352 images, 0.177469 hours left\n",
            "Loaded: 0.000048 seconds\n",
            "\n",
            " (next mAP calculation at 7100 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7069: 0.058389, 0.057399 avg loss, 0.000261 rate, 0.624660 seconds, 452416 images, 0.177279 hours left\n",
            "Loaded: 0.000081 seconds\n",
            "\n",
            " (next mAP calculation at 7100 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7070: 0.030566, 0.054716 avg loss, 0.000261 rate, 0.627105 seconds, 452480 images, 0.177122 hours left\n",
            "Loaded: 0.000042 seconds\n",
            "\n",
            " (next mAP calculation at 7100 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7071: 0.051396, 0.054384 avg loss, 0.000261 rate, 0.640491 seconds, 452544 images, 0.176971 hours left\n",
            "Loaded: 0.000043 seconds\n",
            "\n",
            " (next mAP calculation at 7100 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7072: 0.070458, 0.055991 avg loss, 0.000261 rate, 0.620999 seconds, 452608 images, 0.176854 hours left\n",
            "Loaded: 0.000043 seconds\n",
            "\n",
            " (next mAP calculation at 7100 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7073: 0.049177, 0.055310 avg loss, 0.000261 rate, 0.661299 seconds, 452672 images, 0.176687 hours left\n",
            "Loaded: 0.000050 seconds\n",
            "\n",
            " (next mAP calculation at 7100 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7074: 0.076025, 0.057381 avg loss, 0.000261 rate, 0.608215 seconds, 452736 images, 0.176623 hours left\n",
            "Loaded: 0.000092 seconds\n",
            "\n",
            " (next mAP calculation at 7100 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7075: 0.055989, 0.057242 avg loss, 0.000261 rate, 0.626750 seconds, 452800 images, 0.176421 hours left\n",
            "Loaded: 0.000043 seconds\n",
            "\n",
            " (next mAP calculation at 7100 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7076: 0.076996, 0.059217 avg loss, 0.000261 rate, 0.583979 seconds, 452864 images, 0.176268 hours left\n",
            "Loaded: 0.000039 seconds\n",
            "\n",
            " (next mAP calculation at 7100 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7077: 0.038074, 0.057103 avg loss, 0.000261 rate, 0.589140 seconds, 452928 images, 0.176004 hours left\n",
            "Loaded: 0.000040 seconds\n",
            "\n",
            " (next mAP calculation at 7100 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7078: 0.081925, 0.059585 avg loss, 0.000261 rate, 0.657918 seconds, 452992 images, 0.175755 hours left\n",
            "Loaded: 0.000075 seconds\n",
            "\n",
            " (next mAP calculation at 7100 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7079: 0.069016, 0.060528 avg loss, 0.000261 rate, 0.569672 seconds, 453056 images, 0.175682 hours left\n",
            "Loaded: 0.000042 seconds\n",
            "\n",
            " (next mAP calculation at 7100 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7080: 0.051326, 0.059608 avg loss, 0.000261 rate, 0.600755 seconds, 453120 images, 0.175383 hours left\n",
            "Loaded: 0.000039 seconds\n",
            "\n",
            " (next mAP calculation at 7100 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7081: 0.053263, 0.058974 avg loss, 0.000261 rate, 0.627798 seconds, 453184 images, 0.175165 hours left\n",
            "Loaded: 0.000054 seconds\n",
            "\n",
            " (next mAP calculation at 7100 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7082: 0.035807, 0.056657 avg loss, 0.000261 rate, 0.636178 seconds, 453248 images, 0.175016 hours left\n",
            "Loaded: 0.000049 seconds\n",
            "\n",
            " (next mAP calculation at 7100 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7083: 0.046882, 0.055680 avg loss, 0.000261 rate, 0.597423 seconds, 453312 images, 0.174888 hours left\n",
            "Loaded: 0.000056 seconds\n",
            "\n",
            " (next mAP calculation at 7100 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7084: 0.058149, 0.055926 avg loss, 0.000261 rate, 0.600162 seconds, 453376 images, 0.174661 hours left\n",
            "Loaded: 0.000045 seconds\n",
            "\n",
            " (next mAP calculation at 7100 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7085: 0.039447, 0.054279 avg loss, 0.000261 rate, 0.597530 seconds, 453440 images, 0.174442 hours left\n",
            "Loaded: 0.000049 seconds\n",
            "\n",
            " (next mAP calculation at 7100 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7086: 0.069804, 0.055831 avg loss, 0.000261 rate, 0.591441 seconds, 453504 images, 0.174216 hours left\n",
            "Loaded: 0.000036 seconds\n",
            "\n",
            " (next mAP calculation at 7100 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7087: 0.041062, 0.054354 avg loss, 0.000261 rate, 0.619362 seconds, 453568 images, 0.173976 hours left\n",
            "Loaded: 0.000045 seconds\n",
            "\n",
            " (next mAP calculation at 7100 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7088: 0.043178, 0.053237 avg loss, 0.000261 rate, 0.613049 seconds, 453632 images, 0.173807 hours left\n",
            "Loaded: 0.000042 seconds\n",
            "\n",
            " (next mAP calculation at 7100 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7089: 0.063138, 0.054227 avg loss, 0.000261 rate, 0.626956 seconds, 453696 images, 0.173622 hours left\n",
            "Loaded: 0.000082 seconds\n",
            "\n",
            " (next mAP calculation at 7100 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7090: 0.049591, 0.053763 avg loss, 0.000261 rate, 0.629568 seconds, 453760 images, 0.173473 hours left\n",
            "Loaded: 0.000045 seconds\n",
            "\n",
            " (next mAP calculation at 7100 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7091: 0.047424, 0.053129 avg loss, 0.000261 rate, 0.608186 seconds, 453824 images, 0.173329 hours left\n",
            "Loaded: 0.000040 seconds\n",
            "\n",
            " (next mAP calculation at 7100 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7092: 0.037196, 0.051536 avg loss, 0.000261 rate, 0.605954 seconds, 453888 images, 0.173132 hours left\n",
            "Loaded: 0.000044 seconds\n",
            "\n",
            " (next mAP calculation at 7100 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7093: 0.032980, 0.049680 avg loss, 0.000261 rate, 0.613682 seconds, 453952 images, 0.172929 hours left\n",
            "Loaded: 0.000069 seconds\n",
            "\n",
            " (next mAP calculation at 7100 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7094: 0.066181, 0.051330 avg loss, 0.000261 rate, 0.580315 seconds, 454016 images, 0.172746 hours left\n",
            "Loaded: 0.000081 seconds\n",
            "\n",
            " (next mAP calculation at 7100 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7095: 0.029404, 0.049138 avg loss, 0.000261 rate, 0.645109 seconds, 454080 images, 0.172479 hours left\n",
            "Loaded: 0.000120 seconds\n",
            "\n",
            " (next mAP calculation at 7100 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7096: 0.100373, 0.054261 avg loss, 0.000261 rate, 0.592237 seconds, 454144 images, 0.172377 hours left\n",
            "Loaded: 0.000043 seconds\n",
            "\n",
            " (next mAP calculation at 7100 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7097: 0.057878, 0.054623 avg loss, 0.000261 rate, 0.621422 seconds, 454208 images, 0.172140 hours left\n",
            "Loaded: 0.000053 seconds\n",
            "\n",
            " (next mAP calculation at 7100 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7098: 0.039275, 0.053088 avg loss, 0.000261 rate, 0.592498 seconds, 454272 images, 0.171978 hours left\n",
            "Loaded: 0.000042 seconds\n",
            "\n",
            " (next mAP calculation at 7100 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7099: 0.047865, 0.052566 avg loss, 0.000261 rate, 0.643368 seconds, 454336 images, 0.171743 hours left\n",
            "Loaded: 0.000093 seconds\n",
            "\n",
            " (next mAP calculation at 7100 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7100: 0.072345, 0.054544 avg loss, 0.000261 rate, 0.637909 seconds, 454400 images, 0.171636 hours left\n",
            "\n",
            " calculation mAP (mean average precision)...\n",
            "8\n",
            " detections_count = 18, unique_truth_count = 8  \n",
            "class_id = 0, name = gundhi, ap = 0.00%   \t (TP = 0, FP = 1) \n",
            "class_id = 1, name = hispaa_insect, ap = 100.00%   \t (TP = 3, FP = 0) \n",
            "class_id = 2, name = stem_bor_lar, ap = 75.00%   \t (TP = 3, FP = 1) \n",
            "class_id = 3, name = stem_bor_moth, ap = 50.00%   \t (TP = 2, FP = 1) \n",
            "\n",
            " for conf_thresh = 0.25, precision = 0.73, recall = 1.00, F1-score = 0.84 \n",
            " for conf_thresh = 0.25, TP = 8, FP = 3, FN = 0, average IoU = 52.28 % \n",
            "\n",
            " IoU threshold = 50 %, used Area-Under-Curve for each unique Recall \n",
            " mean average precision (mAP@0.50) = 0.562500, or 56.25 % \n",
            "Total Detection Time: 0 Seconds\n",
            "\n",
            "Set -points flag:\n",
            " `-points 101` for MS COCO \n",
            " `-points 11` for PascalVOC 2007 (uncomment `difficult` in voc.data) \n",
            " `-points 0` (AUC) for ImageNet, PascalVOC 2010-2012, your custom dataset\n",
            "\n",
            " mean_average_precision (mAP@0.5) = 0.562500 \n",
            "Saving weights to backup//custom-yolov4-tiny-detector_last.weights\n",
            "Loaded: 0.000102 seconds\n",
            "\n",
            " (next mAP calculation at 7200 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7101: 0.042042, 0.053294 avg loss, 0.000261 rate, 0.637133 seconds, 454464 images, 0.171610 hours left\n",
            "Loaded: 0.000045 seconds\n",
            "\n",
            " (next mAP calculation at 7200 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7102: 0.064824, 0.054447 avg loss, 0.000261 rate, 0.623620 seconds, 454528 images, 0.171485 hours left\n",
            "Loaded: 0.000103 seconds\n",
            "\n",
            " (next mAP calculation at 7200 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7103: 0.063996, 0.055401 avg loss, 0.000261 rate, 0.609137 seconds, 454592 images, 0.171326 hours left\n",
            "Loaded: 0.000069 seconds\n",
            "\n",
            " (next mAP calculation at 7200 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7104: 0.050515, 0.054913 avg loss, 0.000261 rate, 0.627880 seconds, 454656 images, 0.171131 hours left\n",
            "Loaded: 0.000047 seconds\n",
            "\n",
            " (next mAP calculation at 7200 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7105: 0.030548, 0.052476 avg loss, 0.000261 rate, 0.623171 seconds, 454720 images, 0.170982 hours left\n",
            "Loaded: 0.000050 seconds\n",
            "\n",
            " (next mAP calculation at 7200 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7106: 0.061926, 0.053421 avg loss, 0.000261 rate, 0.596911 seconds, 454784 images, 0.170822 hours left\n",
            "Loaded: 0.000045 seconds\n",
            "\n",
            " (next mAP calculation at 7200 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7107: 0.064749, 0.054554 avg loss, 0.000261 rate, 0.567868 seconds, 454848 images, 0.170596 hours left\n",
            "Loaded: 0.000033 seconds\n",
            "\n",
            " (next mAP calculation at 7200 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7108: 0.087727, 0.057871 avg loss, 0.000261 rate, 0.576147 seconds, 454912 images, 0.170299 hours left\n",
            "Loaded: 0.000046 seconds\n",
            "\n",
            " (next mAP calculation at 7200 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7109: 0.036129, 0.055697 avg loss, 0.000261 rate, 0.588124 seconds, 454976 images, 0.170024 hours left\n",
            "Loaded: 0.000042 seconds\n",
            "\n",
            " (next mAP calculation at 7200 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7110: 0.091886, 0.059316 avg loss, 0.000261 rate, 0.586293 seconds, 455040 images, 0.169779 hours left\n",
            "Loaded: 0.000043 seconds\n",
            "\n",
            " (next mAP calculation at 7200 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7111: 0.051889, 0.058573 avg loss, 0.000261 rate, 0.640360 seconds, 455104 images, 0.169531 hours left\n",
            "Loaded: 0.000060 seconds\n",
            "\n",
            " (next mAP calculation at 7200 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7112: 0.064634, 0.059179 avg loss, 0.000261 rate, 0.661353 seconds, 455168 images, 0.169417 hours left\n",
            "Loaded: 0.000061 seconds\n",
            "\n",
            " (next mAP calculation at 7200 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7113: 0.061025, 0.059364 avg loss, 0.000261 rate, 0.619548 seconds, 455232 images, 0.169354 hours left\n",
            "Loaded: 0.000068 seconds\n",
            "\n",
            " (next mAP calculation at 7200 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7114: 0.047184, 0.058146 avg loss, 0.000261 rate, 0.590891 seconds, 455296 images, 0.169188 hours left\n",
            "Loaded: 0.000042 seconds\n",
            "\n",
            " (next mAP calculation at 7200 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7115: 0.047831, 0.057115 avg loss, 0.000261 rate, 0.611331 seconds, 455360 images, 0.168950 hours left\n",
            "Loaded: 0.000043 seconds\n",
            "\n",
            " (next mAP calculation at 7200 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7116: 0.083457, 0.059749 avg loss, 0.000261 rate, 0.643640 seconds, 455424 images, 0.168764 hours left\n",
            "Loaded: 0.000069 seconds\n",
            "\n",
            " (next mAP calculation at 7200 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7117: 0.061409, 0.059915 avg loss, 0.000261 rate, 0.599225 seconds, 455488 images, 0.168657 hours left\n",
            "Loaded: 0.000033 seconds\n",
            "\n",
            " (next mAP calculation at 7200 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7118: 0.062392, 0.060163 avg loss, 0.000261 rate, 0.607454 seconds, 455552 images, 0.168440 hours left\n",
            "Loaded: 0.000044 seconds\n",
            "\n",
            " (next mAP calculation at 7200 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7119: 0.045668, 0.058713 avg loss, 0.000261 rate, 0.654240 seconds, 455616 images, 0.168244 hours left\n",
            "Loaded: 0.000042 seconds\n",
            "\n",
            " (next mAP calculation at 7200 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7120: 0.041962, 0.057038 avg loss, 0.000261 rate, 0.622636 seconds, 455680 images, 0.168163 hours left\n",
            "Loaded: 0.000048 seconds\n",
            "\n",
            " (next mAP calculation at 7200 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7121: 0.061718, 0.057506 avg loss, 0.000261 rate, 0.627421 seconds, 455744 images, 0.168003 hours left\n",
            "Loaded: 0.000047 seconds\n",
            "\n",
            " (next mAP calculation at 7200 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7122: 0.082259, 0.059981 avg loss, 0.000261 rate, 0.608842 seconds, 455808 images, 0.167855 hours left\n",
            "Loaded: 0.000042 seconds\n",
            "\n",
            " (next mAP calculation at 7200 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7123: 0.078920, 0.061875 avg loss, 0.000261 rate, 0.608936 seconds, 455872 images, 0.167662 hours left\n",
            "Loaded: 0.000047 seconds\n",
            "\n",
            " (next mAP calculation at 7200 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7124: 0.072032, 0.062891 avg loss, 0.000261 rate, 0.630264 seconds, 455936 images, 0.167469 hours left\n",
            "Loaded: 0.000045 seconds\n",
            "\n",
            " (next mAP calculation at 7200 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7125: 0.049176, 0.061519 avg loss, 0.000261 rate, 0.557919 seconds, 456000 images, 0.167328 hours left\n",
            "Loaded: 0.000047 seconds\n",
            "\n",
            " (next mAP calculation at 7200 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7126: 0.046989, 0.060066 avg loss, 0.000261 rate, 0.661641 seconds, 456064 images, 0.167011 hours left\n",
            "Loaded: 0.000050 seconds\n",
            "\n",
            " (next mAP calculation at 7200 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7127: 0.053848, 0.059444 avg loss, 0.000261 rate, 0.635006 seconds, 456128 images, 0.166947 hours left\n",
            "Loaded: 0.000071 seconds\n",
            "\n",
            " (next mAP calculation at 7200 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7128: 0.024435, 0.055944 avg loss, 0.000261 rate, 0.609243 seconds, 456192 images, 0.166818 hours left\n",
            "Loaded: 0.000071 seconds\n",
            "\n",
            " (next mAP calculation at 7200 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7129: 0.058479, 0.056197 avg loss, 0.000261 rate, 0.601103 seconds, 456256 images, 0.166626 hours left\n",
            "Loaded: 0.000043 seconds\n",
            "\n",
            " (next mAP calculation at 7200 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7130: 0.058060, 0.056383 avg loss, 0.000261 rate, 0.599330 seconds, 456320 images, 0.166414 hours left\n",
            "Loaded: 0.000043 seconds\n",
            "\n",
            " (next mAP calculation at 7200 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7131: 0.047523, 0.055497 avg loss, 0.000261 rate, 0.618906 seconds, 456384 images, 0.166198 hours left\n",
            "Loaded: 0.000043 seconds\n",
            "\n",
            " (next mAP calculation at 7200 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7132: 0.059387, 0.055886 avg loss, 0.000261 rate, 0.590524 seconds, 456448 images, 0.166030 hours left\n",
            "Loaded: 0.000052 seconds\n",
            "\n",
            " (next mAP calculation at 7200 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7133: 0.079175, 0.058215 avg loss, 0.000261 rate, 0.583794 seconds, 456512 images, 0.165794 hours left\n",
            "Loaded: 0.000044 seconds\n",
            "\n",
            " (next mAP calculation at 7200 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7134: 0.075486, 0.059942 avg loss, 0.000261 rate, 0.641600 seconds, 456576 images, 0.165542 hours left\n",
            "Loaded: 0.000061 seconds\n",
            "\n",
            " (next mAP calculation at 7200 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7135: 0.036225, 0.057571 avg loss, 0.000261 rate, 0.596758 seconds, 456640 images, 0.165430 hours left\n",
            "Loaded: 0.000061 seconds\n",
            "\n",
            " (next mAP calculation at 7200 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7136: 0.044070, 0.056221 avg loss, 0.000261 rate, 0.620755 seconds, 456704 images, 0.165210 hours left\n",
            "Loaded: 0.000051 seconds\n",
            "\n",
            " (next mAP calculation at 7200 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7137: 0.056627, 0.056261 avg loss, 0.000261 rate, 0.622583 seconds, 456768 images, 0.165048 hours left\n",
            "Loaded: 0.000043 seconds\n",
            "\n",
            " (next mAP calculation at 7200 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7138: 0.047031, 0.055338 avg loss, 0.000261 rate, 0.563804 seconds, 456832 images, 0.164890 hours left\n",
            "Loaded: 0.000041 seconds\n",
            "\n",
            " (next mAP calculation at 7200 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7139: 0.046643, 0.054469 avg loss, 0.000261 rate, 0.591457 seconds, 456896 images, 0.164591 hours left\n",
            "Loaded: 0.000070 seconds\n",
            "\n",
            " (next mAP calculation at 7200 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7140: 0.026211, 0.051643 avg loss, 0.000261 rate, 0.615579 seconds, 456960 images, 0.164360 hours left\n",
            "Loaded: 0.000046 seconds\n",
            "\n",
            " (next mAP calculation at 7200 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7141: 0.022903, 0.048769 avg loss, 0.000261 rate, 0.681151 seconds, 457024 images, 0.164187 hours left\n",
            "Loaded: 0.000053 seconds\n",
            "\n",
            " (next mAP calculation at 7200 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7142: 0.080416, 0.051934 avg loss, 0.000261 rate, 0.605767 seconds, 457088 images, 0.164171 hours left\n",
            "Loaded: 0.000057 seconds\n",
            "\n",
            " (next mAP calculation at 7200 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7143: 0.072478, 0.053988 avg loss, 0.000261 rate, 0.570341 seconds, 457152 images, 0.163973 hours left\n",
            "Loaded: 0.000039 seconds\n",
            "\n",
            " (next mAP calculation at 7200 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7144: 0.054417, 0.054031 avg loss, 0.000261 rate, 0.669696 seconds, 457216 images, 0.163691 hours left\n",
            "Loaded: 0.000061 seconds\n",
            "\n",
            " (next mAP calculation at 7200 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7145: 0.047740, 0.053402 avg loss, 0.000261 rate, 0.602638 seconds, 457280 images, 0.163647 hours left\n",
            "Loaded: 0.000041 seconds\n",
            "\n",
            " (next mAP calculation at 7200 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7146: 0.031542, 0.051216 avg loss, 0.000261 rate, 0.625778 seconds, 457344 images, 0.163442 hours left\n",
            "Loaded: 0.000060 seconds\n",
            "\n",
            " (next mAP calculation at 7200 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7147: 0.041611, 0.050255 avg loss, 0.000261 rate, 0.585687 seconds, 457408 images, 0.163292 hours left\n",
            "Loaded: 0.000071 seconds\n",
            "\n",
            " (next mAP calculation at 7200 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7148: 0.046210, 0.049851 avg loss, 0.000261 rate, 0.584604 seconds, 457472 images, 0.163047 hours left\n",
            "Loaded: 0.000043 seconds\n",
            "\n",
            " (next mAP calculation at 7200 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7149: 0.049506, 0.049816 avg loss, 0.000261 rate, 0.620362 seconds, 457536 images, 0.162800 hours left\n",
            "Loaded: 0.000053 seconds\n",
            "\n",
            " (next mAP calculation at 7200 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7150: 0.026035, 0.047438 avg loss, 0.000261 rate, 0.616296 seconds, 457600 images, 0.162639 hours left\n",
            "Loaded: 0.000051 seconds\n",
            "\n",
            " (next mAP calculation at 7200 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7151: 0.064572, 0.049152 avg loss, 0.000261 rate, 0.641012 seconds, 457664 images, 0.162468 hours left\n",
            "Loaded: 0.000044 seconds\n",
            "\n",
            " (next mAP calculation at 7200 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7152: 0.039761, 0.048213 avg loss, 0.000261 rate, 0.570284 seconds, 457728 images, 0.162355 hours left\n",
            "Loaded: 0.000069 seconds\n",
            "\n",
            " (next mAP calculation at 7200 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7153: 0.075208, 0.050912 avg loss, 0.000261 rate, 0.579468 seconds, 457792 images, 0.162075 hours left\n",
            "Loaded: 0.000043 seconds\n",
            "\n",
            " (next mAP calculation at 7200 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7154: 0.050456, 0.050866 avg loss, 0.000261 rate, 0.616572 seconds, 457856 images, 0.161818 hours left\n",
            "Loaded: 0.000044 seconds\n",
            "\n",
            " (next mAP calculation at 7200 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7155: 0.058023, 0.051582 avg loss, 0.000261 rate, 0.590491 seconds, 457920 images, 0.161649 hours left\n",
            "Loaded: 0.000068 seconds\n",
            "\n",
            " (next mAP calculation at 7200 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7156: 0.050295, 0.051453 avg loss, 0.000261 rate, 0.683782 seconds, 457984 images, 0.161418 hours left\n",
            "Loaded: 0.000044 seconds\n",
            "\n",
            " (next mAP calculation at 7200 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7157: 0.059005, 0.052209 avg loss, 0.000261 rate, 0.612154 seconds, 458048 images, 0.161407 hours left\n",
            "Loaded: 0.000045 seconds\n",
            "\n",
            " (next mAP calculation at 7200 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7158: 0.098569, 0.056845 avg loss, 0.000261 rate, 0.654568 seconds, 458112 images, 0.161227 hours left\n",
            "Loaded: 0.000051 seconds\n",
            "\n",
            " (next mAP calculation at 7200 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7159: 0.051823, 0.056342 avg loss, 0.000261 rate, 0.666802 seconds, 458176 images, 0.161146 hours left\n",
            "Loaded: 0.000050 seconds\n",
            "\n",
            " (next mAP calculation at 7200 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7160: 0.066246, 0.057333 avg loss, 0.000261 rate, 0.610502 seconds, 458240 images, 0.161092 hours left\n",
            "Loaded: 0.000042 seconds\n",
            "\n",
            " (next mAP calculation at 7200 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7161: 0.033092, 0.054909 avg loss, 0.000261 rate, 0.570596 seconds, 458304 images, 0.160906 hours left\n",
            "Loaded: 0.000064 seconds\n",
            "\n",
            " (next mAP calculation at 7200 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7162: 0.033599, 0.052778 avg loss, 0.000261 rate, 0.627571 seconds, 458368 images, 0.160627 hours left\n",
            "Loaded: 0.000041 seconds\n",
            "\n",
            " (next mAP calculation at 7200 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7163: 0.032233, 0.050723 avg loss, 0.000261 rate, 0.576886 seconds, 458432 images, 0.160481 hours left\n",
            "Loaded: 0.000054 seconds\n",
            "\n",
            " (next mAP calculation at 7200 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7164: 0.061452, 0.051796 avg loss, 0.000261 rate, 0.618828 seconds, 458496 images, 0.160218 hours left\n",
            "Loaded: 0.000048 seconds\n",
            "\n",
            " (next mAP calculation at 7200 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7165: 0.036369, 0.050253 avg loss, 0.000261 rate, 0.620100 seconds, 458560 images, 0.160053 hours left\n",
            "Loaded: 0.000048 seconds\n",
            "\n",
            " (next mAP calculation at 7200 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7166: 0.072923, 0.052520 avg loss, 0.000261 rate, 0.635137 seconds, 458624 images, 0.159891 hours left\n",
            "Loaded: 0.000083 seconds\n",
            "\n",
            " (next mAP calculation at 7200 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7167: 0.047637, 0.052032 avg loss, 0.000261 rate, 0.595057 seconds, 458688 images, 0.159764 hours left\n",
            "Loaded: 0.000042 seconds\n",
            "\n",
            " (next mAP calculation at 7200 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7168: 0.032553, 0.050084 avg loss, 0.000261 rate, 0.607759 seconds, 458752 images, 0.159543 hours left\n",
            "Loaded: 0.000077 seconds\n",
            "\n",
            " (next mAP calculation at 7200 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7169: 0.044603, 0.049536 avg loss, 0.000261 rate, 0.581939 seconds, 458816 images, 0.159352 hours left\n",
            "Loaded: 0.000040 seconds\n",
            "\n",
            " (next mAP calculation at 7200 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7170: 0.066924, 0.051275 avg loss, 0.000261 rate, 0.582658 seconds, 458880 images, 0.159102 hours left\n",
            "Loaded: 0.000055 seconds\n",
            "\n",
            " (next mAP calculation at 7200 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7171: 0.058394, 0.051987 avg loss, 0.000261 rate, 0.641879 seconds, 458944 images, 0.158855 hours left\n",
            "Loaded: 0.000070 seconds\n",
            "\n",
            " (next mAP calculation at 7200 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7172: 0.049067, 0.051695 avg loss, 0.000261 rate, 0.651636 seconds, 459008 images, 0.158745 hours left\n",
            "Loaded: 0.000045 seconds\n",
            "\n",
            " (next mAP calculation at 7200 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7173: 0.058669, 0.052392 avg loss, 0.000261 rate, 0.611203 seconds, 459072 images, 0.158656 hours left\n",
            "Loaded: 0.000075 seconds\n",
            "\n",
            " (next mAP calculation at 7200 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7174: 0.043555, 0.051508 avg loss, 0.000261 rate, 0.602160 seconds, 459136 images, 0.158474 hours left\n",
            "Loaded: 0.000047 seconds\n",
            "\n",
            " (next mAP calculation at 7200 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7175: 0.051432, 0.051501 avg loss, 0.000261 rate, 0.631140 seconds, 459200 images, 0.158271 hours left\n",
            "Loaded: 0.000067 seconds\n",
            "\n",
            " (next mAP calculation at 7200 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7176: 0.067828, 0.053133 avg loss, 0.000261 rate, 0.595569 seconds, 459264 images, 0.158135 hours left\n",
            "Loaded: 0.000067 seconds\n",
            "\n",
            " (next mAP calculation at 7200 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7177: 0.056816, 0.053502 avg loss, 0.000261 rate, 0.623420 seconds, 459328 images, 0.157917 hours left\n",
            "Loaded: 0.000060 seconds\n",
            "\n",
            " (next mAP calculation at 7200 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7178: 0.091356, 0.057287 avg loss, 0.000261 rate, 0.596099 seconds, 459392 images, 0.157763 hours left\n",
            "Loaded: 0.000035 seconds\n",
            "\n",
            " (next mAP calculation at 7200 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7179: 0.060366, 0.057595 avg loss, 0.000261 rate, 0.648728 seconds, 459456 images, 0.157546 hours left\n",
            "Loaded: 0.000065 seconds\n",
            "\n",
            " (next mAP calculation at 7200 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7180: 0.016006, 0.053436 avg loss, 0.000261 rate, 0.645565 seconds, 459520 images, 0.157451 hours left\n",
            "Loaded: 0.000046 seconds\n",
            "\n",
            " (next mAP calculation at 7200 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7181: 0.055634, 0.053656 avg loss, 0.000261 rate, 0.610008 seconds, 459584 images, 0.157347 hours left\n",
            "Loaded: 0.000066 seconds\n",
            "\n",
            " (next mAP calculation at 7200 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7182: 0.090647, 0.057355 avg loss, 0.000261 rate, 0.616633 seconds, 459648 images, 0.157161 hours left\n",
            "Loaded: 0.000047 seconds\n",
            "\n",
            " (next mAP calculation at 7200 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7183: 0.068969, 0.058516 avg loss, 0.000261 rate, 0.559190 seconds, 459712 images, 0.156991 hours left\n",
            "Loaded: 0.000048 seconds\n",
            "\n",
            " (next mAP calculation at 7200 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7184: 0.061301, 0.058795 avg loss, 0.000261 rate, 0.647242 seconds, 459776 images, 0.156690 hours left\n",
            "Loaded: 0.000048 seconds\n",
            "\n",
            " (next mAP calculation at 7200 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7185: 0.036588, 0.056574 avg loss, 0.000261 rate, 0.630195 seconds, 459840 images, 0.156590 hours left\n",
            "Loaded: 0.000042 seconds\n",
            "\n",
            " (next mAP calculation at 7200 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7186: 0.017375, 0.052654 avg loss, 0.000261 rate, 0.623767 seconds, 459904 images, 0.156451 hours left\n",
            "Loaded: 0.000092 seconds\n",
            "\n",
            " (next mAP calculation at 7200 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7187: 0.082377, 0.055627 avg loss, 0.000261 rate, 0.589310 seconds, 459968 images, 0.156297 hours left\n",
            "Loaded: 0.000065 seconds\n",
            "\n",
            " (next mAP calculation at 7200 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7188: 0.061272, 0.056191 avg loss, 0.000261 rate, 0.606299 seconds, 460032 images, 0.156065 hours left\n",
            "Loaded: 0.000057 seconds\n",
            "\n",
            " (next mAP calculation at 7200 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7189: 0.050162, 0.055588 avg loss, 0.000261 rate, 0.591405 seconds, 460096 images, 0.155873 hours left\n",
            "Loaded: 0.000094 seconds\n",
            "\n",
            " (next mAP calculation at 7200 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7190: 0.091741, 0.059204 avg loss, 0.000261 rate, 0.649521 seconds, 460160 images, 0.155646 hours left\n",
            "Loaded: 0.000064 seconds\n",
            "\n",
            " (next mAP calculation at 7200 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7191: 0.044018, 0.057685 avg loss, 0.000261 rate, 0.557544 seconds, 460224 images, 0.155551 hours left\n",
            "Loaded: 0.000087 seconds\n",
            "\n",
            " (next mAP calculation at 7200 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7192: 0.085399, 0.060456 avg loss, 0.000261 rate, 0.614744 seconds, 460288 images, 0.155249 hours left\n",
            "Loaded: 0.000082 seconds\n",
            "\n",
            " (next mAP calculation at 7200 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7193: 0.032707, 0.057681 avg loss, 0.000261 rate, 0.593428 seconds, 460352 images, 0.155077 hours left\n",
            "Loaded: 0.000067 seconds\n",
            "\n",
            " (next mAP calculation at 7200 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7194: 0.038597, 0.055773 avg loss, 0.000261 rate, 0.600427 seconds, 460416 images, 0.154856 hours left\n",
            "Loaded: 0.000056 seconds\n",
            "\n",
            " (next mAP calculation at 7200 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7195: 0.049562, 0.055152 avg loss, 0.000261 rate, 0.586319 seconds, 460480 images, 0.154652 hours left\n",
            "Loaded: 0.000041 seconds\n",
            "\n",
            " (next mAP calculation at 7200 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7196: 0.099757, 0.059612 avg loss, 0.000261 rate, 0.642800 seconds, 460544 images, 0.154417 hours left\n",
            "Loaded: 0.000048 seconds\n",
            "\n",
            " (next mAP calculation at 7200 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7197: 0.065708, 0.060222 avg loss, 0.000261 rate, 0.650827 seconds, 460608 images, 0.154308 hours left\n",
            "Loaded: 0.000066 seconds\n",
            "\n",
            " (next mAP calculation at 7200 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7198: 0.028642, 0.057064 avg loss, 0.000261 rate, 0.607434 seconds, 460672 images, 0.154217 hours left\n",
            "Loaded: 0.000042 seconds\n",
            "\n",
            " (next mAP calculation at 7200 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7199: 0.049895, 0.056347 avg loss, 0.000261 rate, 0.621012 seconds, 460736 images, 0.154028 hours left\n",
            "Loaded: 0.000044 seconds\n",
            "\n",
            " (next mAP calculation at 7200 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7200: 0.044944, 0.055207 avg loss, 0.000026 rate, 0.633338 seconds, 460800 images, 0.153870 hours left\n",
            "\n",
            " calculation mAP (mean average precision)...\n",
            "8\n",
            " detections_count = 19, unique_truth_count = 8  \n",
            "class_id = 0, name = gundhi, ap = 0.00%   \t (TP = 0, FP = 1) \n",
            "class_id = 1, name = hispaa_insect, ap = 100.00%   \t (TP = 3, FP = 0) \n",
            "class_id = 2, name = stem_bor_lar, ap = 75.00%   \t (TP = 3, FP = 1) \n",
            "class_id = 3, name = stem_bor_moth, ap = 50.00%   \t (TP = 2, FP = 1) \n",
            "\n",
            " for conf_thresh = 0.25, precision = 0.73, recall = 1.00, F1-score = 0.84 \n",
            " for conf_thresh = 0.25, TP = 8, FP = 3, FN = 0, average IoU = 52.26 % \n",
            "\n",
            " IoU threshold = 50 %, used Area-Under-Curve for each unique Recall \n",
            " mean average precision (mAP@0.50) = 0.562500, or 56.25 % \n",
            "Total Detection Time: 0 Seconds\n",
            "\n",
            "Set -points flag:\n",
            " `-points 101` for MS COCO \n",
            " `-points 11` for PascalVOC 2007 (uncomment `difficult` in voc.data) \n",
            " `-points 0` (AUC) for ImageNet, PascalVOC 2010-2012, your custom dataset\n",
            "\n",
            " mean_average_precision (mAP@0.5) = 0.562500 \n",
            "Saving weights to backup//custom-yolov4-tiny-detector_last.weights\n",
            "Loaded: 0.000058 seconds\n",
            "\n",
            " (next mAP calculation at 7300 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7201: 0.018019, 0.051488 avg loss, 0.000026 rate, 0.652213 seconds, 460864 images, 0.153824 hours left\n",
            "Loaded: 0.000088 seconds\n",
            "\n",
            " (next mAP calculation at 7300 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7202: 0.045482, 0.050887 avg loss, 0.000026 rate, 0.630964 seconds, 460928 images, 0.153733 hours left\n",
            "Loaded: 0.000044 seconds\n",
            "\n",
            " (next mAP calculation at 7300 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7203: 0.044824, 0.050281 avg loss, 0.000026 rate, 0.641165 seconds, 460992 images, 0.153595 hours left\n",
            "Loaded: 0.000044 seconds\n",
            "\n",
            " (next mAP calculation at 7300 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7204: 0.038973, 0.049150 avg loss, 0.000026 rate, 0.610626 seconds, 461056 images, 0.153479 hours left\n",
            "Loaded: 0.000061 seconds\n",
            "\n",
            " (next mAP calculation at 7300 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7205: 0.048912, 0.049126 avg loss, 0.000026 rate, 0.643572 seconds, 461120 images, 0.153294 hours left\n",
            "Loaded: 0.000043 seconds\n",
            "\n",
            " (next mAP calculation at 7300 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7206: 0.056423, 0.049856 avg loss, 0.000026 rate, 0.588995 seconds, 461184 images, 0.153183 hours left\n",
            "Loaded: 0.000042 seconds\n",
            "\n",
            " (next mAP calculation at 7300 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7207: 0.111781, 0.056049 avg loss, 0.000026 rate, 0.600653 seconds, 461248 images, 0.152950 hours left\n",
            "Loaded: 0.000043 seconds\n",
            "\n",
            " (next mAP calculation at 7300 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7208: 0.035612, 0.054005 avg loss, 0.000026 rate, 0.582806 seconds, 461312 images, 0.152744 hours left\n",
            "Loaded: 0.000076 seconds\n",
            "\n",
            " (next mAP calculation at 7300 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7209: 0.026355, 0.051240 avg loss, 0.000026 rate, 0.582189 seconds, 461376 images, 0.152498 hours left\n",
            "Loaded: 0.000043 seconds\n",
            "\n",
            " (next mAP calculation at 7300 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7210: 0.043286, 0.050444 avg loss, 0.000026 rate, 0.597247 seconds, 461440 images, 0.152253 hours left\n",
            "Loaded: 0.000044 seconds\n",
            "\n",
            " (next mAP calculation at 7300 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7211: 0.058635, 0.051263 avg loss, 0.000026 rate, 0.577814 seconds, 461504 images, 0.152041 hours left\n",
            "Loaded: 0.000034 seconds\n",
            "\n",
            " (next mAP calculation at 7300 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7212: 0.086036, 0.054741 avg loss, 0.000026 rate, 0.608766 seconds, 461568 images, 0.151787 hours left\n",
            "Loaded: 0.000067 seconds\n",
            "\n",
            " (next mAP calculation at 7300 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7213: 0.088633, 0.058130 avg loss, 0.000026 rate, 0.600307 seconds, 461632 images, 0.151602 hours left\n",
            "Loaded: 0.000041 seconds\n",
            "\n",
            " (next mAP calculation at 7300 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7214: 0.037089, 0.056026 avg loss, 0.000026 rate, 0.611400 seconds, 461696 images, 0.151398 hours left\n",
            "Loaded: 0.000046 seconds\n",
            "\n",
            " (next mAP calculation at 7300 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7215: 0.078256, 0.058249 avg loss, 0.000026 rate, 0.638232 seconds, 461760 images, 0.151219 hours left\n",
            "Loaded: 0.000044 seconds\n",
            "\n",
            " (next mAP calculation at 7300 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7216: 0.065152, 0.058939 avg loss, 0.000026 rate, 0.638034 seconds, 461824 images, 0.151099 hours left\n",
            "Loaded: 0.000043 seconds\n",
            "\n",
            " (next mAP calculation at 7300 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7217: 0.022551, 0.055300 avg loss, 0.000026 rate, 0.617185 seconds, 461888 images, 0.150978 hours left\n",
            "Loaded: 0.000046 seconds\n",
            "\n",
            " (next mAP calculation at 7300 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7218: 0.046439, 0.054414 avg loss, 0.000026 rate, 0.634876 seconds, 461952 images, 0.150810 hours left\n",
            "Loaded: 0.000044 seconds\n",
            "\n",
            " (next mAP calculation at 7300 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7219: 0.054957, 0.054469 avg loss, 0.000026 rate, 0.588034 seconds, 462016 images, 0.150682 hours left\n",
            "Loaded: 0.000049 seconds\n",
            "\n",
            " (next mAP calculation at 7300 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7220: 0.042562, 0.053278 avg loss, 0.000026 rate, 0.662648 seconds, 462080 images, 0.150451 hours left\n",
            "Loaded: 0.000047 seconds\n",
            "\n",
            " (next mAP calculation at 7300 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7221: 0.070565, 0.055007 avg loss, 0.000026 rate, 0.616095 seconds, 462144 images, 0.150382 hours left\n",
            "Loaded: 0.000060 seconds\n",
            "\n",
            " (next mAP calculation at 7300 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7222: 0.037299, 0.053236 avg loss, 0.000026 rate, 0.620803 seconds, 462208 images, 0.150211 hours left\n",
            "Loaded: 0.000075 seconds\n",
            "\n",
            " (next mAP calculation at 7300 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7223: 0.050463, 0.052959 avg loss, 0.000026 rate, 0.631187 seconds, 462272 images, 0.150051 hours left\n",
            "Loaded: 0.000049 seconds\n",
            "\n",
            " (next mAP calculation at 7300 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7224: 0.050669, 0.052730 avg loss, 0.000026 rate, 0.666899 seconds, 462336 images, 0.149913 hours left\n",
            "Loaded: 0.000042 seconds\n",
            "\n",
            " (next mAP calculation at 7300 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7225: 0.064700, 0.053927 avg loss, 0.000026 rate, 0.608808 seconds, 462400 images, 0.149852 hours left\n",
            "Loaded: 0.000068 seconds\n",
            "\n",
            " (next mAP calculation at 7300 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7226: 0.035360, 0.052070 avg loss, 0.000026 rate, 0.599634 seconds, 462464 images, 0.149664 hours left\n",
            "Loaded: 0.000041 seconds\n",
            "\n",
            " (next mAP calculation at 7300 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7227: 0.051439, 0.052007 avg loss, 0.000026 rate, 0.559798 seconds, 462528 images, 0.149457 hours left\n",
            "Loaded: 0.000041 seconds\n",
            "\n",
            " (next mAP calculation at 7300 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7228: 0.085788, 0.055385 avg loss, 0.000026 rate, 0.619537 seconds, 462592 images, 0.149164 hours left\n",
            "Loaded: 0.000051 seconds\n",
            "\n",
            " (next mAP calculation at 7300 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7229: 0.057396, 0.055586 avg loss, 0.000026 rate, 0.628367 seconds, 462656 images, 0.149001 hours left\n",
            "Loaded: 0.000066 seconds\n",
            "\n",
            " (next mAP calculation at 7300 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7230: 0.047296, 0.054757 avg loss, 0.000026 rate, 0.674393 seconds, 462720 images, 0.148857 hours left\n",
            "Loaded: 0.000074 seconds\n",
            "\n",
            " (next mAP calculation at 7300 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7231: 0.047621, 0.054044 avg loss, 0.000026 rate, 0.630310 seconds, 462784 images, 0.148811 hours left\n",
            "Loaded: 0.000050 seconds\n",
            "\n",
            " (next mAP calculation at 7300 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7232: 0.030034, 0.051643 avg loss, 0.000026 rate, 0.657603 seconds, 462848 images, 0.148670 hours left\n",
            "Loaded: 0.000079 seconds\n",
            "\n",
            " (next mAP calculation at 7300 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7233: 0.041841, 0.050662 avg loss, 0.000026 rate, 0.604632 seconds, 462912 images, 0.148586 hours left\n",
            "Loaded: 0.000041 seconds\n",
            "\n",
            " (next mAP calculation at 7300 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7234: 0.008645, 0.046461 avg loss, 0.000026 rate, 0.598511 seconds, 462976 images, 0.148389 hours left\n",
            "Loaded: 0.000078 seconds\n",
            "\n",
            " (next mAP calculation at 7300 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7235: 0.062698, 0.048084 avg loss, 0.000026 rate, 0.606761 seconds, 463040 images, 0.148178 hours left\n",
            "Loaded: 0.000108 seconds\n",
            "\n",
            " (next mAP calculation at 7300 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7236: 0.079630, 0.051239 avg loss, 0.000026 rate, 0.636275 seconds, 463104 images, 0.147986 hours left\n",
            "Loaded: 0.000067 seconds\n",
            "\n",
            " (next mAP calculation at 7300 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7237: 0.040163, 0.050131 avg loss, 0.000026 rate, 0.596344 seconds, 463168 images, 0.147857 hours left\n",
            "Loaded: 0.000050 seconds\n",
            "\n",
            " (next mAP calculation at 7300 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7238: 0.064724, 0.051591 avg loss, 0.000026 rate, 0.622565 seconds, 463232 images, 0.147642 hours left\n",
            "Loaded: 0.000047 seconds\n",
            "\n",
            " (next mAP calculation at 7300 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7239: 0.055970, 0.052029 avg loss, 0.000026 rate, 0.641962 seconds, 463296 images, 0.147484 hours left\n",
            "Loaded: 0.000051 seconds\n",
            "\n",
            " (next mAP calculation at 7300 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7240: 0.045541, 0.051380 avg loss, 0.000026 rate, 0.657432 seconds, 463360 images, 0.147366 hours left\n",
            "Loaded: 0.000077 seconds\n",
            "\n",
            " (next mAP calculation at 7300 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7241: 0.057100, 0.051952 avg loss, 0.000026 rate, 0.605379 seconds, 463424 images, 0.147280 hours left\n",
            "Loaded: 0.000041 seconds\n",
            "\n",
            " (next mAP calculation at 7300 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7242: 0.034971, 0.050254 avg loss, 0.000026 rate, 0.599522 seconds, 463488 images, 0.147084 hours left\n",
            "Loaded: 0.000041 seconds\n",
            "\n",
            " (next mAP calculation at 7300 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7243: 0.107157, 0.055944 avg loss, 0.000026 rate, 0.597027 seconds, 463552 images, 0.146876 hours left\n",
            "Loaded: 0.000045 seconds\n",
            "\n",
            " (next mAP calculation at 7300 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7244: 0.038564, 0.054206 avg loss, 0.000026 rate, 0.706946 seconds, 463616 images, 0.146663 hours left\n",
            "Loaded: 0.000045 seconds\n",
            "\n",
            " (next mAP calculation at 7300 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7245: 0.024647, 0.051250 avg loss, 0.000026 rate, 0.622445 seconds, 463680 images, 0.146681 hours left\n",
            "Loaded: 0.000043 seconds\n",
            "\n",
            " (next mAP calculation at 7300 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7246: 0.079814, 0.054107 avg loss, 0.000026 rate, 0.617127 seconds, 463744 images, 0.146519 hours left\n",
            "Loaded: 0.000045 seconds\n",
            "\n",
            " (next mAP calculation at 7300 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7247: 0.077385, 0.056434 avg loss, 0.000026 rate, 0.647432 seconds, 463808 images, 0.146347 hours left\n",
            "Loaded: 0.000058 seconds\n",
            "\n",
            " (next mAP calculation at 7300 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7248: 0.046246, 0.055416 avg loss, 0.000026 rate, 0.603531 seconds, 463872 images, 0.146238 hours left\n",
            "Loaded: 0.000069 seconds\n",
            "\n",
            " (next mAP calculation at 7300 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7249: 0.042654, 0.054139 avg loss, 0.000026 rate, 0.632108 seconds, 463936 images, 0.146036 hours left\n",
            "Loaded: 0.000045 seconds\n",
            "\n",
            " (next mAP calculation at 7300 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7250: 0.059094, 0.054635 avg loss, 0.000026 rate, 0.624623 seconds, 464000 images, 0.145895 hours left\n",
            "Loaded: 0.000044 seconds\n",
            "\n",
            " (next mAP calculation at 7300 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7251: 0.052856, 0.054457 avg loss, 0.000026 rate, 0.631288 seconds, 464064 images, 0.145737 hours left\n",
            "Loaded: 0.000040 seconds\n",
            "\n",
            " (next mAP calculation at 7300 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7252: 0.031950, 0.052206 avg loss, 0.000026 rate, 0.608152 seconds, 464128 images, 0.145593 hours left\n",
            "Loaded: 0.000045 seconds\n",
            "\n",
            " (next mAP calculation at 7300 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7253: 0.043013, 0.051287 avg loss, 0.000026 rate, 0.615035 seconds, 464192 images, 0.145401 hours left\n",
            "Loaded: 0.000043 seconds\n",
            "\n",
            " (next mAP calculation at 7300 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7254: 0.036127, 0.049771 avg loss, 0.000026 rate, 0.639779 seconds, 464256 images, 0.145223 hours left\n",
            "Loaded: 0.000095 seconds\n",
            "\n",
            " (next mAP calculation at 7300 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7255: 0.047348, 0.049529 avg loss, 0.000026 rate, 0.582995 seconds, 464320 images, 0.145097 hours left\n",
            "Loaded: 0.000043 seconds\n",
            "\n",
            " (next mAP calculation at 7300 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7256: 0.077974, 0.052373 avg loss, 0.000026 rate, 0.585182 seconds, 464384 images, 0.144853 hours left\n",
            "Loaded: 0.000049 seconds\n",
            "\n",
            " (next mAP calculation at 7300 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7257: 0.062837, 0.053420 avg loss, 0.000026 rate, 0.612890 seconds, 464448 images, 0.144614 hours left\n",
            "Loaded: 0.000050 seconds\n",
            "\n",
            " (next mAP calculation at 7300 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7258: 0.028032, 0.050881 avg loss, 0.000026 rate, 0.630460 seconds, 464512 images, 0.144433 hours left\n",
            "Loaded: 0.000047 seconds\n",
            "\n",
            " (next mAP calculation at 7300 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7259: 0.064831, 0.052276 avg loss, 0.000026 rate, 0.673501 seconds, 464576 images, 0.144288 hours left\n",
            "Loaded: 0.000043 seconds\n",
            "\n",
            " (next mAP calculation at 7300 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7260: 0.051295, 0.052178 avg loss, 0.000026 rate, 0.632380 seconds, 464640 images, 0.144231 hours left\n",
            "Loaded: 0.000065 seconds\n",
            "\n",
            " (next mAP calculation at 7300 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7261: 0.039739, 0.050934 avg loss, 0.000026 rate, 0.653001 seconds, 464704 images, 0.144089 hours left\n",
            "Loaded: 0.000077 seconds\n",
            "\n",
            " (next mAP calculation at 7300 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7262: 0.084056, 0.054246 avg loss, 0.000026 rate, 0.613723 seconds, 464768 images, 0.143989 hours left\n",
            "Loaded: 0.000043 seconds\n",
            "\n",
            " (next mAP calculation at 7300 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7263: 0.036909, 0.052512 avg loss, 0.000026 rate, 0.600420 seconds, 464832 images, 0.143807 hours left\n",
            "Loaded: 0.000034 seconds\n",
            "\n",
            " (next mAP calculation at 7300 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7264: 0.036191, 0.050880 avg loss, 0.000026 rate, 0.617361 seconds, 464896 images, 0.143598 hours left\n",
            "Loaded: 0.000044 seconds\n",
            "\n",
            " (next mAP calculation at 7300 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7265: 0.034806, 0.049273 avg loss, 0.000026 rate, 0.605577 seconds, 464960 images, 0.143425 hours left\n",
            "Loaded: 0.000043 seconds\n",
            "\n",
            " (next mAP calculation at 7300 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7266: 0.051587, 0.049504 avg loss, 0.000026 rate, 0.622941 seconds, 465024 images, 0.143227 hours left\n",
            "Loaded: 0.000065 seconds\n",
            "\n",
            " (next mAP calculation at 7300 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7267: 0.036696, 0.048223 avg loss, 0.000026 rate, 0.637074 seconds, 465088 images, 0.143065 hours left\n",
            "Loaded: 0.000050 seconds\n",
            "\n",
            " (next mAP calculation at 7300 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7268: 0.047146, 0.048116 avg loss, 0.000026 rate, 0.650579 seconds, 465152 images, 0.142932 hours left\n",
            "Loaded: 0.000042 seconds\n",
            "\n",
            " (next mAP calculation at 7300 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7269: 0.038336, 0.047138 avg loss, 0.000026 rate, 0.598581 seconds, 465216 images, 0.142825 hours left\n",
            "Loaded: 0.000070 seconds\n",
            "\n",
            " (next mAP calculation at 7300 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7270: 0.084499, 0.050874 avg loss, 0.000026 rate, 0.641351 seconds, 465280 images, 0.142613 hours left\n",
            "Loaded: 0.000043 seconds\n",
            "\n",
            " (next mAP calculation at 7300 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7271: 0.016867, 0.047473 avg loss, 0.000026 rate, 0.565589 seconds, 465344 images, 0.142487 hours left\n",
            "Loaded: 0.000055 seconds\n",
            "\n",
            " (next mAP calculation at 7300 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7272: 0.037434, 0.046469 avg loss, 0.000026 rate, 0.619138 seconds, 465408 images, 0.142208 hours left\n",
            "Loaded: 0.000034 seconds\n",
            "\n",
            " (next mAP calculation at 7300 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7273: 0.057292, 0.047551 avg loss, 0.000026 rate, 0.629422 seconds, 465472 images, 0.142038 hours left\n",
            "Loaded: 0.000074 seconds\n",
            "\n",
            " (next mAP calculation at 7300 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7274: 0.065178, 0.049314 avg loss, 0.000026 rate, 0.678640 seconds, 465536 images, 0.141889 hours left\n",
            "Loaded: 0.000045 seconds\n",
            "\n",
            " (next mAP calculation at 7300 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7275: 0.080413, 0.052424 avg loss, 0.000026 rate, 0.636693 seconds, 465600 images, 0.141839 hours left\n",
            "Loaded: 0.000051 seconds\n",
            "\n",
            " (next mAP calculation at 7300 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7276: 0.085311, 0.055713 avg loss, 0.000026 rate, 0.666454 seconds, 465664 images, 0.141702 hours left\n",
            "Loaded: 0.000045 seconds\n",
            "\n",
            " (next mAP calculation at 7300 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7277: 0.041459, 0.054287 avg loss, 0.000026 rate, 0.597710 seconds, 465728 images, 0.141626 hours left\n",
            "Loaded: 0.000127 seconds\n",
            "\n",
            " (next mAP calculation at 7300 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7278: 0.089429, 0.057801 avg loss, 0.000026 rate, 0.635880 seconds, 465792 images, 0.141410 hours left\n",
            "Loaded: 0.000045 seconds\n",
            "\n",
            " (next mAP calculation at 7300 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7279: 0.052645, 0.057286 avg loss, 0.000026 rate, 0.626964 seconds, 465856 images, 0.141272 hours left\n",
            "Loaded: 0.000052 seconds\n",
            "\n",
            " (next mAP calculation at 7300 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7280: 0.061328, 0.057690 avg loss, 0.000026 rate, 0.621451 seconds, 465920 images, 0.141115 hours left\n",
            "Loaded: 0.000048 seconds\n",
            "\n",
            " (next mAP calculation at 7300 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7281: 0.051296, 0.057051 avg loss, 0.000026 rate, 0.617322 seconds, 465984 images, 0.140947 hours left\n",
            "Loaded: 0.000042 seconds\n",
            "\n",
            " (next mAP calculation at 7300 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7282: 0.033266, 0.054672 avg loss, 0.000026 rate, 0.616703 seconds, 466048 images, 0.140770 hours left\n",
            "Loaded: 0.000043 seconds\n",
            "\n",
            " (next mAP calculation at 7300 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7283: 0.017579, 0.050963 avg loss, 0.000026 rate, 0.598577 seconds, 466112 images, 0.140593 hours left\n",
            "Loaded: 0.000044 seconds\n",
            "\n",
            " (next mAP calculation at 7300 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7284: 0.073272, 0.053194 avg loss, 0.000026 rate, 0.625181 seconds, 466176 images, 0.140379 hours left\n",
            "Loaded: 0.000042 seconds\n",
            "\n",
            " (next mAP calculation at 7300 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7285: 0.068794, 0.054754 avg loss, 0.000026 rate, 0.582128 seconds, 466240 images, 0.140219 hours left\n",
            "Loaded: 0.000042 seconds\n",
            "\n",
            " (next mAP calculation at 7300 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7286: 0.018693, 0.051148 avg loss, 0.000026 rate, 0.582139 seconds, 466304 images, 0.139973 hours left\n",
            "Loaded: 0.000037 seconds\n",
            "\n",
            " (next mAP calculation at 7300 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7287: 0.078660, 0.053899 avg loss, 0.000026 rate, 0.611979 seconds, 466368 images, 0.139728 hours left\n",
            "Loaded: 0.000047 seconds\n",
            "\n",
            " (next mAP calculation at 7300 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7288: 0.056302, 0.054139 avg loss, 0.000026 rate, 0.596694 seconds, 466432 images, 0.139543 hours left\n",
            "Loaded: 0.000049 seconds\n",
            "\n",
            " (next mAP calculation at 7300 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7289: 0.084687, 0.057194 avg loss, 0.000026 rate, 0.632744 seconds, 466496 images, 0.139327 hours left\n",
            "Loaded: 0.000043 seconds\n",
            "\n",
            " (next mAP calculation at 7300 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7290: 0.063717, 0.057846 avg loss, 0.000026 rate, 0.633174 seconds, 466560 images, 0.139184 hours left\n",
            "Loaded: 0.000047 seconds\n",
            "\n",
            " (next mAP calculation at 7300 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7291: 0.066183, 0.058680 avg loss, 0.000026 rate, 0.631185 seconds, 466624 images, 0.139041 hours left\n",
            "Loaded: 0.000042 seconds\n",
            "\n",
            " (next mAP calculation at 7300 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7292: 0.017513, 0.054563 avg loss, 0.000026 rate, 0.631120 seconds, 466688 images, 0.138894 hours left\n",
            "Loaded: 0.000066 seconds\n",
            "\n",
            " (next mAP calculation at 7300 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7293: 0.034617, 0.052569 avg loss, 0.000026 rate, 0.589527 seconds, 466752 images, 0.138746 hours left\n",
            "Loaded: 0.000042 seconds\n",
            "\n",
            " (next mAP calculation at 7300 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7294: 0.052565, 0.052568 avg loss, 0.000026 rate, 0.629449 seconds, 466816 images, 0.138517 hours left\n",
            "Loaded: 0.000044 seconds\n",
            "\n",
            " (next mAP calculation at 7300 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7295: 0.086663, 0.055978 avg loss, 0.000026 rate, 0.636470 seconds, 466880 images, 0.138366 hours left\n",
            "Loaded: 0.000067 seconds\n",
            "\n",
            " (next mAP calculation at 7300 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7296: 0.060693, 0.056449 avg loss, 0.000026 rate, 0.603640 seconds, 466944 images, 0.138229 hours left\n",
            "Loaded: 0.000046 seconds\n",
            "\n",
            " (next mAP calculation at 7300 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7297: 0.072117, 0.058016 avg loss, 0.000026 rate, 0.625370 seconds, 467008 images, 0.138027 hours left\n",
            "Loaded: 0.000072 seconds\n",
            "\n",
            " (next mAP calculation at 7300 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7298: 0.042397, 0.056454 avg loss, 0.000026 rate, 0.616807 seconds, 467072 images, 0.137868 hours left\n",
            "Loaded: 0.000044 seconds\n",
            "\n",
            " (next mAP calculation at 7300 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7299: 0.029770, 0.053786 avg loss, 0.000026 rate, 0.592938 seconds, 467136 images, 0.137693 hours left\n",
            "Loaded: 0.000041 seconds\n",
            "\n",
            " (next mAP calculation at 7300 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7300: 0.058949, 0.054302 avg loss, 0.000026 rate, 0.632444 seconds, 467200 images, 0.137470 hours left\n",
            "\n",
            " calculation mAP (mean average precision)...\n",
            "8\n",
            " detections_count = 18, unique_truth_count = 8  \n",
            "class_id = 0, name = gundhi, ap = 0.00%   \t (TP = 0, FP = 1) \n",
            "class_id = 1, name = hispaa_insect, ap = 100.00%   \t (TP = 3, FP = 0) \n",
            "class_id = 2, name = stem_bor_lar, ap = 75.00%   \t (TP = 3, FP = 1) \n",
            "class_id = 3, name = stem_bor_moth, ap = 50.00%   \t (TP = 2, FP = 1) \n",
            "\n",
            " for conf_thresh = 0.25, precision = 0.73, recall = 1.00, F1-score = 0.84 \n",
            " for conf_thresh = 0.25, TP = 8, FP = 3, FN = 0, average IoU = 52.27 % \n",
            "\n",
            " IoU threshold = 50 %, used Area-Under-Curve for each unique Recall \n",
            " mean average precision (mAP@0.50) = 0.562500, or 56.25 % \n",
            "Total Detection Time: 0 Seconds\n",
            "\n",
            "Set -points flag:\n",
            " `-points 101` for MS COCO \n",
            " `-points 11` for PascalVOC 2007 (uncomment `difficult` in voc.data) \n",
            " `-points 0` (AUC) for ImageNet, PascalVOC 2010-2012, your custom dataset\n",
            "\n",
            " mean_average_precision (mAP@0.5) = 0.562500 \n",
            "Saving weights to backup//custom-yolov4-tiny-detector_last.weights\n",
            "Loaded: 0.000067 seconds\n",
            "\n",
            " (next mAP calculation at 7400 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7301: 0.031190, 0.051991 avg loss, 0.000026 rate, 0.657661 seconds, 467264 images, 0.137398 hours left\n",
            "Loaded: 0.000043 seconds\n",
            "\n",
            " (next mAP calculation at 7400 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7302: 0.102773, 0.057069 avg loss, 0.000026 rate, 0.609871 seconds, 467328 images, 0.137301 hours left\n",
            "Loaded: 0.000059 seconds\n",
            "\n",
            " (next mAP calculation at 7400 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7303: 0.067330, 0.058095 avg loss, 0.000026 rate, 0.643027 seconds, 467392 images, 0.137111 hours left\n",
            "Loaded: 0.000090 seconds\n",
            "\n",
            " (next mAP calculation at 7400 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7304: 0.036422, 0.055928 avg loss, 0.000026 rate, 0.605846 seconds, 467456 images, 0.136985 hours left\n",
            "Loaded: 0.000056 seconds\n",
            "\n",
            " (next mAP calculation at 7400 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7305: 0.059740, 0.056309 avg loss, 0.000026 rate, 0.634045 seconds, 467520 images, 0.136787 hours left\n",
            "Loaded: 0.000039 seconds\n",
            "\n",
            " (next mAP calculation at 7400 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7306: 0.061306, 0.056809 avg loss, 0.000026 rate, 0.599627 seconds, 467584 images, 0.136643 hours left\n",
            "Loaded: 0.000071 seconds\n",
            "\n",
            " (next mAP calculation at 7400 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7307: 0.055695, 0.056697 avg loss, 0.000026 rate, 0.626293 seconds, 467648 images, 0.136432 hours left\n",
            "Loaded: 0.000061 seconds\n",
            "\n",
            " (next mAP calculation at 7400 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7308: 0.045928, 0.055621 avg loss, 0.000026 rate, 0.600569 seconds, 467712 images, 0.136274 hours left\n",
            "Loaded: 0.000043 seconds\n",
            "\n",
            " (next mAP calculation at 7400 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7309: 0.036060, 0.053664 avg loss, 0.000026 rate, 0.608915 seconds, 467776 images, 0.136066 hours left\n",
            "Loaded: 0.000064 seconds\n",
            "\n",
            " (next mAP calculation at 7400 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7310: 0.051196, 0.053418 avg loss, 0.000026 rate, 0.629189 seconds, 467840 images, 0.135874 hours left\n",
            "Loaded: 0.000047 seconds\n",
            "\n",
            " (next mAP calculation at 7400 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7311: 0.058410, 0.053917 avg loss, 0.000026 rate, 0.615143 seconds, 467904 images, 0.135721 hours left\n",
            "Loaded: 0.000045 seconds\n",
            "\n",
            " (next mAP calculation at 7400 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7312: 0.055881, 0.054113 avg loss, 0.000026 rate, 0.604786 seconds, 467968 images, 0.135542 hours left\n",
            "Loaded: 0.000058 seconds\n",
            "\n",
            " (next mAP calculation at 7400 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7313: 0.052012, 0.053903 avg loss, 0.000026 rate, 0.577366 seconds, 468032 images, 0.135342 hours left\n",
            "Loaded: 0.000039 seconds\n",
            "\n",
            " (next mAP calculation at 7400 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7314: 0.059426, 0.054455 avg loss, 0.000026 rate, 0.609642 seconds, 468096 images, 0.135091 hours left\n",
            "Loaded: 0.000044 seconds\n",
            "\n",
            " (next mAP calculation at 7400 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7315: 0.051900, 0.054200 avg loss, 0.000026 rate, 0.612313 seconds, 468160 images, 0.134902 hours left\n",
            "Loaded: 0.000036 seconds\n",
            "\n",
            " (next mAP calculation at 7400 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7316: 0.062981, 0.055078 avg loss, 0.000026 rate, 0.588290 seconds, 468224 images, 0.134718 hours left\n",
            "Loaded: 0.000051 seconds\n",
            "\n",
            " (next mAP calculation at 7400 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7317: 0.044079, 0.053978 avg loss, 0.000026 rate, 0.639332 seconds, 468288 images, 0.134488 hours left\n",
            "Loaded: 0.000044 seconds\n",
            "\n",
            " (next mAP calculation at 7400 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7318: 0.056266, 0.054207 avg loss, 0.000026 rate, 0.617291 seconds, 468352 images, 0.134357 hours left\n",
            "Loaded: 0.000045 seconds\n",
            "\n",
            " (next mAP calculation at 7400 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7319: 0.037758, 0.052562 avg loss, 0.000026 rate, 0.623445 seconds, 468416 images, 0.134183 hours left\n",
            "Loaded: 0.000070 seconds\n",
            "\n",
            " (next mAP calculation at 7400 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7320: 0.059190, 0.053225 avg loss, 0.000026 rate, 0.641442 seconds, 468480 images, 0.134020 hours left\n",
            "Loaded: 0.000041 seconds\n",
            "\n",
            " (next mAP calculation at 7400 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7321: 0.028029, 0.050705 avg loss, 0.000026 rate, 0.614443 seconds, 468544 images, 0.133892 hours left\n",
            "Loaded: 0.000069 seconds\n",
            "\n",
            " (next mAP calculation at 7400 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7322: 0.070852, 0.052720 avg loss, 0.000026 rate, 0.611903 seconds, 468608 images, 0.133712 hours left\n",
            "Loaded: 0.000041 seconds\n",
            "\n",
            " (next mAP calculation at 7400 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7323: 0.037848, 0.051233 avg loss, 0.000026 rate, 0.579413 seconds, 468672 images, 0.133527 hours left\n",
            "Loaded: 0.000041 seconds\n",
            "\n",
            " (next mAP calculation at 7400 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7324: 0.047936, 0.050903 avg loss, 0.000026 rate, 0.629181 seconds, 468736 images, 0.133282 hours left\n",
            "Loaded: 0.000066 seconds\n",
            "\n",
            " (next mAP calculation at 7400 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7325: 0.067627, 0.052575 avg loss, 0.000026 rate, 0.611364 seconds, 468800 images, 0.133131 hours left\n",
            "Loaded: 0.000042 seconds\n",
            "\n",
            " (next mAP calculation at 7400 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7326: 0.021154, 0.049433 avg loss, 0.000026 rate, 0.596028 seconds, 468864 images, 0.132946 hours left\n",
            "Loaded: 0.000063 seconds\n",
            "\n",
            " (next mAP calculation at 7400 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7327: 0.027550, 0.047245 avg loss, 0.000026 rate, 0.620237 seconds, 468928 images, 0.132732 hours left\n",
            "Loaded: 0.000045 seconds\n",
            "\n",
            " (next mAP calculation at 7400 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7328: 0.070150, 0.049535 avg loss, 0.000026 rate, 0.598172 seconds, 468992 images, 0.132565 hours left\n",
            "Loaded: 0.000045 seconds\n",
            "\n",
            " (next mAP calculation at 7400 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7329: 0.060080, 0.050590 avg loss, 0.000026 rate, 0.602005 seconds, 469056 images, 0.132356 hours left\n",
            "Loaded: 0.000042 seconds\n",
            "\n",
            " (next mAP calculation at 7400 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7330: 0.075819, 0.053113 avg loss, 0.000026 rate, 0.546446 seconds, 469120 images, 0.132154 hours left\n",
            "Loaded: 0.000085 seconds\n",
            "\n",
            " (next mAP calculation at 7400 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7331: 0.021717, 0.049973 avg loss, 0.000026 rate, 0.637903 seconds, 469184 images, 0.131850 hours left\n",
            "Loaded: 0.000047 seconds\n",
            "\n",
            " (next mAP calculation at 7400 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7332: 0.097320, 0.054708 avg loss, 0.000026 rate, 0.604199 seconds, 469248 images, 0.131717 hours left\n",
            "Loaded: 0.000048 seconds\n",
            "\n",
            " (next mAP calculation at 7400 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7333: 0.035181, 0.052755 avg loss, 0.000026 rate, 0.620107 seconds, 469312 images, 0.131521 hours left\n",
            "Loaded: 0.000072 seconds\n",
            "\n",
            " (next mAP calculation at 7400 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7334: 0.039348, 0.051414 avg loss, 0.000026 rate, 0.599402 seconds, 469376 images, 0.131355 hours left\n",
            "Loaded: 0.000045 seconds\n",
            "\n",
            " (next mAP calculation at 7400 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7335: 0.036680, 0.049941 avg loss, 0.000026 rate, 0.614586 seconds, 469440 images, 0.131150 hours left\n",
            "Loaded: 0.000034 seconds\n",
            "\n",
            " (next mAP calculation at 7400 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7336: 0.045543, 0.049501 avg loss, 0.000026 rate, 0.624809 seconds, 469504 images, 0.130974 hours left\n",
            "Loaded: 0.000078 seconds\n",
            "\n",
            " (next mAP calculation at 7400 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7337: 0.042001, 0.048751 avg loss, 0.000026 rate, 0.619793 seconds, 469568 images, 0.130817 hours left\n",
            "Loaded: 0.000042 seconds\n",
            "\n",
            " (next mAP calculation at 7400 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7338: 0.094346, 0.053311 avg loss, 0.000026 rate, 0.560630 seconds, 469632 images, 0.130650 hours left\n",
            "Loaded: 0.000072 seconds\n",
            "\n",
            " (next mAP calculation at 7400 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7339: 0.075455, 0.055525 avg loss, 0.000026 rate, 0.636151 seconds, 469696 images, 0.130375 hours left\n",
            "Loaded: 0.000042 seconds\n",
            "\n",
            " (next mAP calculation at 7400 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7340: 0.079892, 0.057962 avg loss, 0.000026 rate, 0.580064 seconds, 469760 images, 0.130239 hours left\n",
            "Loaded: 0.000035 seconds\n",
            "\n",
            " (next mAP calculation at 7400 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7341: 0.047317, 0.056897 avg loss, 0.000026 rate, 0.573287 seconds, 469824 images, 0.130001 hours left\n",
            "Loaded: 0.000091 seconds\n",
            "\n",
            " (next mAP calculation at 7400 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7342: 0.059446, 0.057152 avg loss, 0.000026 rate, 0.624257 seconds, 469888 images, 0.129750 hours left\n",
            "Loaded: 0.000039 seconds\n",
            "\n",
            " (next mAP calculation at 7400 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7343: 0.030811, 0.054518 avg loss, 0.000026 rate, 0.634442 seconds, 469952 images, 0.129594 hours left\n",
            "Loaded: 0.000055 seconds\n",
            "\n",
            " (next mAP calculation at 7400 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7344: 0.022423, 0.051309 avg loss, 0.000026 rate, 0.610611 seconds, 470016 images, 0.129456 hours left\n",
            "Loaded: 0.000045 seconds\n",
            "\n",
            " (next mAP calculation at 7400 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7345: 0.078627, 0.054040 avg loss, 0.000026 rate, 0.621001 seconds, 470080 images, 0.129274 hours left\n",
            "Loaded: 0.000052 seconds\n",
            "\n",
            " (next mAP calculation at 7400 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7346: 0.039815, 0.052618 avg loss, 0.000026 rate, 0.604328 seconds, 470144 images, 0.129111 hours left\n",
            "Loaded: 0.000085 seconds\n",
            "\n",
            " (next mAP calculation at 7400 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7347: 0.020507, 0.049407 avg loss, 0.000026 rate, 0.662066 seconds, 470208 images, 0.128918 hours left\n",
            "Loaded: 0.000051 seconds\n",
            "\n",
            " (next mAP calculation at 7400 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7348: 0.077367, 0.052203 avg loss, 0.000026 rate, 0.634998 seconds, 470272 images, 0.128830 hours left\n",
            "Loaded: 0.000042 seconds\n",
            "\n",
            " (next mAP calculation at 7400 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7349: 0.051452, 0.052128 avg loss, 0.000026 rate, 0.577275 seconds, 470336 images, 0.128692 hours left\n",
            "Loaded: 0.000042 seconds\n",
            "\n",
            " (next mAP calculation at 7400 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7350: 0.121582, 0.059073 avg loss, 0.000026 rate, 0.652353 seconds, 470400 images, 0.128449 hours left\n",
            "Loaded: 0.000043 seconds\n",
            "\n",
            " (next mAP calculation at 7400 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7351: 0.048620, 0.058028 avg loss, 0.000026 rate, 0.598833 seconds, 470464 images, 0.128343 hours left\n",
            "Loaded: 0.000097 seconds\n",
            "\n",
            " (next mAP calculation at 7400 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7352: 0.068953, 0.059120 avg loss, 0.000026 rate, 0.632118 seconds, 470528 images, 0.128139 hours left\n",
            "Loaded: 0.000044 seconds\n",
            "\n",
            " (next mAP calculation at 7400 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7353: 0.069338, 0.060142 avg loss, 0.000026 rate, 0.623714 seconds, 470592 images, 0.127995 hours left\n",
            "Loaded: 0.000071 seconds\n",
            "\n",
            " (next mAP calculation at 7400 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7354: 0.047562, 0.058884 avg loss, 0.000026 rate, 0.603293 seconds, 470656 images, 0.127837 hours left\n",
            "Loaded: 0.000068 seconds\n",
            "\n",
            " (next mAP calculation at 7400 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7355: 0.092192, 0.062215 avg loss, 0.000026 rate, 0.620603 seconds, 470720 images, 0.127641 hours left\n",
            "Loaded: 0.000045 seconds\n",
            "\n",
            " (next mAP calculation at 7400 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7356: 0.035986, 0.059592 avg loss, 0.000026 rate, 0.628669 seconds, 470784 images, 0.127477 hours left\n",
            "Loaded: 0.000044 seconds\n",
            "\n",
            " (next mAP calculation at 7400 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7357: 0.043388, 0.057972 avg loss, 0.000026 rate, 0.616274 seconds, 470848 images, 0.127326 hours left\n",
            "Loaded: 0.000047 seconds\n",
            "\n",
            " (next mAP calculation at 7400 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7358: 0.042412, 0.056416 avg loss, 0.000026 rate, 0.648273 seconds, 470912 images, 0.127154 hours left\n",
            "Loaded: 0.000053 seconds\n",
            "\n",
            " (next mAP calculation at 7400 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7359: 0.051578, 0.055932 avg loss, 0.000026 rate, 0.612470 seconds, 470976 images, 0.127039 hours left\n",
            "Loaded: 0.000074 seconds\n",
            "\n",
            " (next mAP calculation at 7400 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7360: 0.045957, 0.054934 avg loss, 0.000026 rate, 0.584391 seconds, 471040 images, 0.126859 hours left\n",
            "Loaded: 0.000062 seconds\n",
            "\n",
            " (next mAP calculation at 7400 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7361: 0.045089, 0.053950 avg loss, 0.000026 rate, 0.621284 seconds, 471104 images, 0.126629 hours left\n",
            "Loaded: 0.000076 seconds\n",
            "\n",
            " (next mAP calculation at 7400 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7362: 0.053783, 0.053933 avg loss, 0.000026 rate, 0.630341 seconds, 471168 images, 0.126466 hours left\n",
            "Loaded: 0.000046 seconds\n",
            "\n",
            " (next mAP calculation at 7400 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7363: 0.059612, 0.054501 avg loss, 0.000026 rate, 0.640812 seconds, 471232 images, 0.126319 hours left\n",
            "Loaded: 0.000038 seconds\n",
            "\n",
            " (next mAP calculation at 7400 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7364: 0.062865, 0.055337 avg loss, 0.000026 rate, 0.633123 seconds, 471296 images, 0.126189 hours left\n",
            "Loaded: 0.000076 seconds\n",
            "\n",
            " (next mAP calculation at 7400 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7365: 0.059438, 0.055748 avg loss, 0.000026 rate, 0.602164 seconds, 471360 images, 0.126046 hours left\n",
            "Loaded: 0.000044 seconds\n",
            "\n",
            " (next mAP calculation at 7400 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7366: 0.048021, 0.054975 avg loss, 0.000026 rate, 0.644578 seconds, 471424 images, 0.125848 hours left\n",
            "Loaded: 0.000054 seconds\n",
            "\n",
            " (next mAP calculation at 7400 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7367: 0.046414, 0.054119 avg loss, 0.000026 rate, 0.619420 seconds, 471488 images, 0.125725 hours left\n",
            "Loaded: 0.000061 seconds\n",
            "\n",
            " (next mAP calculation at 7400 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7368: 0.075220, 0.056229 avg loss, 0.000026 rate, 0.602125 seconds, 471552 images, 0.125557 hours left\n",
            "Loaded: 0.000051 seconds\n",
            "\n",
            " (next mAP calculation at 7400 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7369: 0.092873, 0.059893 avg loss, 0.000026 rate, 0.607051 seconds, 471616 images, 0.125358 hours left\n",
            "Loaded: 0.000044 seconds\n",
            "\n",
            " (next mAP calculation at 7400 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7370: 0.038977, 0.057802 avg loss, 0.000026 rate, 0.609828 seconds, 471680 images, 0.125169 hours left\n",
            "Loaded: 0.000042 seconds\n",
            "\n",
            " (next mAP calculation at 7400 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7371: 0.053233, 0.057345 avg loss, 0.000026 rate, 0.572054 seconds, 471744 images, 0.124985 hours left\n",
            "Loaded: 0.000069 seconds\n",
            "\n",
            " (next mAP calculation at 7400 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7372: 0.061337, 0.057744 avg loss, 0.000026 rate, 0.581969 seconds, 471808 images, 0.124734 hours left\n",
            "Loaded: 0.000042 seconds\n",
            "\n",
            " (next mAP calculation at 7400 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7373: 0.047164, 0.056686 avg loss, 0.000026 rate, 0.601644 seconds, 471872 images, 0.124502 hours left\n",
            "Loaded: 0.000035 seconds\n",
            "\n",
            " (next mAP calculation at 7400 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7374: 0.070541, 0.058071 avg loss, 0.000026 rate, 0.585232 seconds, 471936 images, 0.124305 hours left\n",
            "Loaded: 0.000062 seconds\n",
            "\n",
            " (next mAP calculation at 7400 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7375: 0.047200, 0.056984 avg loss, 0.000026 rate, 0.632152 seconds, 472000 images, 0.124080 hours left\n",
            "Loaded: 0.000051 seconds\n",
            "\n",
            " (next mAP calculation at 7400 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7376: 0.045650, 0.055851 avg loss, 0.000026 rate, 0.637759 seconds, 472064 images, 0.123937 hours left\n",
            "Loaded: 0.000045 seconds\n",
            "\n",
            " (next mAP calculation at 7400 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7377: 0.058324, 0.056098 avg loss, 0.000026 rate, 0.656204 seconds, 472128 images, 0.123803 hours left\n",
            "Loaded: 0.000046 seconds\n",
            "\n",
            " (next mAP calculation at 7400 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7378: 0.077753, 0.058264 avg loss, 0.000026 rate, 0.611618 seconds, 472192 images, 0.123701 hours left\n",
            "Loaded: 0.000044 seconds\n",
            "\n",
            " (next mAP calculation at 7400 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7379: 0.049579, 0.057395 avg loss, 0.000026 rate, 0.644936 seconds, 472256 images, 0.123521 hours left\n",
            "Loaded: 0.000057 seconds\n",
            "\n",
            " (next mAP calculation at 7400 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7380: 0.040842, 0.055740 avg loss, 0.000026 rate, 0.576946 seconds, 472320 images, 0.123398 hours left\n",
            "Loaded: 0.000041 seconds\n",
            "\n",
            " (next mAP calculation at 7400 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7381: 0.064307, 0.056597 avg loss, 0.000026 rate, 0.623995 seconds, 472384 images, 0.123158 hours left\n",
            "Loaded: 0.000058 seconds\n",
            "\n",
            " (next mAP calculation at 7400 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7382: 0.056601, 0.056597 avg loss, 0.000026 rate, 0.632627 seconds, 472448 images, 0.122999 hours left\n",
            "Loaded: 0.000054 seconds\n",
            "\n",
            " (next mAP calculation at 7400 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7383: 0.072134, 0.058151 avg loss, 0.000026 rate, 0.599957 seconds, 472512 images, 0.122855 hours left\n",
            "Loaded: 0.000109 seconds\n",
            "\n",
            " (next mAP calculation at 7400 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7384: 0.084402, 0.060776 avg loss, 0.000026 rate, 0.603323 seconds, 472576 images, 0.122655 hours left\n",
            "Loaded: 0.000063 seconds\n",
            "\n",
            " (next mAP calculation at 7400 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7385: 0.052695, 0.059968 avg loss, 0.000026 rate, 0.616138 seconds, 472640 images, 0.122461 hours left\n",
            "Loaded: 0.000042 seconds\n",
            "\n",
            " (next mAP calculation at 7400 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7386: 0.024972, 0.056468 avg loss, 0.000026 rate, 0.589392 seconds, 472704 images, 0.122289 hours left\n",
            "Loaded: 0.000059 seconds\n",
            "\n",
            " (next mAP calculation at 7400 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7387: 0.031417, 0.053963 avg loss, 0.000026 rate, 0.617684 seconds, 472768 images, 0.122072 hours left\n",
            "Loaded: 0.000044 seconds\n",
            "\n",
            " (next mAP calculation at 7400 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7388: 0.037116, 0.052278 avg loss, 0.000026 rate, 0.598617 seconds, 472832 images, 0.121903 hours left\n",
            "Loaded: 0.000073 seconds\n",
            "\n",
            " (next mAP calculation at 7400 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7389: 0.027947, 0.049845 avg loss, 0.000026 rate, 0.612894 seconds, 472896 images, 0.121702 hours left\n",
            "Loaded: 0.000064 seconds\n",
            "\n",
            " (next mAP calculation at 7400 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7390: 0.024226, 0.047283 avg loss, 0.000026 rate, 0.577039 seconds, 472960 images, 0.121525 hours left\n",
            "Loaded: 0.000059 seconds\n",
            "\n",
            " (next mAP calculation at 7400 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7391: 0.078437, 0.050399 avg loss, 0.000026 rate, 0.598287 seconds, 473024 images, 0.121288 hours left\n",
            "Loaded: 0.000045 seconds\n",
            "\n",
            " (next mAP calculation at 7400 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7392: 0.040803, 0.049439 avg loss, 0.000026 rate, 0.652680 seconds, 473088 images, 0.121087 hours left\n",
            "Loaded: 0.000050 seconds\n",
            "\n",
            " (next mAP calculation at 7400 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7393: 0.088922, 0.053388 avg loss, 0.000026 rate, 0.593908 seconds, 473152 images, 0.120978 hours left\n",
            "Loaded: 0.000045 seconds\n",
            "\n",
            " (next mAP calculation at 7400 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7394: 0.043804, 0.052429 avg loss, 0.000026 rate, 0.649385 seconds, 473216 images, 0.120770 hours left\n",
            "Loaded: 0.000067 seconds\n",
            "\n",
            " (next mAP calculation at 7400 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7395: 0.059185, 0.053105 avg loss, 0.000026 rate, 0.596641 seconds, 473280 images, 0.120656 hours left\n",
            "Loaded: 0.000040 seconds\n",
            "\n",
            " (next mAP calculation at 7400 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7396: 0.062776, 0.054072 avg loss, 0.000026 rate, 0.622154 seconds, 473344 images, 0.120452 hours left\n",
            "Loaded: 0.000048 seconds\n",
            "\n",
            " (next mAP calculation at 7400 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7397: 0.075058, 0.056170 avg loss, 0.000026 rate, 0.610649 seconds, 473408 images, 0.120291 hours left\n",
            "Loaded: 0.000059 seconds\n",
            "\n",
            " (next mAP calculation at 7400 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7398: 0.047778, 0.055331 avg loss, 0.000026 rate, 0.626038 seconds, 473472 images, 0.120111 hours left\n",
            "Loaded: 0.000058 seconds\n",
            "\n",
            " (next mAP calculation at 7400 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7399: 0.043145, 0.054113 avg loss, 0.000026 rate, 0.610366 seconds, 473536 images, 0.119957 hours left\n",
            "Loaded: 0.000044 seconds\n",
            "\n",
            " (next mAP calculation at 7400 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7400: 0.060702, 0.054772 avg loss, 0.000026 rate, 0.616345 seconds, 473600 images, 0.119777 hours left\n",
            "\n",
            " calculation mAP (mean average precision)...\n",
            "8\n",
            " detections_count = 19, unique_truth_count = 8  \n",
            "class_id = 0, name = gundhi, ap = 0.00%   \t (TP = 0, FP = 1) \n",
            "class_id = 1, name = hispaa_insect, ap = 100.00%   \t (TP = 3, FP = 0) \n",
            "class_id = 2, name = stem_bor_lar, ap = 75.00%   \t (TP = 3, FP = 1) \n",
            "class_id = 3, name = stem_bor_moth, ap = 50.00%   \t (TP = 2, FP = 1) \n",
            "\n",
            " for conf_thresh = 0.25, precision = 0.73, recall = 1.00, F1-score = 0.84 \n",
            " for conf_thresh = 0.25, TP = 8, FP = 3, FN = 0, average IoU = 52.34 % \n",
            "\n",
            " IoU threshold = 50 %, used Area-Under-Curve for each unique Recall \n",
            " mean average precision (mAP@0.50) = 0.562500, or 56.25 % \n",
            "Total Detection Time: 1 Seconds\n",
            "\n",
            "Set -points flag:\n",
            " `-points 101` for MS COCO \n",
            " `-points 11` for PascalVOC 2007 (uncomment `difficult` in voc.data) \n",
            " `-points 0` (AUC) for ImageNet, PascalVOC 2010-2012, your custom dataset\n",
            "\n",
            " mean_average_precision (mAP@0.5) = 0.562500 \n",
            "Saving weights to backup//custom-yolov4-tiny-detector_last.weights\n",
            "Loaded: 0.000075 seconds\n",
            "\n",
            " (next mAP calculation at 7500 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7401: 0.033595, 0.052654 avg loss, 0.000026 rate, 0.667624 seconds, 473664 images, 0.119669 hours left\n",
            "Loaded: 0.000048 seconds\n",
            "\n",
            " (next mAP calculation at 7500 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7402: 0.055268, 0.052915 avg loss, 0.000026 rate, 0.579030 seconds, 473728 images, 0.119584 hours left\n",
            "Loaded: 0.000039 seconds\n",
            "\n",
            " (next mAP calculation at 7500 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7403: 0.041589, 0.051783 avg loss, 0.000026 rate, 0.590817 seconds, 473792 images, 0.119350 hours left\n",
            "Loaded: 0.000057 seconds\n",
            "\n",
            " (next mAP calculation at 7500 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7404: 0.044974, 0.051102 avg loss, 0.000026 rate, 0.585719 seconds, 473856 images, 0.119136 hours left\n",
            "Loaded: 0.000053 seconds\n",
            "\n",
            " (next mAP calculation at 7500 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7405: 0.040372, 0.050029 avg loss, 0.000026 rate, 0.656191 seconds, 473920 images, 0.118915 hours left\n",
            "Loaded: 0.000043 seconds\n",
            "\n",
            " (next mAP calculation at 7500 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7406: 0.017872, 0.046813 avg loss, 0.000026 rate, 0.626470 seconds, 473984 images, 0.118810 hours left\n",
            "Loaded: 0.000042 seconds\n",
            "\n",
            " (next mAP calculation at 7500 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7407: 0.048497, 0.046982 avg loss, 0.000026 rate, 0.598065 seconds, 474048 images, 0.118656 hours left\n",
            "Loaded: 0.000054 seconds\n",
            "\n",
            " (next mAP calculation at 7500 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7408: 0.034930, 0.045776 avg loss, 0.000026 rate, 0.660825 seconds, 474112 images, 0.118455 hours left\n",
            "Loaded: 0.000069 seconds\n",
            "\n",
            " (next mAP calculation at 7500 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7409: 0.065066, 0.047705 avg loss, 0.000026 rate, 0.577066 seconds, 474176 images, 0.118357 hours left\n",
            "Loaded: 0.000061 seconds\n",
            "\n",
            " (next mAP calculation at 7500 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7410: 0.045378, 0.047473 avg loss, 0.000026 rate, 0.606413 seconds, 474240 images, 0.118121 hours left\n",
            "Loaded: 0.000069 seconds\n",
            "\n",
            " (next mAP calculation at 7500 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7411: 0.052837, 0.048009 avg loss, 0.000026 rate, 0.643848 seconds, 474304 images, 0.117933 hours left\n",
            "Loaded: 0.000044 seconds\n",
            "\n",
            " (next mAP calculation at 7500 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7412: 0.057500, 0.048958 avg loss, 0.000026 rate, 0.638703 seconds, 474368 images, 0.117808 hours left\n",
            "Loaded: 0.000096 seconds\n",
            "\n",
            " (next mAP calculation at 7500 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7413: 0.064685, 0.050531 avg loss, 0.000026 rate, 0.677757 seconds, 474432 images, 0.117673 hours left\n",
            "Loaded: 0.000047 seconds\n",
            "\n",
            " (next mAP calculation at 7500 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7414: 0.039235, 0.049401 avg loss, 0.000026 rate, 0.588996 seconds, 474496 images, 0.117601 hours left\n",
            "Loaded: 0.000037 seconds\n",
            "\n",
            " (next mAP calculation at 7500 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7415: 0.063446, 0.050806 avg loss, 0.000026 rate, 0.614670 seconds, 474560 images, 0.117384 hours left\n",
            "Loaded: 0.000074 seconds\n",
            "\n",
            " (next mAP calculation at 7500 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7416: 0.022864, 0.048012 avg loss, 0.000026 rate, 0.638607 seconds, 474624 images, 0.117209 hours left\n",
            "Loaded: 0.000043 seconds\n",
            "\n",
            " (next mAP calculation at 7500 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7417: 0.050547, 0.048265 avg loss, 0.000026 rate, 0.614214 seconds, 474688 images, 0.117073 hours left\n",
            "Loaded: 0.000039 seconds\n",
            "\n",
            " (next mAP calculation at 7500 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7418: 0.042418, 0.047680 avg loss, 0.000026 rate, 0.633766 seconds, 474752 images, 0.116897 hours left\n",
            "Loaded: 0.000043 seconds\n",
            "\n",
            " (next mAP calculation at 7500 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7419: 0.042358, 0.047148 avg loss, 0.000026 rate, 0.646212 seconds, 474816 images, 0.116753 hours left\n",
            "Loaded: 0.000074 seconds\n",
            "\n",
            " (next mAP calculation at 7500 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7420: 0.027178, 0.045151 avg loss, 0.000026 rate, 0.603267 seconds, 474880 images, 0.116629 hours left\n",
            "Loaded: 0.000073 seconds\n",
            "\n",
            " (next mAP calculation at 7500 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7421: 0.029404, 0.043576 avg loss, 0.000026 rate, 0.664933 seconds, 474944 images, 0.116434 hours left\n",
            "Loaded: 0.000059 seconds\n",
            "\n",
            " (next mAP calculation at 7500 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7422: 0.065820, 0.045801 avg loss, 0.000026 rate, 0.608392 seconds, 475008 images, 0.116340 hours left\n",
            "Loaded: 0.000059 seconds\n",
            "\n",
            " (next mAP calculation at 7500 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7423: 0.019235, 0.043144 avg loss, 0.000026 rate, 0.607324 seconds, 475072 images, 0.116153 hours left\n",
            "Loaded: 0.000042 seconds\n",
            "\n",
            " (next mAP calculation at 7500 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7424: 0.067491, 0.045579 avg loss, 0.000026 rate, 0.614548 seconds, 475136 images, 0.115965 hours left\n",
            "Loaded: 0.000044 seconds\n",
            "\n",
            " (next mAP calculation at 7500 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7425: 0.074663, 0.048487 avg loss, 0.000026 rate, 0.602612 seconds, 475200 images, 0.115789 hours left\n",
            "Loaded: 0.000044 seconds\n",
            "\n",
            " (next mAP calculation at 7500 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7426: 0.023516, 0.045990 avg loss, 0.000026 rate, 0.613516 seconds, 475264 images, 0.115594 hours left\n",
            "Loaded: 0.000044 seconds\n",
            "\n",
            " (next mAP calculation at 7500 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7427: 0.032688, 0.044660 avg loss, 0.000026 rate, 0.628570 seconds, 475328 images, 0.115416 hours left\n",
            "Loaded: 0.000046 seconds\n",
            "\n",
            " (next mAP calculation at 7500 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7428: 0.048791, 0.045073 avg loss, 0.000026 rate, 0.630188 seconds, 475392 images, 0.115262 hours left\n",
            "Loaded: 0.000055 seconds\n",
            "\n",
            " (next mAP calculation at 7500 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7429: 0.057046, 0.046270 avg loss, 0.000026 rate, 0.612926 seconds, 475456 images, 0.115111 hours left\n",
            "Loaded: 0.000095 seconds\n",
            "\n",
            " (next mAP calculation at 7500 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7430: 0.076948, 0.049338 avg loss, 0.000026 rate, 0.620858 seconds, 475520 images, 0.114932 hours left\n",
            "Loaded: 0.000031 seconds\n",
            "\n",
            " (next mAP calculation at 7500 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7431: 0.050490, 0.049453 avg loss, 0.000026 rate, 0.613812 seconds, 475584 images, 0.114766 hours left\n",
            "Loaded: 0.000043 seconds\n",
            "\n",
            " (next mAP calculation at 7500 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7432: 0.098095, 0.054317 avg loss, 0.000026 rate, 0.589251 seconds, 475648 images, 0.114589 hours left\n",
            "Loaded: 0.000058 seconds\n",
            "\n",
            " (next mAP calculation at 7500 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7433: 0.055417, 0.054427 avg loss, 0.000026 rate, 0.588376 seconds, 475712 images, 0.114373 hours left\n",
            "Loaded: 0.000049 seconds\n",
            "\n",
            " (next mAP calculation at 7500 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7434: 0.042360, 0.053221 avg loss, 0.000026 rate, 0.628505 seconds, 475776 images, 0.114156 hours left\n",
            "Loaded: 0.000042 seconds\n",
            "\n",
            " (next mAP calculation at 7500 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7435: 0.065201, 0.054419 avg loss, 0.000026 rate, 0.612467 seconds, 475840 images, 0.114002 hours left\n",
            "Loaded: 0.000070 seconds\n",
            "\n",
            " (next mAP calculation at 7500 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7436: 0.057960, 0.054773 avg loss, 0.000026 rate, 0.647370 seconds, 475904 images, 0.113824 hours left\n",
            "Loaded: 0.000050 seconds\n",
            "\n",
            " (next mAP calculation at 7500 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7437: 0.065355, 0.055831 avg loss, 0.000026 rate, 0.611670 seconds, 475968 images, 0.113700 hours left\n",
            "Loaded: 0.000060 seconds\n",
            "\n",
            " (next mAP calculation at 7500 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7438: 0.080277, 0.058276 avg loss, 0.000026 rate, 0.634130 seconds, 476032 images, 0.113519 hours left\n",
            "Loaded: 0.000099 seconds\n",
            "\n",
            " (next mAP calculation at 7500 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7439: 0.052302, 0.057678 avg loss, 0.000026 rate, 0.637276 seconds, 476096 images, 0.113374 hours left\n",
            "Loaded: 0.000044 seconds\n",
            "\n",
            " (next mAP calculation at 7500 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7440: 0.058170, 0.057727 avg loss, 0.000026 rate, 0.643063 seconds, 476160 images, 0.113234 hours left\n",
            "Loaded: 0.000066 seconds\n",
            "\n",
            " (next mAP calculation at 7500 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7441: 0.026164, 0.054571 avg loss, 0.000026 rate, 0.599119 seconds, 476224 images, 0.113102 hours left\n",
            "Loaded: 0.000071 seconds\n",
            "\n",
            " (next mAP calculation at 7500 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7442: 0.076559, 0.056770 avg loss, 0.000026 rate, 0.624158 seconds, 476288 images, 0.112901 hours left\n",
            "Loaded: 0.000128 seconds\n",
            "\n",
            " (next mAP calculation at 7500 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7443: 0.066523, 0.057745 avg loss, 0.000026 rate, 0.636134 seconds, 476352 images, 0.112740 hours left\n",
            "Loaded: 0.000090 seconds\n",
            "\n",
            " (next mAP calculation at 7500 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7444: 0.072245, 0.059195 avg loss, 0.000026 rate, 0.653975 seconds, 476416 images, 0.112597 hours left\n",
            "Loaded: 0.000041 seconds\n",
            "\n",
            " (next mAP calculation at 7500 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7445: 0.064534, 0.059729 avg loss, 0.000026 rate, 0.570303 seconds, 476480 images, 0.112481 hours left\n",
            "Loaded: 0.000072 seconds\n",
            "\n",
            " (next mAP calculation at 7500 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7446: 0.071554, 0.060912 avg loss, 0.000026 rate, 0.606747 seconds, 476544 images, 0.112236 hours left\n",
            "Loaded: 0.000042 seconds\n",
            "\n",
            " (next mAP calculation at 7500 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7447: 0.031588, 0.057979 avg loss, 0.000026 rate, 0.579973 seconds, 476608 images, 0.112047 hours left\n",
            "Loaded: 0.000040 seconds\n",
            "\n",
            " (next mAP calculation at 7500 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7448: 0.042579, 0.056439 avg loss, 0.000026 rate, 0.567912 seconds, 476672 images, 0.111818 hours left\n",
            "Loaded: 0.000063 seconds\n",
            "\n",
            " (next mAP calculation at 7500 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7449: 0.084954, 0.059291 avg loss, 0.000026 rate, 0.665784 seconds, 476736 images, 0.111570 hours left\n",
            "Loaded: 0.000071 seconds\n",
            "\n",
            " (next mAP calculation at 7500 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7450: 0.036828, 0.057044 avg loss, 0.000026 rate, 0.654118 seconds, 476800 images, 0.111474 hours left\n",
            "Loaded: 0.000046 seconds\n",
            "\n",
            " (next mAP calculation at 7500 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7451: 0.040026, 0.055343 avg loss, 0.000026 rate, 0.621840 seconds, 476864 images, 0.111359 hours left\n",
            "Loaded: 0.000068 seconds\n",
            "\n",
            " (next mAP calculation at 7500 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7452: 0.017418, 0.051550 avg loss, 0.000026 rate, 0.645813 seconds, 476928 images, 0.111193 hours left\n",
            "Loaded: 0.000082 seconds\n",
            "\n",
            " (next mAP calculation at 7500 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7453: 0.063061, 0.052701 avg loss, 0.000026 rate, 0.604053 seconds, 476992 images, 0.111065 hours left\n",
            "Loaded: 0.000042 seconds\n",
            "\n",
            " (next mAP calculation at 7500 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7454: 0.030548, 0.050486 avg loss, 0.000026 rate, 0.583678 seconds, 477056 images, 0.110872 hours left\n",
            "Loaded: 0.000045 seconds\n",
            "\n",
            " (next mAP calculation at 7500 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7455: 0.051464, 0.050584 avg loss, 0.000026 rate, 0.635867 seconds, 477120 images, 0.110649 hours left\n",
            "Loaded: 0.000080 seconds\n",
            "\n",
            " (next mAP calculation at 7500 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7456: 0.065461, 0.052071 avg loss, 0.000026 rate, 0.616582 seconds, 477184 images, 0.110505 hours left\n",
            "Loaded: 0.000042 seconds\n",
            "\n",
            " (next mAP calculation at 7500 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7457: 0.087893, 0.055654 avg loss, 0.000026 rate, 0.566754 seconds, 477248 images, 0.110332 hours left\n",
            "Loaded: 0.000043 seconds\n",
            "\n",
            " (next mAP calculation at 7500 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7458: 0.040096, 0.054098 avg loss, 0.000026 rate, 0.605293 seconds, 477312 images, 0.110083 hours left\n",
            "Loaded: 0.000072 seconds\n",
            "\n",
            " (next mAP calculation at 7500 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7459: 0.055545, 0.054243 avg loss, 0.000026 rate, 0.618403 seconds, 477376 images, 0.109894 hours left\n",
            "Loaded: 0.000046 seconds\n",
            "\n",
            " (next mAP calculation at 7500 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7460: 0.083514, 0.057170 avg loss, 0.000026 rate, 0.630038 seconds, 477440 images, 0.109724 hours left\n",
            "Loaded: 0.000059 seconds\n",
            "\n",
            " (next mAP calculation at 7500 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7461: 0.081861, 0.059639 avg loss, 0.000026 rate, 0.613849 seconds, 477504 images, 0.109572 hours left\n",
            "Loaded: 0.000041 seconds\n",
            "\n",
            " (next mAP calculation at 7500 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7462: 0.033499, 0.057025 avg loss, 0.000026 rate, 0.582339 seconds, 477568 images, 0.109396 hours left\n",
            "Loaded: 0.000041 seconds\n",
            "\n",
            " (next mAP calculation at 7500 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7463: 0.065100, 0.057832 avg loss, 0.000026 rate, 0.599695 seconds, 477632 images, 0.109172 hours left\n",
            "Loaded: 0.000045 seconds\n",
            "\n",
            " (next mAP calculation at 7500 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7464: 0.044805, 0.056530 avg loss, 0.000026 rate, 0.651964 seconds, 477696 images, 0.108975 hours left\n",
            "Loaded: 0.000043 seconds\n",
            "\n",
            " (next mAP calculation at 7500 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7465: 0.056646, 0.056541 avg loss, 0.000026 rate, 0.606997 seconds, 477760 images, 0.108856 hours left\n",
            "Loaded: 0.000043 seconds\n",
            "\n",
            " (next mAP calculation at 7500 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7466: 0.048900, 0.055777 avg loss, 0.000026 rate, 0.614350 seconds, 477824 images, 0.108670 hours left\n",
            "Loaded: 0.000076 seconds\n",
            "\n",
            " (next mAP calculation at 7500 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7467: 0.064669, 0.056666 avg loss, 0.000026 rate, 0.636410 seconds, 477888 images, 0.108494 hours left\n",
            "Loaded: 0.000058 seconds\n",
            "\n",
            " (next mAP calculation at 7500 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7468: 0.063735, 0.057373 avg loss, 0.000026 rate, 0.651875 seconds, 477952 images, 0.108352 hours left\n",
            "Loaded: 0.000046 seconds\n",
            "\n",
            " (next mAP calculation at 7500 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7469: 0.034300, 0.055066 avg loss, 0.000026 rate, 0.623710 seconds, 478016 images, 0.108232 hours left\n",
            "Loaded: 0.000078 seconds\n",
            "\n",
            " (next mAP calculation at 7500 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7470: 0.040128, 0.053572 avg loss, 0.000026 rate, 0.655028 seconds, 478080 images, 0.108069 hours left\n",
            "Loaded: 0.000043 seconds\n",
            "\n",
            " (next mAP calculation at 7500 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7471: 0.033122, 0.051527 avg loss, 0.000026 rate, 0.641849 seconds, 478144 images, 0.107953 hours left\n",
            "Loaded: 0.000038 seconds\n",
            "\n",
            " (next mAP calculation at 7500 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7472: 0.057407, 0.052115 avg loss, 0.000026 rate, 0.579024 seconds, 478208 images, 0.107817 hours left\n",
            "Loaded: 0.000046 seconds\n",
            "\n",
            " (next mAP calculation at 7500 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7473: 0.049269, 0.051830 avg loss, 0.000026 rate, 0.599855 seconds, 478272 images, 0.107588 hours left\n",
            "Loaded: 0.000048 seconds\n",
            "\n",
            " (next mAP calculation at 7500 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7474: 0.038817, 0.050529 avg loss, 0.000026 rate, 0.633478 seconds, 478336 images, 0.107390 hours left\n",
            "Loaded: 0.000043 seconds\n",
            "\n",
            " (next mAP calculation at 7500 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7475: 0.041792, 0.049655 avg loss, 0.000026 rate, 0.604160 seconds, 478400 images, 0.107242 hours left\n",
            "Loaded: 0.000042 seconds\n",
            "\n",
            " (next mAP calculation at 7500 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7476: 0.078969, 0.052587 avg loss, 0.000026 rate, 0.634257 seconds, 478464 images, 0.107051 hours left\n",
            "Loaded: 0.000047 seconds\n",
            "\n",
            " (next mAP calculation at 7500 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7477: 0.041982, 0.051526 avg loss, 0.000026 rate, 0.650267 seconds, 478528 images, 0.106904 hours left\n",
            "Loaded: 0.000043 seconds\n",
            "\n",
            " (next mAP calculation at 7500 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7478: 0.035006, 0.049874 avg loss, 0.000026 rate, 0.588761 seconds, 478592 images, 0.106779 hours left\n",
            "Loaded: 0.000077 seconds\n",
            "\n",
            " (next mAP calculation at 7500 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7479: 0.080292, 0.052916 avg loss, 0.000026 rate, 0.604149 seconds, 478656 images, 0.106565 hours left\n",
            "Loaded: 0.000042 seconds\n",
            "\n",
            " (next mAP calculation at 7500 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7480: 0.092877, 0.056912 avg loss, 0.000026 rate, 0.598539 seconds, 478720 images, 0.106374 hours left\n",
            "Loaded: 0.000044 seconds\n",
            "\n",
            " (next mAP calculation at 7500 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7481: 0.027364, 0.053957 avg loss, 0.000026 rate, 0.579937 seconds, 478784 images, 0.106175 hours left\n",
            "Loaded: 0.000073 seconds\n",
            "\n",
            " (next mAP calculation at 7500 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7482: 0.034574, 0.052019 avg loss, 0.000026 rate, 0.584684 seconds, 478848 images, 0.105949 hours left\n",
            "Loaded: 0.000092 seconds\n",
            "\n",
            " (next mAP calculation at 7500 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7483: 0.031311, 0.049948 avg loss, 0.000026 rate, 0.587788 seconds, 478912 images, 0.105731 hours left\n",
            "Loaded: 0.000044 seconds\n",
            "\n",
            " (next mAP calculation at 7500 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7484: 0.065769, 0.051530 avg loss, 0.000026 rate, 0.624092 seconds, 478976 images, 0.105518 hours left\n",
            "Loaded: 0.000066 seconds\n",
            "\n",
            " (next mAP calculation at 7500 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7485: 0.024614, 0.048839 avg loss, 0.000026 rate, 0.628959 seconds, 479040 images, 0.105358 hours left\n",
            "Loaded: 0.000059 seconds\n",
            "\n",
            " (next mAP calculation at 7500 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7486: 0.024228, 0.046378 avg loss, 0.000026 rate, 0.574971 seconds, 479104 images, 0.105204 hours left\n",
            "Loaded: 0.000045 seconds\n",
            "\n",
            " (next mAP calculation at 7500 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7487: 0.041743, 0.045914 avg loss, 0.000026 rate, 0.647511 seconds, 479168 images, 0.104973 hours left\n",
            "Loaded: 0.000045 seconds\n",
            "\n",
            " (next mAP calculation at 7500 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7488: 0.084847, 0.049807 avg loss, 0.000026 rate, 0.598142 seconds, 479232 images, 0.104846 hours left\n",
            "Loaded: 0.000045 seconds\n",
            "\n",
            " (next mAP calculation at 7500 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7489: 0.055908, 0.050417 avg loss, 0.000026 rate, 0.568048 seconds, 479296 images, 0.104649 hours left\n",
            "Loaded: 0.000080 seconds\n",
            "\n",
            " (next mAP calculation at 7500 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7490: 0.047576, 0.050133 avg loss, 0.000026 rate, 0.635902 seconds, 479360 images, 0.104408 hours left\n",
            "Loaded: 0.000070 seconds\n",
            "\n",
            " (next mAP calculation at 7500 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7491: 0.041376, 0.049257 avg loss, 0.000026 rate, 0.612546 seconds, 479424 images, 0.104265 hours left\n",
            "Loaded: 0.000031 seconds\n",
            "\n",
            " (next mAP calculation at 7500 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7492: 0.015545, 0.045886 avg loss, 0.000026 rate, 0.596366 seconds, 479488 images, 0.104089 hours left\n",
            "Loaded: 0.000060 seconds\n",
            "\n",
            " (next mAP calculation at 7500 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7493: 0.028334, 0.044131 avg loss, 0.000026 rate, 0.604409 seconds, 479552 images, 0.103890 hours left\n",
            "Loaded: 0.000076 seconds\n",
            "\n",
            " (next mAP calculation at 7500 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7494: 0.048072, 0.044525 avg loss, 0.000026 rate, 0.590759 seconds, 479616 images, 0.103702 hours left\n",
            "Loaded: 0.000071 seconds\n",
            "\n",
            " (next mAP calculation at 7500 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7495: 0.047006, 0.044773 avg loss, 0.000026 rate, 0.599858 seconds, 479680 images, 0.103495 hours left\n",
            "Loaded: 0.000075 seconds\n",
            "\n",
            " (next mAP calculation at 7500 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7496: 0.031925, 0.043488 avg loss, 0.000026 rate, 0.601945 seconds, 479744 images, 0.103302 hours left\n",
            "Loaded: 0.000068 seconds\n",
            "\n",
            " (next mAP calculation at 7500 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7497: 0.038831, 0.043023 avg loss, 0.000026 rate, 0.571256 seconds, 479808 images, 0.103112 hours left\n",
            "Loaded: 0.000046 seconds\n",
            "\n",
            " (next mAP calculation at 7500 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7498: 0.035488, 0.042269 avg loss, 0.000026 rate, 0.627159 seconds, 479872 images, 0.102879 hours left\n",
            "Loaded: 0.000066 seconds\n",
            "\n",
            " (next mAP calculation at 7500 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7499: 0.066144, 0.044657 avg loss, 0.000026 rate, 0.620722 seconds, 479936 images, 0.102725 hours left\n",
            "Loaded: 0.000061 seconds\n",
            "\n",
            " (next mAP calculation at 7500 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7500: 0.037205, 0.043911 avg loss, 0.000026 rate, 0.602991 seconds, 480000 images, 0.102562 hours left\n",
            "\n",
            " calculation mAP (mean average precision)...\n",
            "8\n",
            " detections_count = 18, unique_truth_count = 8  \n",
            "class_id = 0, name = gundhi, ap = 0.00%   \t (TP = 0, FP = 1) \n",
            "class_id = 1, name = hispaa_insect, ap = 100.00%   \t (TP = 3, FP = 0) \n",
            "class_id = 2, name = stem_bor_lar, ap = 75.00%   \t (TP = 3, FP = 1) \n",
            "class_id = 3, name = stem_bor_moth, ap = 50.00%   \t (TP = 2, FP = 1) \n",
            "\n",
            " for conf_thresh = 0.25, precision = 0.73, recall = 1.00, F1-score = 0.84 \n",
            " for conf_thresh = 0.25, TP = 8, FP = 3, FN = 0, average IoU = 52.29 % \n",
            "\n",
            " IoU threshold = 50 %, used Area-Under-Curve for each unique Recall \n",
            " mean average precision (mAP@0.50) = 0.562500, or 56.25 % \n",
            "Total Detection Time: 1 Seconds\n",
            "\n",
            "Set -points flag:\n",
            " `-points 101` for MS COCO \n",
            " `-points 11` for PascalVOC 2007 (uncomment `difficult` in voc.data) \n",
            " `-points 0` (AUC) for ImageNet, PascalVOC 2010-2012, your custom dataset\n",
            "\n",
            " mean_average_precision (mAP@0.5) = 0.562500 \n",
            "Saving weights to backup//custom-yolov4-tiny-detector_last.weights\n",
            "Loaded: 0.000059 seconds\n",
            "\n",
            " (next mAP calculation at 7600 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7501: 0.056454, 0.045166 avg loss, 0.000026 rate, 0.615310 seconds, 480064 images, 0.102426 hours left\n",
            "Loaded: 0.000043 seconds\n",
            "\n",
            " (next mAP calculation at 7600 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7502: 0.041170, 0.044766 avg loss, 0.000026 rate, 0.613383 seconds, 480128 images, 0.102255 hours left\n",
            "Loaded: 0.000043 seconds\n",
            "\n",
            " (next mAP calculation at 7600 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7503: 0.063222, 0.046612 avg loss, 0.000026 rate, 0.630605 seconds, 480192 images, 0.102081 hours left\n",
            "Loaded: 0.000086 seconds\n",
            "\n",
            " (next mAP calculation at 7600 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7504: 0.040347, 0.045985 avg loss, 0.000026 rate, 0.652373 seconds, 480256 images, 0.101931 hours left\n",
            "Loaded: 0.000064 seconds\n",
            "\n",
            " (next mAP calculation at 7600 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7505: 0.051465, 0.046533 avg loss, 0.000026 rate, 0.615888 seconds, 480320 images, 0.101810 hours left\n",
            "Loaded: 0.000047 seconds\n",
            "\n",
            " (next mAP calculation at 7600 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7506: 0.054086, 0.047288 avg loss, 0.000026 rate, 0.648025 seconds, 480384 images, 0.101639 hours left\n",
            "Loaded: 0.000067 seconds\n",
            "\n",
            " (next mAP calculation at 7600 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7507: 0.058295, 0.048389 avg loss, 0.000026 rate, 0.623735 seconds, 480448 images, 0.101512 hours left\n",
            "Loaded: 0.000043 seconds\n",
            "\n",
            " (next mAP calculation at 7600 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7508: 0.061403, 0.049690 avg loss, 0.000026 rate, 0.598517 seconds, 480512 images, 0.101351 hours left\n",
            "Loaded: 0.000042 seconds\n",
            "\n",
            " (next mAP calculation at 7600 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7509: 0.089054, 0.053627 avg loss, 0.000026 rate, 0.618347 seconds, 480576 images, 0.101156 hours left\n",
            "Loaded: 0.000046 seconds\n",
            "\n",
            " (next mAP calculation at 7600 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7510: 0.025324, 0.050797 avg loss, 0.000026 rate, 0.595243 seconds, 480640 images, 0.100988 hours left\n",
            "Loaded: 0.000042 seconds\n",
            "\n",
            " (next mAP calculation at 7600 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7511: 0.040665, 0.049783 avg loss, 0.000026 rate, 0.585286 seconds, 480704 images, 0.100788 hours left\n",
            "Loaded: 0.000059 seconds\n",
            "\n",
            " (next mAP calculation at 7600 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7512: 0.049056, 0.049711 avg loss, 0.000026 rate, 0.581503 seconds, 480768 images, 0.100575 hours left\n",
            "Loaded: 0.000041 seconds\n",
            "\n",
            " (next mAP calculation at 7600 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7513: 0.055414, 0.050281 avg loss, 0.000026 rate, 0.594999 seconds, 480832 images, 0.100358 hours left\n",
            "Loaded: 0.000043 seconds\n",
            "\n",
            " (next mAP calculation at 7600 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7514: 0.041895, 0.049442 avg loss, 0.000026 rate, 0.625878 seconds, 480896 images, 0.100159 hours left\n",
            "Loaded: 0.000057 seconds\n",
            "\n",
            " (next mAP calculation at 7600 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7515: 0.097037, 0.054202 avg loss, 0.000026 rate, 0.572554 seconds, 480960 images, 0.100003 hours left\n",
            "Loaded: 0.000065 seconds\n",
            "\n",
            " (next mAP calculation at 7600 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7516: 0.041268, 0.052909 avg loss, 0.000026 rate, 0.625364 seconds, 481024 images, 0.099774 hours left\n",
            "Loaded: 0.000043 seconds\n",
            "\n",
            " (next mAP calculation at 7600 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7517: 0.046335, 0.052251 avg loss, 0.000026 rate, 0.613112 seconds, 481088 images, 0.099617 hours left\n",
            "Loaded: 0.000049 seconds\n",
            "\n",
            " (next mAP calculation at 7600 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7518: 0.047164, 0.051742 avg loss, 0.000026 rate, 0.616826 seconds, 481152 images, 0.099444 hours left\n",
            "Loaded: 0.000041 seconds\n",
            "\n",
            " (next mAP calculation at 7600 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7519: 0.055222, 0.052090 avg loss, 0.000026 rate, 0.592965 seconds, 481216 images, 0.099275 hours left\n",
            "Loaded: 0.000065 seconds\n",
            "\n",
            " (next mAP calculation at 7600 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7520: 0.017677, 0.048649 avg loss, 0.000026 rate, 0.632544 seconds, 481280 images, 0.099075 hours left\n",
            "Loaded: 0.000077 seconds\n",
            "\n",
            " (next mAP calculation at 7600 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7521: 0.031696, 0.046954 avg loss, 0.000026 rate, 0.570546 seconds, 481344 images, 0.098928 hours left\n",
            "Loaded: 0.000041 seconds\n",
            "\n",
            " (next mAP calculation at 7600 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7522: 0.047052, 0.046964 avg loss, 0.000026 rate, 0.622165 seconds, 481408 images, 0.098698 hours left\n",
            "Loaded: 0.000060 seconds\n",
            "\n",
            " (next mAP calculation at 7600 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7523: 0.065492, 0.048816 avg loss, 0.000026 rate, 0.669347 seconds, 481472 images, 0.098537 hours left\n",
            "Loaded: 0.000065 seconds\n",
            "\n",
            " (next mAP calculation at 7600 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7524: 0.036110, 0.047546 avg loss, 0.000026 rate, 0.618205 seconds, 481536 images, 0.098438 hours left\n",
            "Loaded: 0.000039 seconds\n",
            "\n",
            " (next mAP calculation at 7600 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7525: 0.073800, 0.050171 avg loss, 0.000026 rate, 0.567728 seconds, 481600 images, 0.098272 hours left\n",
            "Loaded: 0.000057 seconds\n",
            "\n",
            " (next mAP calculation at 7600 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7526: 0.048132, 0.049967 avg loss, 0.000026 rate, 0.638616 seconds, 481664 images, 0.098038 hours left\n",
            "Loaded: 0.000044 seconds\n",
            "\n",
            " (next mAP calculation at 7600 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7527: 0.090669, 0.054037 avg loss, 0.000026 rate, 0.628144 seconds, 481728 images, 0.097899 hours left\n",
            "Loaded: 0.000046 seconds\n",
            "\n",
            " (next mAP calculation at 7600 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7528: 0.072941, 0.055928 avg loss, 0.000026 rate, 0.649358 seconds, 481792 images, 0.097745 hours left\n",
            "Loaded: 0.000050 seconds\n",
            "\n",
            " (next mAP calculation at 7600 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7529: 0.057775, 0.056113 avg loss, 0.000026 rate, 0.618585 seconds, 481856 images, 0.097619 hours left\n",
            "Loaded: 0.000042 seconds\n",
            "\n",
            " (next mAP calculation at 7600 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7530: 0.088306, 0.059332 avg loss, 0.000026 rate, 0.640573 seconds, 481920 images, 0.097452 hours left\n",
            "Loaded: 0.000054 seconds\n",
            "\n",
            " (next mAP calculation at 7600 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7531: 0.039953, 0.057394 avg loss, 0.000026 rate, 0.603950 seconds, 481984 images, 0.097314 hours left\n",
            "Loaded: 0.000042 seconds\n",
            "\n",
            " (next mAP calculation at 7600 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7532: 0.058909, 0.057546 avg loss, 0.000026 rate, 0.611269 seconds, 482048 images, 0.097128 hours left\n",
            "Loaded: 0.000037 seconds\n",
            "\n",
            " (next mAP calculation at 7600 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7533: 0.078789, 0.059670 avg loss, 0.000026 rate, 0.608765 seconds, 482112 images, 0.096951 hours left\n",
            "Loaded: 0.000064 seconds\n",
            "\n",
            " (next mAP calculation at 7600 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7534: 0.048096, 0.058512 avg loss, 0.000026 rate, 0.627013 seconds, 482176 images, 0.096772 hours left\n",
            "Loaded: 0.000042 seconds\n",
            "\n",
            " (next mAP calculation at 7600 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7535: 0.037422, 0.056403 avg loss, 0.000026 rate, 0.614818 seconds, 482240 images, 0.096616 hours left\n",
            "Loaded: 0.000055 seconds\n",
            "\n",
            " (next mAP calculation at 7600 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7536: 0.064964, 0.057259 avg loss, 0.000026 rate, 0.627902 seconds, 482304 images, 0.096444 hours left\n",
            "Loaded: 0.000052 seconds\n",
            "\n",
            " (next mAP calculation at 7600 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7537: 0.038233, 0.055357 avg loss, 0.000026 rate, 0.607619 seconds, 482368 images, 0.096289 hours left\n",
            "Loaded: 0.000090 seconds\n",
            "\n",
            " (next mAP calculation at 7600 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7538: 0.032308, 0.053052 avg loss, 0.000026 rate, 0.599921 seconds, 482432 images, 0.096107 hours left\n",
            "Loaded: 0.000041 seconds\n",
            "\n",
            " (next mAP calculation at 7600 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7539: 0.063125, 0.054059 avg loss, 0.000026 rate, 0.606355 seconds, 482496 images, 0.095916 hours left\n",
            "Loaded: 0.000039 seconds\n",
            "\n",
            " (next mAP calculation at 7600 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7540: 0.035192, 0.052173 avg loss, 0.000026 rate, 0.579330 seconds, 482560 images, 0.095734 hours left\n",
            "Loaded: 0.000064 seconds\n",
            "\n",
            " (next mAP calculation at 7600 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7541: 0.039875, 0.050943 avg loss, 0.000026 rate, 0.629151 seconds, 482624 images, 0.095517 hours left\n",
            "Loaded: 0.000043 seconds\n",
            "\n",
            " (next mAP calculation at 7600 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7542: 0.053910, 0.051240 avg loss, 0.000026 rate, 0.620066 seconds, 482688 images, 0.095364 hours left\n",
            "Loaded: 0.000047 seconds\n",
            "\n",
            " (next mAP calculation at 7600 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7543: 0.079134, 0.054029 avg loss, 0.000026 rate, 0.636104 seconds, 482752 images, 0.095199 hours left\n",
            "Loaded: 0.000079 seconds\n",
            "\n",
            " (next mAP calculation at 7600 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7544: 0.044138, 0.053040 avg loss, 0.000026 rate, 0.576355 seconds, 482816 images, 0.095055 hours left\n",
            "Loaded: 0.000045 seconds\n",
            "\n",
            " (next mAP calculation at 7600 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7545: 0.081362, 0.055872 avg loss, 0.000026 rate, 0.645847 seconds, 482880 images, 0.094834 hours left\n",
            "Loaded: 0.000045 seconds\n",
            "\n",
            " (next mAP calculation at 7600 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7546: 0.056740, 0.055959 avg loss, 0.000026 rate, 0.612183 seconds, 482944 images, 0.094702 hours left\n",
            "Loaded: 0.000043 seconds\n",
            "\n",
            " (next mAP calculation at 7600 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7547: 0.055770, 0.055940 avg loss, 0.000026 rate, 0.580139 seconds, 483008 images, 0.094527 hours left\n",
            "Loaded: 0.000052 seconds\n",
            "\n",
            " (next mAP calculation at 7600 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7548: 0.040827, 0.054429 avg loss, 0.000026 rate, 0.602393 seconds, 483072 images, 0.094312 hours left\n",
            "Loaded: 0.000064 seconds\n",
            "\n",
            " (next mAP calculation at 7600 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7549: 0.072331, 0.056219 avg loss, 0.000026 rate, 0.619406 seconds, 483136 images, 0.094125 hours left\n",
            "Loaded: 0.000044 seconds\n",
            "\n",
            " (next mAP calculation at 7600 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7550: 0.031483, 0.053745 avg loss, 0.000026 rate, 0.587410 seconds, 483200 images, 0.093960 hours left\n",
            "Loaded: 0.000043 seconds\n",
            "\n",
            " (next mAP calculation at 7600 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7551: 0.053096, 0.053680 avg loss, 0.000026 rate, 0.627479 seconds, 483264 images, 0.093755 hours left\n",
            "Loaded: 0.000046 seconds\n",
            "\n",
            " (next mAP calculation at 7600 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7552: 0.035893, 0.051902 avg loss, 0.000026 rate, 0.622918 seconds, 483328 images, 0.093600 hours left\n",
            "Loaded: 0.000095 seconds\n",
            "\n",
            " (next mAP calculation at 7600 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7553: 0.025289, 0.049240 avg loss, 0.000026 rate, 0.594933 seconds, 483392 images, 0.093439 hours left\n",
            "Loaded: 0.000066 seconds\n",
            "\n",
            " (next mAP calculation at 7600 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7554: 0.046801, 0.048996 avg loss, 0.000026 rate, 0.620554 seconds, 483456 images, 0.093244 hours left\n",
            "Loaded: 0.000061 seconds\n",
            "\n",
            " (next mAP calculation at 7600 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7555: 0.063139, 0.050411 avg loss, 0.000026 rate, 0.620588 seconds, 483520 images, 0.093080 hours left\n",
            "Loaded: 0.000039 seconds\n",
            "\n",
            " (next mAP calculation at 7600 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7556: 0.094690, 0.054839 avg loss, 0.000026 rate, 0.551160 seconds, 483584 images, 0.092917 hours left\n",
            "Loaded: 0.000040 seconds\n",
            "\n",
            " (next mAP calculation at 7600 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7557: 0.033936, 0.052748 avg loss, 0.000026 rate, 0.587919 seconds, 483648 images, 0.092667 hours left\n",
            "Loaded: 0.000034 seconds\n",
            "\n",
            " (next mAP calculation at 7600 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7558: 0.112655, 0.058739 avg loss, 0.000026 rate, 0.661386 seconds, 483712 images, 0.092464 hours left\n",
            "Loaded: 0.000051 seconds\n",
            "\n",
            " (next mAP calculation at 7600 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7559: 0.079949, 0.060860 avg loss, 0.000026 rate, 0.575952 seconds, 483776 images, 0.092352 hours left\n",
            "Loaded: 0.000040 seconds\n",
            "\n",
            " (next mAP calculation at 7600 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7560: 0.039957, 0.058770 avg loss, 0.000026 rate, 0.604084 seconds, 483840 images, 0.092134 hours left\n",
            "Loaded: 0.000051 seconds\n",
            "\n",
            " (next mAP calculation at 7600 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7561: 0.039788, 0.056872 avg loss, 0.000026 rate, 0.624982 seconds, 483904 images, 0.091951 hours left\n",
            "Loaded: 0.000050 seconds\n",
            "\n",
            " (next mAP calculation at 7600 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7562: 0.067644, 0.057949 avg loss, 0.000026 rate, 0.645731 seconds, 483968 images, 0.091793 hours left\n",
            "Loaded: 0.000053 seconds\n",
            "\n",
            " (next mAP calculation at 7600 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7563: 0.051669, 0.057321 avg loss, 0.000026 rate, 0.610602 seconds, 484032 images, 0.091661 hours left\n",
            "Loaded: 0.000068 seconds\n",
            "\n",
            " (next mAP calculation at 7600 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7564: 0.038456, 0.055434 avg loss, 0.000026 rate, 0.604684 seconds, 484096 images, 0.091486 hours left\n",
            "Loaded: 0.000044 seconds\n",
            "\n",
            " (next mAP calculation at 7600 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7565: 0.055648, 0.055456 avg loss, 0.000026 rate, 0.603669 seconds, 484160 images, 0.091303 hours left\n",
            "Loaded: 0.000089 seconds\n",
            "\n",
            " (next mAP calculation at 7600 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7566: 0.022261, 0.052136 avg loss, 0.000026 rate, 0.629340 seconds, 484224 images, 0.091120 hours left\n",
            "Loaded: 0.000046 seconds\n",
            "\n",
            " (next mAP calculation at 7600 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7567: 0.064105, 0.053333 avg loss, 0.000026 rate, 0.592073 seconds, 484288 images, 0.090968 hours left\n",
            "Loaded: 0.000042 seconds\n",
            "\n",
            " (next mAP calculation at 7600 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7568: 0.104293, 0.058429 avg loss, 0.000026 rate, 0.627916 seconds, 484352 images, 0.090770 hours left\n",
            "Loaded: 0.000067 seconds\n",
            "\n",
            " (next mAP calculation at 7600 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7569: 0.024786, 0.055065 avg loss, 0.000026 rate, 0.608477 seconds, 484416 images, 0.090616 hours left\n",
            "Loaded: 0.000057 seconds\n",
            "\n",
            " (next mAP calculation at 7600 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7570: 0.030055, 0.052564 avg loss, 0.000026 rate, 0.578272 seconds, 484480 images, 0.090438 hours left\n",
            "Loaded: 0.000065 seconds\n",
            "\n",
            " (next mAP calculation at 7600 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7571: 0.033155, 0.050623 avg loss, 0.000026 rate, 0.609158 seconds, 484544 images, 0.090225 hours left\n",
            "Loaded: 0.000081 seconds\n",
            "\n",
            " (next mAP calculation at 7600 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7572: 0.051504, 0.050711 avg loss, 0.000026 rate, 0.623930 seconds, 484608 images, 0.090049 hours left\n",
            "Loaded: 0.000041 seconds\n",
            "\n",
            " (next mAP calculation at 7600 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7573: 0.065274, 0.052167 avg loss, 0.000026 rate, 0.585914 seconds, 484672 images, 0.089890 hours left\n",
            "Loaded: 0.000058 seconds\n",
            "\n",
            " (next mAP calculation at 7600 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7574: 0.027236, 0.049674 avg loss, 0.000026 rate, 0.605638 seconds, 484736 images, 0.089686 hours left\n",
            "Loaded: 0.000068 seconds\n",
            "\n",
            " (next mAP calculation at 7600 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7575: 0.096272, 0.054334 avg loss, 0.000026 rate, 0.626701 seconds, 484800 images, 0.089506 hours left\n",
            "Loaded: 0.000051 seconds\n",
            "\n",
            " (next mAP calculation at 7600 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7576: 0.030263, 0.051927 avg loss, 0.000026 rate, 0.605455 seconds, 484864 images, 0.089351 hours left\n",
            "Loaded: 0.000052 seconds\n",
            "\n",
            " (next mAP calculation at 7600 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7577: 0.026597, 0.049394 avg loss, 0.000026 rate, 0.599511 seconds, 484928 images, 0.089171 hours left\n",
            "Loaded: 0.000037 seconds\n",
            "\n",
            " (next mAP calculation at 7600 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7578: 0.067832, 0.051238 avg loss, 0.000026 rate, 0.654667 seconds, 484992 images, 0.088983 hours left\n",
            "Loaded: 0.000062 seconds\n",
            "\n",
            " (next mAP calculation at 7600 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7579: 0.085419, 0.054656 avg loss, 0.000026 rate, 0.594722 seconds, 485056 images, 0.088861 hours left\n",
            "Loaded: 0.000043 seconds\n",
            "\n",
            " (next mAP calculation at 7600 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7580: 0.058952, 0.055086 avg loss, 0.000026 rate, 0.631509 seconds, 485120 images, 0.088668 hours left\n",
            "Loaded: 0.000048 seconds\n",
            "\n",
            " (next mAP calculation at 7600 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7581: 0.058998, 0.055477 avg loss, 0.000026 rate, 0.692608 seconds, 485184 images, 0.088518 hours left\n",
            "Loaded: 0.000037 seconds\n",
            "\n",
            " (next mAP calculation at 7600 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7582: 0.035317, 0.053461 avg loss, 0.000026 rate, 0.576560 seconds, 485248 images, 0.088439 hours left\n",
            "Loaded: 0.000043 seconds\n",
            "\n",
            " (next mAP calculation at 7600 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7583: 0.052321, 0.053347 avg loss, 0.000026 rate, 0.606021 seconds, 485312 images, 0.088224 hours left\n",
            "Loaded: 0.000068 seconds\n",
            "\n",
            " (next mAP calculation at 7600 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7584: 0.042448, 0.052257 avg loss, 0.000026 rate, 0.656025 seconds, 485376 images, 0.088044 hours left\n",
            "Loaded: 0.000044 seconds\n",
            "\n",
            " (next mAP calculation at 7600 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7585: 0.045014, 0.051533 avg loss, 0.000026 rate, 0.637336 seconds, 485440 images, 0.087922 hours left\n",
            "Loaded: 0.000052 seconds\n",
            "\n",
            " (next mAP calculation at 7600 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7586: 0.062821, 0.052661 avg loss, 0.000026 rate, 0.594857 seconds, 485504 images, 0.087777 hours left\n",
            "Loaded: 0.000067 seconds\n",
            "\n",
            " (next mAP calculation at 7600 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7587: 0.031460, 0.050541 avg loss, 0.000026 rate, 0.586978 seconds, 485568 images, 0.087584 hours left\n",
            "Loaded: 0.000049 seconds\n",
            "\n",
            " (next mAP calculation at 7600 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7588: 0.029987, 0.048486 avg loss, 0.000026 rate, 0.643112 seconds, 485632 images, 0.087381 hours left\n",
            "Loaded: 0.000046 seconds\n",
            "\n",
            " (next mAP calculation at 7600 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7589: 0.049000, 0.048537 avg loss, 0.000026 rate, 0.617154 seconds, 485696 images, 0.087244 hours left\n",
            "Loaded: 0.000048 seconds\n",
            "\n",
            " (next mAP calculation at 7600 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7590: 0.066732, 0.050357 avg loss, 0.000026 rate, 0.602737 seconds, 485760 images, 0.087076 hours left\n",
            "Loaded: 0.000036 seconds\n",
            "\n",
            " (next mAP calculation at 7600 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7591: 0.032148, 0.048536 avg loss, 0.000026 rate, 0.616879 seconds, 485824 images, 0.086892 hours left\n",
            "Loaded: 0.000069 seconds\n",
            "\n",
            " (next mAP calculation at 7600 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7592: 0.083532, 0.052036 avg loss, 0.000026 rate, 0.624245 seconds, 485888 images, 0.086724 hours left\n",
            "Loaded: 0.000056 seconds\n",
            "\n",
            " (next mAP calculation at 7600 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7593: 0.031705, 0.050002 avg loss, 0.000026 rate, 0.580865 seconds, 485952 images, 0.086564 hours left\n",
            "Loaded: 0.000051 seconds\n",
            "\n",
            " (next mAP calculation at 7600 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7594: 0.040587, 0.049061 avg loss, 0.000026 rate, 0.591823 seconds, 486016 images, 0.086355 hours left\n",
            "Loaded: 0.000041 seconds\n",
            "\n",
            " (next mAP calculation at 7600 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7595: 0.070620, 0.051217 avg loss, 0.000026 rate, 0.558455 seconds, 486080 images, 0.086159 hours left\n",
            "Loaded: 0.000089 seconds\n",
            "\n",
            " (next mAP calculation at 7600 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7596: 0.085260, 0.054621 avg loss, 0.000026 rate, 0.611231 seconds, 486144 images, 0.085926 hours left\n",
            "Loaded: 0.000066 seconds\n",
            "\n",
            " (next mAP calculation at 7600 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7597: 0.042366, 0.053396 avg loss, 0.000026 rate, 0.642628 seconds, 486208 images, 0.085753 hours left\n",
            "Loaded: 0.000044 seconds\n",
            "\n",
            " (next mAP calculation at 7600 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7598: 0.045446, 0.052601 avg loss, 0.000026 rate, 0.573362 seconds, 486272 images, 0.085615 hours left\n",
            "Loaded: 0.000062 seconds\n",
            "\n",
            " (next mAP calculation at 7600 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7599: 0.054432, 0.052784 avg loss, 0.000026 rate, 0.616393 seconds, 486336 images, 0.085399 hours left\n",
            "Loaded: 0.000068 seconds\n",
            "\n",
            " (next mAP calculation at 7600 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7600: 0.049147, 0.052420 avg loss, 0.000026 rate, 0.636948 seconds, 486400 images, 0.085231 hours left\n",
            "\n",
            " calculation mAP (mean average precision)...\n",
            "8\n",
            " detections_count = 19, unique_truth_count = 8  \n",
            "class_id = 0, name = gundhi, ap = 0.00%   \t (TP = 0, FP = 1) \n",
            "class_id = 1, name = hispaa_insect, ap = 100.00%   \t (TP = 3, FP = 0) \n",
            "class_id = 2, name = stem_bor_lar, ap = 75.00%   \t (TP = 3, FP = 1) \n",
            "class_id = 3, name = stem_bor_moth, ap = 50.00%   \t (TP = 2, FP = 1) \n",
            "\n",
            " for conf_thresh = 0.25, precision = 0.73, recall = 1.00, F1-score = 0.84 \n",
            " for conf_thresh = 0.25, TP = 8, FP = 3, FN = 0, average IoU = 52.24 % \n",
            "\n",
            " IoU threshold = 50 %, used Area-Under-Curve for each unique Recall \n",
            " mean average precision (mAP@0.50) = 0.562500, or 56.25 % \n",
            "Total Detection Time: 0 Seconds\n",
            "\n",
            "Set -points flag:\n",
            " `-points 101` for MS COCO \n",
            " `-points 11` for PascalVOC 2007 (uncomment `difficult` in voc.data) \n",
            " `-points 0` (AUC) for ImageNet, PascalVOC 2010-2012, your custom dataset\n",
            "\n",
            " mean_average_precision (mAP@0.5) = 0.562500 \n",
            "Saving weights to backup//custom-yolov4-tiny-detector_last.weights\n",
            "Loaded: 0.000060 seconds\n",
            "\n",
            " (next mAP calculation at 7700 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7601: 0.078019, 0.054980 avg loss, 0.000026 rate, 0.609819 seconds, 486464 images, 0.085133 hours left\n",
            "Loaded: 0.000045 seconds\n",
            "\n",
            " (next mAP calculation at 7700 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7602: 0.043057, 0.053788 avg loss, 0.000026 rate, 0.604513 seconds, 486528 images, 0.084957 hours left\n",
            "Loaded: 0.000073 seconds\n",
            "\n",
            " (next mAP calculation at 7700 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7603: 0.060999, 0.054509 avg loss, 0.000026 rate, 0.608132 seconds, 486592 images, 0.084776 hours left\n",
            "Loaded: 0.000043 seconds\n",
            "\n",
            " (next mAP calculation at 7700 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7604: 0.046694, 0.053727 avg loss, 0.000026 rate, 0.584072 seconds, 486656 images, 0.084599 hours left\n",
            "Loaded: 0.000044 seconds\n",
            "\n",
            " (next mAP calculation at 7700 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7605: 0.055273, 0.053882 avg loss, 0.000026 rate, 0.577024 seconds, 486720 images, 0.084396 hours left\n",
            "Loaded: 0.000066 seconds\n",
            "\n",
            " (next mAP calculation at 7700 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7606: 0.045099, 0.053004 avg loss, 0.000026 rate, 0.658378 seconds, 486784 images, 0.084185 hours left\n",
            "Loaded: 0.000044 seconds\n",
            "\n",
            " (next mAP calculation at 7700 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7607: 0.084706, 0.056174 avg loss, 0.000026 rate, 0.572394 seconds, 486848 images, 0.084064 hours left\n",
            "Loaded: 0.000053 seconds\n",
            "\n",
            " (next mAP calculation at 7700 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7608: 0.059651, 0.056522 avg loss, 0.000026 rate, 0.575113 seconds, 486912 images, 0.083848 hours left\n",
            "Loaded: 0.000044 seconds\n",
            "\n",
            " (next mAP calculation at 7700 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7609: 0.075560, 0.058425 avg loss, 0.000026 rate, 0.633372 seconds, 486976 images, 0.083636 hours left\n",
            "Loaded: 0.000074 seconds\n",
            "\n",
            " (next mAP calculation at 7700 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7610: 0.061699, 0.058753 avg loss, 0.000026 rate, 0.613427 seconds, 487040 images, 0.083487 hours left\n",
            "Loaded: 0.000062 seconds\n",
            "\n",
            " (next mAP calculation at 7700 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7611: 0.061129, 0.058990 avg loss, 0.000026 rate, 0.644302 seconds, 487104 images, 0.083317 hours left\n",
            "Loaded: 0.000078 seconds\n",
            "\n",
            " (next mAP calculation at 7700 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7612: 0.073510, 0.060442 avg loss, 0.000026 rate, 0.642865 seconds, 487168 images, 0.083180 hours left\n",
            "Loaded: 0.000050 seconds\n",
            "\n",
            " (next mAP calculation at 7700 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7613: 0.061213, 0.060519 avg loss, 0.000026 rate, 0.642044 seconds, 487232 images, 0.083041 hours left\n",
            "Loaded: 0.000045 seconds\n",
            "\n",
            " (next mAP calculation at 7700 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7614: 0.056061, 0.060074 avg loss, 0.000026 rate, 0.596067 seconds, 487296 images, 0.082901 hours left\n",
            "Loaded: 0.000042 seconds\n",
            "\n",
            " (next mAP calculation at 7700 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7615: 0.059290, 0.059995 avg loss, 0.000026 rate, 0.588726 seconds, 487360 images, 0.082712 hours left\n",
            "Loaded: 0.000040 seconds\n",
            "\n",
            " (next mAP calculation at 7700 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7616: 0.028549, 0.056851 avg loss, 0.000026 rate, 0.636647 seconds, 487424 images, 0.082514 hours left\n",
            "Loaded: 0.000059 seconds\n",
            "\n",
            " (next mAP calculation at 7700 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7617: 0.046093, 0.055775 avg loss, 0.000026 rate, 0.602733 seconds, 487488 images, 0.082368 hours left\n",
            "Loaded: 0.000108 seconds\n",
            "\n",
            " (next mAP calculation at 7700 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7618: 0.051638, 0.055361 avg loss, 0.000026 rate, 0.643056 seconds, 487552 images, 0.082186 hours left\n",
            "Loaded: 0.000043 seconds\n",
            "\n",
            " (next mAP calculation at 7700 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7619: 0.068618, 0.056687 avg loss, 0.000026 rate, 0.615467 seconds, 487616 images, 0.082046 hours left\n",
            "Loaded: 0.000043 seconds\n",
            "\n",
            " (next mAP calculation at 7700 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7620: 0.089522, 0.059970 avg loss, 0.000026 rate, 0.654844 seconds, 487680 images, 0.081877 hours left\n",
            "Loaded: 0.000046 seconds\n",
            "\n",
            " (next mAP calculation at 7700 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7621: 0.050352, 0.059009 avg loss, 0.000026 rate, 0.622888 seconds, 487744 images, 0.081750 hours left\n",
            "Loaded: 0.000075 seconds\n",
            "\n",
            " (next mAP calculation at 7700 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7622: 0.093860, 0.062494 avg loss, 0.000026 rate, 0.611512 seconds, 487808 images, 0.081588 hours left\n",
            "Loaded: 0.000066 seconds\n",
            "\n",
            " (next mAP calculation at 7700 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7623: 0.021416, 0.058386 avg loss, 0.000026 rate, 0.592888 seconds, 487872 images, 0.081414 hours left\n",
            "Loaded: 0.000051 seconds\n",
            "\n",
            " (next mAP calculation at 7700 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7624: 0.039372, 0.056485 avg loss, 0.000026 rate, 0.614483 seconds, 487936 images, 0.081221 hours left\n",
            "Loaded: 0.000046 seconds\n",
            "\n",
            " (next mAP calculation at 7700 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7625: 0.062024, 0.057038 avg loss, 0.000026 rate, 0.638331 seconds, 488000 images, 0.081051 hours left\n",
            "Loaded: 0.000056 seconds\n",
            "\n",
            " (next mAP calculation at 7700 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7626: 0.029777, 0.054312 avg loss, 0.000026 rate, 0.638303 seconds, 488064 images, 0.080905 hours left\n",
            "Loaded: 0.000066 seconds\n",
            "\n",
            " (next mAP calculation at 7700 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7627: 0.051391, 0.054020 avg loss, 0.000026 rate, 0.602048 seconds, 488128 images, 0.080760 hours left\n",
            "Loaded: 0.000125 seconds\n",
            "\n",
            " (next mAP calculation at 7700 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7628: 0.034080, 0.052026 avg loss, 0.000026 rate, 0.621132 seconds, 488192 images, 0.080576 hours left\n",
            "Loaded: 0.000043 seconds\n",
            "\n",
            " (next mAP calculation at 7700 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7629: 0.053256, 0.052149 avg loss, 0.000026 rate, 0.623522 seconds, 488256 images, 0.080412 hours left\n",
            "Loaded: 0.000076 seconds\n",
            "\n",
            " (next mAP calculation at 7700 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7630: 0.068162, 0.053751 avg loss, 0.000026 rate, 0.620784 seconds, 488320 images, 0.080251 hours left\n",
            "Loaded: 0.000043 seconds\n",
            "\n",
            " (next mAP calculation at 7700 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7631: 0.052362, 0.053612 avg loss, 0.000026 rate, 0.605605 seconds, 488384 images, 0.080086 hours left\n",
            "Loaded: 0.000061 seconds\n",
            "\n",
            " (next mAP calculation at 7700 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7632: 0.080718, 0.056322 avg loss, 0.000026 rate, 0.645491 seconds, 488448 images, 0.079906 hours left\n",
            "Loaded: 0.000065 seconds\n",
            "\n",
            " (next mAP calculation at 7700 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7633: 0.060780, 0.056768 avg loss, 0.000026 rate, 0.576533 seconds, 488512 images, 0.079767 hours left\n",
            "Loaded: 0.000076 seconds\n",
            "\n",
            " (next mAP calculation at 7700 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7634: 0.057275, 0.056819 avg loss, 0.000026 rate, 0.625429 seconds, 488576 images, 0.079557 hours left\n",
            "Loaded: 0.000044 seconds\n",
            "\n",
            " (next mAP calculation at 7700 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7635: 0.071811, 0.058318 avg loss, 0.000026 rate, 0.588778 seconds, 488640 images, 0.079397 hours left\n",
            "Loaded: 0.000066 seconds\n",
            "\n",
            " (next mAP calculation at 7700 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7636: 0.035336, 0.056020 avg loss, 0.000026 rate, 0.606603 seconds, 488704 images, 0.079201 hours left\n",
            "Loaded: 0.000038 seconds\n",
            "\n",
            " (next mAP calculation at 7700 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7637: 0.050279, 0.055446 avg loss, 0.000026 rate, 0.586395 seconds, 488768 images, 0.079022 hours left\n",
            "Loaded: 0.000067 seconds\n",
            "\n",
            " (next mAP calculation at 7700 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7638: 0.040613, 0.053962 avg loss, 0.000026 rate, 0.652002 seconds, 488832 images, 0.078823 hours left\n",
            "Loaded: 0.000049 seconds\n",
            "\n",
            " (next mAP calculation at 7700 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7639: 0.026078, 0.051174 avg loss, 0.000026 rate, 0.637809 seconds, 488896 images, 0.078691 hours left\n",
            "Loaded: 0.000066 seconds\n",
            "\n",
            " (next mAP calculation at 7700 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7640: 0.050817, 0.051138 avg loss, 0.000026 rate, 0.611075 seconds, 488960 images, 0.078543 hours left\n",
            "Loaded: 0.000044 seconds\n",
            "\n",
            " (next mAP calculation at 7700 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7641: 0.039998, 0.050024 avg loss, 0.000026 rate, 0.646125 seconds, 489024 images, 0.078369 hours left\n",
            "Loaded: 0.000049 seconds\n",
            "\n",
            " (next mAP calculation at 7700 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7642: 0.048560, 0.049878 avg loss, 0.000026 rate, 0.585448 seconds, 489088 images, 0.078230 hours left\n",
            "Loaded: 0.000047 seconds\n",
            "\n",
            " (next mAP calculation at 7700 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7643: 0.072268, 0.052117 avg loss, 0.000026 rate, 0.623958 seconds, 489152 images, 0.078030 hours left\n",
            "Loaded: 0.000044 seconds\n",
            "\n",
            " (next mAP calculation at 7700 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7644: 0.094766, 0.056382 avg loss, 0.000026 rate, 0.633355 seconds, 489216 images, 0.077868 hours left\n",
            "Loaded: 0.000033 seconds\n",
            "\n",
            " (next mAP calculation at 7700 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7645: 0.057733, 0.056517 avg loss, 0.000026 rate, 0.619298 seconds, 489280 images, 0.077716 hours left\n",
            "Loaded: 0.000047 seconds\n",
            "\n",
            " (next mAP calculation at 7700 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7646: 0.070880, 0.057953 avg loss, 0.000026 rate, 0.601749 seconds, 489344 images, 0.077549 hours left\n",
            "Loaded: 0.000065 seconds\n",
            "\n",
            " (next mAP calculation at 7700 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7647: 0.045515, 0.056709 avg loss, 0.000026 rate, 0.579009 seconds, 489408 images, 0.077366 hours left\n",
            "Loaded: 0.000074 seconds\n",
            "\n",
            " (next mAP calculation at 7700 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7648: 0.047669, 0.055805 avg loss, 0.000026 rate, 0.621656 seconds, 489472 images, 0.077160 hours left\n",
            "Loaded: 0.000088 seconds\n",
            "\n",
            " (next mAP calculation at 7700 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7649: 0.026075, 0.052832 avg loss, 0.000026 rate, 0.641490 seconds, 489536 images, 0.076996 hours left\n",
            "Loaded: 0.000059 seconds\n",
            "\n",
            " (next mAP calculation at 7700 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7650: 0.030125, 0.050562 avg loss, 0.000026 rate, 0.639792 seconds, 489600 images, 0.076852 hours left\n",
            "Loaded: 0.000114 seconds\n",
            "\n",
            " (next mAP calculation at 7700 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7651: 0.051613, 0.050667 avg loss, 0.000026 rate, 0.632054 seconds, 489664 images, 0.076705 hours left\n",
            "Loaded: 0.000044 seconds\n",
            "\n",
            " (next mAP calculation at 7700 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7652: 0.087029, 0.054303 avg loss, 0.000026 rate, 0.600508 seconds, 489728 images, 0.076551 hours left\n",
            "Loaded: 0.000040 seconds\n",
            "\n",
            " (next mAP calculation at 7700 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7653: 0.052429, 0.054116 avg loss, 0.000026 rate, 0.646608 seconds, 489792 images, 0.076366 hours left\n",
            "Loaded: 0.000044 seconds\n",
            "\n",
            " (next mAP calculation at 7700 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7654: 0.073282, 0.056032 avg loss, 0.000026 rate, 0.593754 seconds, 489856 images, 0.076226 hours left\n",
            "Loaded: 0.000042 seconds\n",
            "\n",
            " (next mAP calculation at 7700 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7655: 0.086791, 0.059108 avg loss, 0.000026 rate, 0.573819 seconds, 489920 images, 0.076034 hours left\n",
            "Loaded: 0.000082 seconds\n",
            "\n",
            " (next mAP calculation at 7700 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7656: 0.031831, 0.056380 avg loss, 0.000026 rate, 0.598452 seconds, 489984 images, 0.075824 hours left\n",
            "Loaded: 0.000054 seconds\n",
            "\n",
            " (next mAP calculation at 7700 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7657: 0.052470, 0.055989 avg loss, 0.000026 rate, 0.621128 seconds, 490048 images, 0.075638 hours left\n",
            "Loaded: 0.000045 seconds\n",
            "\n",
            " (next mAP calculation at 7700 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7658: 0.042470, 0.054637 avg loss, 0.000026 rate, 0.638634 seconds, 490112 images, 0.075473 hours left\n",
            "Loaded: 0.000042 seconds\n",
            "\n",
            " (next mAP calculation at 7700 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7659: 0.065812, 0.055755 avg loss, 0.000026 rate, 0.603382 seconds, 490176 images, 0.075325 hours left\n",
            "Loaded: 0.000043 seconds\n",
            "\n",
            " (next mAP calculation at 7700 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7660: 0.060118, 0.056191 avg loss, 0.000026 rate, 0.611820 seconds, 490240 images, 0.075144 hours left\n",
            "Loaded: 0.000060 seconds\n",
            "\n",
            " (next mAP calculation at 7700 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7661: 0.035339, 0.054106 avg loss, 0.000026 rate, 0.667237 seconds, 490304 images, 0.074970 hours left\n",
            "Loaded: 0.000040 seconds\n",
            "\n",
            " (next mAP calculation at 7700 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7662: 0.050581, 0.053754 avg loss, 0.000026 rate, 0.579273 seconds, 490368 images, 0.074849 hours left\n",
            "Loaded: 0.000038 seconds\n",
            "\n",
            " (next mAP calculation at 7700 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7663: 0.043852, 0.052763 avg loss, 0.000026 rate, 0.584153 seconds, 490432 images, 0.074644 hours left\n",
            "Loaded: 0.000047 seconds\n",
            "\n",
            " (next mAP calculation at 7700 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7664: 0.048275, 0.052315 avg loss, 0.000026 rate, 0.595767 seconds, 490496 images, 0.074445 hours left\n",
            "Loaded: 0.000042 seconds\n",
            "\n",
            " (next mAP calculation at 7700 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7665: 0.071151, 0.054198 avg loss, 0.000026 rate, 0.637172 seconds, 490560 images, 0.074256 hours left\n",
            "Loaded: 0.000043 seconds\n",
            "\n",
            " (next mAP calculation at 7700 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7666: 0.051245, 0.053903 avg loss, 0.000026 rate, 0.591867 seconds, 490624 images, 0.074107 hours left\n",
            "Loaded: 0.000039 seconds\n",
            "\n",
            " (next mAP calculation at 7700 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7667: 0.058248, 0.054337 avg loss, 0.000026 rate, 0.637404 seconds, 490688 images, 0.073915 hours left\n",
            "Loaded: 0.000073 seconds\n",
            "\n",
            " (next mAP calculation at 7700 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7668: 0.031726, 0.052076 avg loss, 0.000026 rate, 0.641140 seconds, 490752 images, 0.073765 hours left\n",
            "Loaded: 0.000050 seconds\n",
            "\n",
            " (next mAP calculation at 7700 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7669: 0.058687, 0.052737 avg loss, 0.000026 rate, 0.602383 seconds, 490816 images, 0.073619 hours left\n",
            "Loaded: 0.000052 seconds\n",
            "\n",
            " (next mAP calculation at 7700 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7670: 0.101030, 0.057567 avg loss, 0.000026 rate, 0.617959 seconds, 490880 images, 0.073437 hours left\n",
            "Loaded: 0.000037 seconds\n",
            "\n",
            " (next mAP calculation at 7700 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7671: 0.053770, 0.057187 avg loss, 0.000026 rate, 0.633859 seconds, 490944 images, 0.073269 hours left\n",
            "Loaded: 0.000056 seconds\n",
            "\n",
            " (next mAP calculation at 7700 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7672: 0.037918, 0.055260 avg loss, 0.000026 rate, 0.617997 seconds, 491008 images, 0.073116 hours left\n",
            "Loaded: 0.000062 seconds\n",
            "\n",
            " (next mAP calculation at 7700 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7673: 0.095867, 0.059321 avg loss, 0.000026 rate, 0.587532 seconds, 491072 images, 0.072948 hours left\n",
            "Loaded: 0.000057 seconds\n",
            "\n",
            " (next mAP calculation at 7700 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7674: 0.099449, 0.063333 avg loss, 0.000026 rate, 0.611079 seconds, 491136 images, 0.072752 hours left\n",
            "Loaded: 0.000066 seconds\n",
            "\n",
            " (next mAP calculation at 7700 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7675: 0.055472, 0.062547 avg loss, 0.000026 rate, 0.566772 seconds, 491200 images, 0.072578 hours left\n",
            "Loaded: 0.000034 seconds\n",
            "\n",
            " (next mAP calculation at 7700 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7676: 0.041147, 0.060407 avg loss, 0.000026 rate, 0.584435 seconds, 491264 images, 0.072364 hours left\n",
            "Loaded: 0.000042 seconds\n",
            "\n",
            " (next mAP calculation at 7700 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7677: 0.052294, 0.059596 avg loss, 0.000026 rate, 0.669289 seconds, 491328 images, 0.072166 hours left\n",
            "Loaded: 0.000043 seconds\n",
            "\n",
            " (next mAP calculation at 7700 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7678: 0.065519, 0.060188 avg loss, 0.000026 rate, 0.634797 seconds, 491392 images, 0.072045 hours left\n",
            "Loaded: 0.000061 seconds\n",
            "\n",
            " (next mAP calculation at 7700 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7679: 0.053558, 0.059525 avg loss, 0.000026 rate, 0.591633 seconds, 491456 images, 0.071892 hours left\n",
            "Loaded: 0.000045 seconds\n",
            "\n",
            " (next mAP calculation at 7700 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7680: 0.053648, 0.058938 avg loss, 0.000026 rate, 0.632135 seconds, 491520 images, 0.071701 hours left\n",
            "Loaded: 0.000045 seconds\n",
            "\n",
            " (next mAP calculation at 7700 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7681: 0.042288, 0.057273 avg loss, 0.000026 rate, 0.645520 seconds, 491584 images, 0.071546 hours left\n",
            "Loaded: 0.000043 seconds\n",
            "\n",
            " (next mAP calculation at 7700 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7682: 0.033354, 0.054881 avg loss, 0.000026 rate, 0.640545 seconds, 491648 images, 0.071403 hours left\n",
            "Loaded: 0.000046 seconds\n",
            "\n",
            " (next mAP calculation at 7700 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7683: 0.045272, 0.053920 avg loss, 0.000026 rate, 0.606981 seconds, 491712 images, 0.071254 hours left\n",
            "Loaded: 0.000071 seconds\n",
            "\n",
            " (next mAP calculation at 7700 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7684: 0.032931, 0.051821 avg loss, 0.000026 rate, 0.606132 seconds, 491776 images, 0.071076 hours left\n",
            "Loaded: 0.000044 seconds\n",
            "\n",
            " (next mAP calculation at 7700 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7685: 0.055168, 0.052156 avg loss, 0.000026 rate, 0.626191 seconds, 491840 images, 0.070898 hours left\n",
            "Loaded: 0.000042 seconds\n",
            "\n",
            " (next mAP calculation at 7700 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7686: 0.062000, 0.053140 avg loss, 0.000026 rate, 0.595309 seconds, 491904 images, 0.070737 hours left\n",
            "Loaded: 0.000042 seconds\n",
            "\n",
            " (next mAP calculation at 7700 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7687: 0.071067, 0.054933 avg loss, 0.000026 rate, 0.582576 seconds, 491968 images, 0.070549 hours left\n",
            "Loaded: 0.000045 seconds\n",
            "\n",
            " (next mAP calculation at 7700 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7688: 0.029313, 0.052371 avg loss, 0.000026 rate, 0.553463 seconds, 492032 images, 0.070350 hours left\n",
            "Loaded: 0.000035 seconds\n",
            "\n",
            " (next mAP calculation at 7700 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7689: 0.064567, 0.053590 avg loss, 0.000026 rate, 0.638064 seconds, 492096 images, 0.070126 hours left\n",
            "Loaded: 0.000078 seconds\n",
            "\n",
            " (next mAP calculation at 7700 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7690: 0.030552, 0.051287 avg loss, 0.000026 rate, 0.594948 seconds, 492160 images, 0.069976 hours left\n",
            "Loaded: 0.000043 seconds\n",
            "\n",
            " (next mAP calculation at 7700 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7691: 0.058716, 0.052029 avg loss, 0.000026 rate, 0.633011 seconds, 492224 images, 0.069789 hours left\n",
            "Loaded: 0.000044 seconds\n",
            "\n",
            " (next mAP calculation at 7700 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7692: 0.049795, 0.051806 avg loss, 0.000026 rate, 0.536374 seconds, 492288 images, 0.069634 hours left\n",
            "Loaded: 0.000060 seconds\n",
            "\n",
            " (next mAP calculation at 7700 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7693: 0.063432, 0.052969 avg loss, 0.000026 rate, 0.614130 seconds, 492352 images, 0.069397 hours left\n",
            "Loaded: 0.000044 seconds\n",
            "\n",
            " (next mAP calculation at 7700 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7694: 0.031295, 0.050801 avg loss, 0.000026 rate, 0.646327 seconds, 492416 images, 0.069227 hours left\n",
            "Loaded: 0.000042 seconds\n",
            "\n",
            " (next mAP calculation at 7700 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7695: 0.047810, 0.050502 avg loss, 0.000026 rate, 0.608550 seconds, 492480 images, 0.069084 hours left\n",
            "Loaded: 0.000137 seconds\n",
            "\n",
            " (next mAP calculation at 7700 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7696: 0.023022, 0.047754 avg loss, 0.000026 rate, 0.622212 seconds, 492544 images, 0.068908 hours left\n",
            "Loaded: 0.000054 seconds\n",
            "\n",
            " (next mAP calculation at 7700 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7697: 0.031913, 0.046170 avg loss, 0.000026 rate, 0.636417 seconds, 492608 images, 0.068745 hours left\n",
            "Loaded: 0.000074 seconds\n",
            "\n",
            " (next mAP calculation at 7700 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7698: 0.055412, 0.047094 avg loss, 0.000026 rate, 0.636272 seconds, 492672 images, 0.068593 hours left\n",
            "Loaded: 0.000058 seconds\n",
            "\n",
            " (next mAP calculation at 7700 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7699: 0.061361, 0.048521 avg loss, 0.000026 rate, 0.636471 seconds, 492736 images, 0.068441 hours left\n",
            "Loaded: 0.000044 seconds\n",
            "\n",
            " (next mAP calculation at 7700 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7700: 0.083496, 0.052018 avg loss, 0.000026 rate, 0.577089 seconds, 492800 images, 0.068289 hours left\n",
            "\n",
            " calculation mAP (mean average precision)...\n",
            "8\n",
            " detections_count = 19, unique_truth_count = 8  \n",
            "class_id = 0, name = gundhi, ap = 0.00%   \t (TP = 0, FP = 1) \n",
            "class_id = 1, name = hispaa_insect, ap = 100.00%   \t (TP = 3, FP = 0) \n",
            "class_id = 2, name = stem_bor_lar, ap = 75.00%   \t (TP = 3, FP = 1) \n",
            "class_id = 3, name = stem_bor_moth, ap = 50.00%   \t (TP = 2, FP = 1) \n",
            "\n",
            " for conf_thresh = 0.25, precision = 0.73, recall = 1.00, F1-score = 0.84 \n",
            " for conf_thresh = 0.25, TP = 8, FP = 3, FN = 0, average IoU = 52.16 % \n",
            "\n",
            " IoU threshold = 50 %, used Area-Under-Curve for each unique Recall \n",
            " mean average precision (mAP@0.50) = 0.562500, or 56.25 % \n",
            "Total Detection Time: 0 Seconds\n",
            "\n",
            "Set -points flag:\n",
            " `-points 101` for MS COCO \n",
            " `-points 11` for PascalVOC 2007 (uncomment `difficult` in voc.data) \n",
            " `-points 0` (AUC) for ImageNet, PascalVOC 2010-2012, your custom dataset\n",
            "\n",
            " mean_average_precision (mAP@0.5) = 0.562500 \n",
            "Saving weights to backup//custom-yolov4-tiny-detector_last.weights\n",
            "Loaded: 0.000067 seconds\n",
            "\n",
            " (next mAP calculation at 7800 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7701: 0.051255, 0.051942 avg loss, 0.000026 rate, 0.623625 seconds, 492864 images, 0.068118 hours left\n",
            "Loaded: 0.000045 seconds\n",
            "\n",
            " (next mAP calculation at 7800 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7702: 0.065689, 0.053317 avg loss, 0.000026 rate, 0.620572 seconds, 492928 images, 0.067955 hours left\n",
            "Loaded: 0.000048 seconds\n",
            "\n",
            " (next mAP calculation at 7800 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7703: 0.036289, 0.051614 avg loss, 0.000026 rate, 0.652012 seconds, 492992 images, 0.067789 hours left\n",
            "Loaded: 0.000036 seconds\n",
            "\n",
            " (next mAP calculation at 7800 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7704: 0.039465, 0.050399 avg loss, 0.000026 rate, 0.647203 seconds, 493056 images, 0.067650 hours left\n",
            "Loaded: 0.000042 seconds\n",
            "\n",
            " (next mAP calculation at 7800 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7705: 0.068505, 0.052210 avg loss, 0.000026 rate, 0.610695 seconds, 493120 images, 0.067505 hours left\n",
            "Loaded: 0.000046 seconds\n",
            "\n",
            " (next mAP calculation at 7800 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7706: 0.084516, 0.055440 avg loss, 0.000026 rate, 0.593392 seconds, 493184 images, 0.067331 hours left\n",
            "Loaded: 0.000043 seconds\n",
            "\n",
            " (next mAP calculation at 7800 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7707: 0.015848, 0.051481 avg loss, 0.000026 rate, 0.640304 seconds, 493248 images, 0.067142 hours left\n",
            "Loaded: 0.000042 seconds\n",
            "\n",
            " (next mAP calculation at 7800 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7708: 0.043015, 0.050634 avg loss, 0.000026 rate, 0.607596 seconds, 493312 images, 0.066992 hours left\n",
            "Loaded: 0.000047 seconds\n",
            "\n",
            " (next mAP calculation at 7800 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7709: 0.055702, 0.051141 avg loss, 0.000026 rate, 0.596704 seconds, 493376 images, 0.066815 hours left\n",
            "Loaded: 0.000043 seconds\n",
            "\n",
            " (next mAP calculation at 7800 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7710: 0.081475, 0.054175 avg loss, 0.000026 rate, 0.585898 seconds, 493440 images, 0.066629 hours left\n",
            "Loaded: 0.000045 seconds\n",
            "\n",
            " (next mAP calculation at 7800 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7711: 0.046937, 0.053451 avg loss, 0.000026 rate, 0.615174 seconds, 493504 images, 0.066435 hours left\n",
            "Loaded: 0.000044 seconds\n",
            "\n",
            " (next mAP calculation at 7800 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7712: 0.036544, 0.051760 avg loss, 0.000026 rate, 0.588005 seconds, 493568 images, 0.066264 hours left\n",
            "Loaded: 0.000070 seconds\n",
            "\n",
            " (next mAP calculation at 7800 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7713: 0.080069, 0.054591 avg loss, 0.000026 rate, 0.587992 seconds, 493632 images, 0.066072 hours left\n",
            "Loaded: 0.000038 seconds\n",
            "\n",
            " (next mAP calculation at 7800 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7714: 0.049713, 0.054103 avg loss, 0.000026 rate, 0.664664 seconds, 493696 images, 0.065880 hours left\n",
            "Loaded: 0.000100 seconds\n",
            "\n",
            " (next mAP calculation at 7800 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7715: 0.092265, 0.057919 avg loss, 0.000026 rate, 0.661573 seconds, 493760 images, 0.065749 hours left\n",
            "Loaded: 0.000051 seconds\n",
            "\n",
            " (next mAP calculation at 7800 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7716: 0.058660, 0.057993 avg loss, 0.000026 rate, 0.597448 seconds, 493824 images, 0.065616 hours left\n",
            "Loaded: 0.000071 seconds\n",
            "\n",
            " (next mAP calculation at 7800 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7717: 0.059478, 0.058142 avg loss, 0.000026 rate, 0.596681 seconds, 493888 images, 0.065431 hours left\n",
            "Loaded: 0.000058 seconds\n",
            "\n",
            " (next mAP calculation at 7800 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7718: 0.055761, 0.057904 avg loss, 0.000026 rate, 0.638541 seconds, 493952 images, 0.065246 hours left\n",
            "Loaded: 0.000047 seconds\n",
            "\n",
            " (next mAP calculation at 7800 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7719: 0.040116, 0.056125 avg loss, 0.000026 rate, 0.618329 seconds, 494016 images, 0.065094 hours left\n",
            "Loaded: 0.000043 seconds\n",
            "\n",
            " (next mAP calculation at 7800 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7720: 0.062186, 0.056731 avg loss, 0.000026 rate, 0.568822 seconds, 494080 images, 0.064925 hours left\n",
            "Loaded: 0.000047 seconds\n",
            "\n",
            " (next mAP calculation at 7800 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7721: 0.029695, 0.054027 avg loss, 0.000026 rate, 0.611945 seconds, 494144 images, 0.064719 hours left\n",
            "Loaded: 0.000088 seconds\n",
            "\n",
            " (next mAP calculation at 7800 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7722: 0.049323, 0.053557 avg loss, 0.000026 rate, 0.637551 seconds, 494208 images, 0.064546 hours left\n",
            "Loaded: 0.000053 seconds\n",
            "\n",
            " (next mAP calculation at 7800 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7723: 0.038151, 0.052016 avg loss, 0.000026 rate, 0.604440 seconds, 494272 images, 0.064393 hours left\n",
            "Loaded: 0.000062 seconds\n",
            "\n",
            " (next mAP calculation at 7800 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7724: 0.025350, 0.049350 avg loss, 0.000026 rate, 0.621317 seconds, 494336 images, 0.064214 hours left\n",
            "Loaded: 0.000051 seconds\n",
            "\n",
            " (next mAP calculation at 7800 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7725: 0.052952, 0.049710 avg loss, 0.000026 rate, 0.646846 seconds, 494400 images, 0.064048 hours left\n",
            "Loaded: 0.000046 seconds\n",
            "\n",
            " (next mAP calculation at 7800 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7726: 0.031669, 0.047906 avg loss, 0.000026 rate, 0.581158 seconds, 494464 images, 0.063902 hours left\n",
            "Loaded: 0.000057 seconds\n",
            "\n",
            " (next mAP calculation at 7800 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7727: 0.056230, 0.048738 avg loss, 0.000026 rate, 0.622027 seconds, 494528 images, 0.063705 hours left\n",
            "Loaded: 0.000070 seconds\n",
            "\n",
            " (next mAP calculation at 7800 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7728: 0.048087, 0.048673 avg loss, 0.000026 rate, 0.683234 seconds, 494592 images, 0.063540 hours left\n",
            "Loaded: 0.000040 seconds\n",
            "\n",
            " (next mAP calculation at 7800 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7729: 0.053614, 0.049167 avg loss, 0.000026 rate, 0.595847 seconds, 494656 images, 0.063421 hours left\n",
            "Loaded: 0.000044 seconds\n",
            "\n",
            " (next mAP calculation at 7800 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7730: 0.044984, 0.048749 avg loss, 0.000026 rate, 0.612818 seconds, 494720 images, 0.063235 hours left\n",
            "Loaded: 0.000045 seconds\n",
            "\n",
            " (next mAP calculation at 7800 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7731: 0.033830, 0.047257 avg loss, 0.000026 rate, 0.582922 seconds, 494784 images, 0.063062 hours left\n",
            "Loaded: 0.000045 seconds\n",
            "\n",
            " (next mAP calculation at 7800 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7732: 0.046580, 0.047189 avg loss, 0.000026 rate, 0.588075 seconds, 494848 images, 0.062867 hours left\n",
            "Loaded: 0.000047 seconds\n",
            "\n",
            " (next mAP calculation at 7800 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7733: 0.076995, 0.050170 avg loss, 0.000026 rate, 0.589880 seconds, 494912 images, 0.062677 hours left\n",
            "Loaded: 0.000044 seconds\n",
            "\n",
            " (next mAP calculation at 7800 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7734: 0.049523, 0.050105 avg loss, 0.000026 rate, 0.638047 seconds, 494976 images, 0.062487 hours left\n",
            "Loaded: 0.000047 seconds\n",
            "\n",
            " (next mAP calculation at 7800 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7735: 0.057514, 0.050846 avg loss, 0.000026 rate, 0.610420 seconds, 495040 images, 0.062334 hours left\n",
            "Loaded: 0.000064 seconds\n",
            "\n",
            " (next mAP calculation at 7800 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7736: 0.041777, 0.049939 avg loss, 0.000026 rate, 0.645307 seconds, 495104 images, 0.062160 hours left\n",
            "Loaded: 0.000044 seconds\n",
            "\n",
            " (next mAP calculation at 7800 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7737: 0.046617, 0.049607 avg loss, 0.000026 rate, 0.610996 seconds, 495168 images, 0.062012 hours left\n",
            "Loaded: 0.000063 seconds\n",
            "\n",
            " (next mAP calculation at 7800 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7738: 0.067983, 0.051445 avg loss, 0.000026 rate, 0.621198 seconds, 495232 images, 0.061838 hours left\n",
            "Loaded: 0.000062 seconds\n",
            "\n",
            " (next mAP calculation at 7800 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7739: 0.072564, 0.053557 avg loss, 0.000026 rate, 0.596133 seconds, 495296 images, 0.061672 hours left\n",
            "Loaded: 0.000042 seconds\n",
            "\n",
            " (next mAP calculation at 7800 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7740: 0.034636, 0.051665 avg loss, 0.000026 rate, 0.612056 seconds, 495360 images, 0.061487 hours left\n",
            "Loaded: 0.000041 seconds\n",
            "\n",
            " (next mAP calculation at 7800 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7741: 0.057163, 0.052214 avg loss, 0.000026 rate, 0.618168 seconds, 495424 images, 0.061314 hours left\n",
            "Loaded: 0.000051 seconds\n",
            "\n",
            " (next mAP calculation at 7800 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7742: 0.055806, 0.052574 avg loss, 0.000026 rate, 0.644069 seconds, 495488 images, 0.061146 hours left\n",
            "Loaded: 0.000058 seconds\n",
            "\n",
            " (next mAP calculation at 7800 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7743: 0.060925, 0.053409 avg loss, 0.000026 rate, 0.633143 seconds, 495552 images, 0.060996 hours left\n",
            "Loaded: 0.000046 seconds\n",
            "\n",
            " (next mAP calculation at 7800 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7744: 0.097851, 0.057853 avg loss, 0.000026 rate, 0.619235 seconds, 495616 images, 0.060838 hours left\n",
            "Loaded: 0.000059 seconds\n",
            "\n",
            " (next mAP calculation at 7800 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7745: 0.050615, 0.057129 avg loss, 0.000026 rate, 0.596768 seconds, 495680 images, 0.060670 hours left\n",
            "Loaded: 0.000044 seconds\n",
            "\n",
            " (next mAP calculation at 7800 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7746: 0.065503, 0.057967 avg loss, 0.000026 rate, 0.596490 seconds, 495744 images, 0.060486 hours left\n",
            "Loaded: 0.000055 seconds\n",
            "\n",
            " (next mAP calculation at 7800 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7747: 0.032656, 0.055436 avg loss, 0.000026 rate, 0.636492 seconds, 495808 images, 0.060302 hours left\n",
            "Loaded: 0.000069 seconds\n",
            "\n",
            " (next mAP calculation at 7800 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7748: 0.070722, 0.056964 avg loss, 0.000026 rate, 0.643134 seconds, 495872 images, 0.060147 hours left\n",
            "Loaded: 0.000057 seconds\n",
            "\n",
            " (next mAP calculation at 7800 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7749: 0.039584, 0.055226 avg loss, 0.000026 rate, 0.574825 seconds, 495936 images, 0.059996 hours left\n",
            "Loaded: 0.000069 seconds\n",
            "\n",
            " (next mAP calculation at 7800 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7750: 0.048986, 0.054602 avg loss, 0.000026 rate, 0.572549 seconds, 496000 images, 0.059796 hours left\n",
            "Loaded: 0.000037 seconds\n",
            "\n",
            " (next mAP calculation at 7800 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7751: 0.054315, 0.054573 avg loss, 0.000026 rate, 0.548186 seconds, 496064 images, 0.059596 hours left\n",
            "Loaded: 0.000044 seconds\n",
            "\n",
            " (next mAP calculation at 7800 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7752: 0.046894, 0.053806 avg loss, 0.000026 rate, 0.593991 seconds, 496128 images, 0.059379 hours left\n",
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            "\n",
            " (next mAP calculation at 7800 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7753: 0.025677, 0.050993 avg loss, 0.000026 rate, 0.637233 seconds, 496192 images, 0.059195 hours left\n",
            "Loaded: 0.000042 seconds\n",
            "\n",
            " (next mAP calculation at 7800 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7754: 0.056029, 0.051496 avg loss, 0.000026 rate, 0.661945 seconds, 496256 images, 0.059040 hours left\n",
            "Loaded: 0.000081 seconds\n",
            "\n",
            " (next mAP calculation at 7800 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7755: 0.101434, 0.056490 avg loss, 0.000026 rate, 0.612671 seconds, 496320 images, 0.058902 hours left\n",
            "Loaded: 0.000045 seconds\n",
            "\n",
            " (next mAP calculation at 7800 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7756: 0.032741, 0.054115 avg loss, 0.000026 rate, 0.617889 seconds, 496384 images, 0.058730 hours left\n",
            "Loaded: 0.000103 seconds\n",
            "\n",
            " (next mAP calculation at 7800 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7757: 0.043039, 0.053008 avg loss, 0.000026 rate, 0.627369 seconds, 496448 images, 0.058562 hours left\n",
            "Loaded: 0.000049 seconds\n",
            "\n",
            " (next mAP calculation at 7800 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7758: 0.064579, 0.054165 avg loss, 0.000026 rate, 0.613549 seconds, 496512 images, 0.058400 hours left\n",
            "Loaded: 0.000047 seconds\n",
            "\n",
            " (next mAP calculation at 7800 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7759: 0.087341, 0.057482 avg loss, 0.000026 rate, 0.604342 seconds, 496576 images, 0.058228 hours left\n",
            "Loaded: 0.000065 seconds\n",
            "\n",
            " (next mAP calculation at 7800 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7760: 0.056569, 0.057391 avg loss, 0.000026 rate, 0.593782 seconds, 496640 images, 0.058050 hours left\n",
            "Loaded: 0.000050 seconds\n",
            "\n",
            " (next mAP calculation at 7800 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7761: 0.045177, 0.056170 avg loss, 0.000026 rate, 0.601448 seconds, 496704 images, 0.057866 hours left\n",
            "Loaded: 0.000042 seconds\n",
            "\n",
            " (next mAP calculation at 7800 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7762: 0.029561, 0.053509 avg loss, 0.000026 rate, 0.605509 seconds, 496768 images, 0.057686 hours left\n",
            "Loaded: 0.000063 seconds\n",
            "\n",
            " (next mAP calculation at 7800 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7763: 0.041636, 0.052321 avg loss, 0.000026 rate, 0.623419 seconds, 496832 images, 0.057510 hours left\n",
            "Loaded: 0.000042 seconds\n",
            "\n",
            " (next mAP calculation at 7800 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7764: 0.048526, 0.051942 avg loss, 0.000026 rate, 0.637966 seconds, 496896 images, 0.057345 hours left\n",
            "Loaded: 0.000076 seconds\n",
            "\n",
            " (next mAP calculation at 7800 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7765: 0.014249, 0.048173 avg loss, 0.000026 rate, 0.657741 seconds, 496960 images, 0.057190 hours left\n",
            "Loaded: 0.000045 seconds\n",
            "\n",
            " (next mAP calculation at 7800 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7766: 0.062019, 0.049557 avg loss, 0.000026 rate, 0.627305 seconds, 497024 images, 0.057048 hours left\n",
            "Loaded: 0.000062 seconds\n",
            "\n",
            " (next mAP calculation at 7800 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7767: 0.043085, 0.048910 avg loss, 0.000026 rate, 0.617053 seconds, 497088 images, 0.056885 hours left\n",
            "Loaded: 0.000044 seconds\n",
            "\n",
            " (next mAP calculation at 7800 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7768: 0.048044, 0.048823 avg loss, 0.000026 rate, 0.587138 seconds, 497152 images, 0.056716 hours left\n",
            "Loaded: 0.000052 seconds\n",
            "\n",
            " (next mAP calculation at 7800 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7769: 0.032400, 0.047181 avg loss, 0.000026 rate, 0.612204 seconds, 497216 images, 0.056527 hours left\n",
            "Loaded: 0.000042 seconds\n",
            "\n",
            " (next mAP calculation at 7800 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7770: 0.036733, 0.046136 avg loss, 0.000026 rate, 0.582796 seconds, 497280 images, 0.056354 hours left\n",
            "Loaded: 0.000043 seconds\n",
            "\n",
            " (next mAP calculation at 7800 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7771: 0.036180, 0.045141 avg loss, 0.000026 rate, 0.588157 seconds, 497344 images, 0.056163 hours left\n",
            "Loaded: 0.000083 seconds\n",
            "\n",
            " (next mAP calculation at 7800 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7772: 0.038338, 0.044460 avg loss, 0.000026 rate, 0.637269 seconds, 497408 images, 0.055976 hours left\n",
            "Loaded: 0.000069 seconds\n",
            "\n",
            " (next mAP calculation at 7800 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7773: 0.050728, 0.045087 avg loss, 0.000026 rate, 0.605098 seconds, 497472 images, 0.055820 hours left\n",
            "Loaded: 0.000051 seconds\n",
            "\n",
            " (next mAP calculation at 7800 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7774: 0.102049, 0.050783 avg loss, 0.000026 rate, 0.593442 seconds, 497536 images, 0.055643 hours left\n",
            "Loaded: 0.000041 seconds\n",
            "\n",
            " (next mAP calculation at 7800 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7775: 0.024889, 0.048194 avg loss, 0.000026 rate, 0.613318 seconds, 497600 images, 0.055459 hours left\n",
            "Loaded: 0.000071 seconds\n",
            "\n",
            " (next mAP calculation at 7800 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7776: 0.051946, 0.048569 avg loss, 0.000026 rate, 0.612122 seconds, 497664 images, 0.055288 hours left\n",
            "Loaded: 0.000083 seconds\n",
            "\n",
            " (next mAP calculation at 7800 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7777: 0.054876, 0.049200 avg loss, 0.000026 rate, 0.613614 seconds, 497728 images, 0.055116 hours left\n",
            "Loaded: 0.000043 seconds\n",
            "\n",
            " (next mAP calculation at 7800 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7778: 0.033398, 0.047620 avg loss, 0.000026 rate, 0.594053 seconds, 497792 images, 0.054945 hours left\n",
            "Loaded: 0.000040 seconds\n",
            "\n",
            " (next mAP calculation at 7800 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7779: 0.036009, 0.046459 avg loss, 0.000026 rate, 0.604945 seconds, 497856 images, 0.054762 hours left\n",
            "Loaded: 0.000042 seconds\n",
            "\n",
            " (next mAP calculation at 7800 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7780: 0.035635, 0.045376 avg loss, 0.000026 rate, 0.646793 seconds, 497920 images, 0.054586 hours left\n",
            "Loaded: 0.000049 seconds\n",
            "\n",
            " (next mAP calculation at 7800 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7781: 0.041950, 0.045034 avg loss, 0.000026 rate, 0.625593 seconds, 497984 images, 0.054435 hours left\n",
            "Loaded: 0.000046 seconds\n",
            "\n",
            " (next mAP calculation at 7800 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7782: 0.045124, 0.045043 avg loss, 0.000026 rate, 0.611676 seconds, 498048 images, 0.054271 hours left\n",
            "Loaded: 0.000070 seconds\n",
            "\n",
            " (next mAP calculation at 7800 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7783: 0.065195, 0.047058 avg loss, 0.000026 rate, 0.631751 seconds, 498112 images, 0.054099 hours left\n",
            "Loaded: 0.000052 seconds\n",
            "\n",
            " (next mAP calculation at 7800 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7784: 0.050748, 0.047427 avg loss, 0.000026 rate, 0.605999 seconds, 498176 images, 0.053939 hours left\n",
            "Loaded: 0.000065 seconds\n",
            "\n",
            " (next mAP calculation at 7800 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7785: 0.050994, 0.047784 avg loss, 0.000026 rate, 0.602528 seconds, 498240 images, 0.053763 hours left\n",
            "Loaded: 0.000063 seconds\n",
            "\n",
            " (next mAP calculation at 7800 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7786: 0.046505, 0.047656 avg loss, 0.000026 rate, 0.607393 seconds, 498304 images, 0.053586 hours left\n",
            "Loaded: 0.000050 seconds\n",
            "\n",
            " (next mAP calculation at 7800 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7787: 0.048745, 0.047765 avg loss, 0.000026 rate, 0.579487 seconds, 498368 images, 0.053411 hours left\n",
            "Loaded: 0.000060 seconds\n",
            "\n",
            " (next mAP calculation at 7800 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7788: 0.027748, 0.045763 avg loss, 0.000026 rate, 0.647741 seconds, 498432 images, 0.053220 hours left\n",
            "Loaded: 0.000042 seconds\n",
            "\n",
            " (next mAP calculation at 7800 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7789: 0.057884, 0.046975 avg loss, 0.000026 rate, 0.586622 seconds, 498496 images, 0.053069 hours left\n",
            "Loaded: 0.000062 seconds\n",
            "\n",
            " (next mAP calculation at 7800 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7790: 0.044884, 0.046766 avg loss, 0.000026 rate, 0.575634 seconds, 498560 images, 0.052882 hours left\n",
            "Loaded: 0.000067 seconds\n",
            "\n",
            " (next mAP calculation at 7800 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7791: 0.046815, 0.046771 avg loss, 0.000026 rate, 0.624394 seconds, 498624 images, 0.052689 hours left\n",
            "Loaded: 0.000048 seconds\n",
            "\n",
            " (next mAP calculation at 7800 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7792: 0.050786, 0.047172 avg loss, 0.000026 rate, 0.617254 seconds, 498688 images, 0.052525 hours left\n",
            "Loaded: 0.000042 seconds\n",
            "\n",
            " (next mAP calculation at 7800 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7793: 0.058869, 0.048342 avg loss, 0.000026 rate, 0.663942 seconds, 498752 images, 0.052356 hours left\n",
            "Loaded: 0.000044 seconds\n",
            "\n",
            " (next mAP calculation at 7800 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7794: 0.020180, 0.045526 avg loss, 0.000026 rate, 0.570277 seconds, 498816 images, 0.052214 hours left\n",
            "Loaded: 0.000073 seconds\n",
            "\n",
            " (next mAP calculation at 7800 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7795: 0.090997, 0.050073 avg loss, 0.000026 rate, 0.633213 seconds, 498880 images, 0.052019 hours left\n",
            "Loaded: 0.000035 seconds\n",
            "\n",
            " (next mAP calculation at 7800 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7796: 0.055230, 0.050589 avg loss, 0.000026 rate, 0.590902 seconds, 498944 images, 0.051859 hours left\n",
            "Loaded: 0.000068 seconds\n",
            "\n",
            " (next mAP calculation at 7800 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7797: 0.047902, 0.050320 avg loss, 0.000026 rate, 0.608004 seconds, 499008 images, 0.051675 hours left\n",
            "Loaded: 0.000042 seconds\n",
            "\n",
            " (next mAP calculation at 7800 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7798: 0.096041, 0.054892 avg loss, 0.000026 rate, 0.621039 seconds, 499072 images, 0.051501 hours left\n",
            "Loaded: 0.000043 seconds\n",
            "\n",
            " (next mAP calculation at 7800 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7799: 0.084660, 0.057869 avg loss, 0.000026 rate, 0.605175 seconds, 499136 images, 0.051335 hours left\n",
            "Loaded: 0.000090 seconds\n",
            "\n",
            " (next mAP calculation at 7800 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7800: 0.030348, 0.055117 avg loss, 0.000026 rate, 0.642041 seconds, 499200 images, 0.051160 hours left\n",
            "\n",
            " calculation mAP (mean average precision)...\n",
            "8\n",
            " detections_count = 18, unique_truth_count = 8  \n",
            "class_id = 0, name = gundhi, ap = 0.00%   \t (TP = 0, FP = 1) \n",
            "class_id = 1, name = hispaa_insect, ap = 100.00%   \t (TP = 3, FP = 0) \n",
            "class_id = 2, name = stem_bor_lar, ap = 75.00%   \t (TP = 3, FP = 1) \n",
            "class_id = 3, name = stem_bor_moth, ap = 50.00%   \t (TP = 2, FP = 1) \n",
            "\n",
            " for conf_thresh = 0.25, precision = 0.73, recall = 1.00, F1-score = 0.84 \n",
            " for conf_thresh = 0.25, TP = 8, FP = 3, FN = 0, average IoU = 52.16 % \n",
            "\n",
            " IoU threshold = 50 %, used Area-Under-Curve for each unique Recall \n",
            " mean average precision (mAP@0.50) = 0.562500, or 56.25 % \n",
            "Total Detection Time: 0 Seconds\n",
            "\n",
            "Set -points flag:\n",
            " `-points 101` for MS COCO \n",
            " `-points 11` for PascalVOC 2007 (uncomment `difficult` in voc.data) \n",
            " `-points 0` (AUC) for ImageNet, PascalVOC 2010-2012, your custom dataset\n",
            "\n",
            " mean_average_precision (mAP@0.5) = 0.562500 \n",
            "Saving weights to backup//custom-yolov4-tiny-detector_last.weights\n",
            "Loaded: 0.000067 seconds\n",
            "\n",
            " (next mAP calculation at 7900 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7801: 0.039028, 0.053508 avg loss, 0.000026 rate, 0.653598 seconds, 499264 images, 0.051026 hours left\n",
            "Loaded: 0.000045 seconds\n",
            "\n",
            " (next mAP calculation at 7900 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7802: 0.027095, 0.050867 avg loss, 0.000026 rate, 0.627925 seconds, 499328 images, 0.050877 hours left\n",
            "Loaded: 0.000043 seconds\n",
            "\n",
            " (next mAP calculation at 7900 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7803: 0.059081, 0.051688 avg loss, 0.000026 rate, 0.615676 seconds, 499392 images, 0.050714 hours left\n",
            "Loaded: 0.000080 seconds\n",
            "\n",
            " (next mAP calculation at 7900 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7804: 0.024641, 0.048983 avg loss, 0.000026 rate, 0.615891 seconds, 499456 images, 0.050544 hours left\n",
            "Loaded: 0.000069 seconds\n",
            "\n",
            " (next mAP calculation at 7900 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7805: 0.028174, 0.046902 avg loss, 0.000026 rate, 0.647689 seconds, 499520 images, 0.050374 hours left\n",
            "Loaded: 0.000030 seconds\n",
            "\n",
            " (next mAP calculation at 7900 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7806: 0.080059, 0.050218 avg loss, 0.000026 rate, 0.597526 seconds, 499584 images, 0.050221 hours left\n",
            "Loaded: 0.000054 seconds\n",
            "\n",
            " (next mAP calculation at 7900 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7807: 0.047946, 0.049991 avg loss, 0.000026 rate, 0.634630 seconds, 499648 images, 0.050041 hours left\n",
            "Loaded: 0.000044 seconds\n",
            "\n",
            " (next mAP calculation at 7900 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7808: 0.037296, 0.048721 avg loss, 0.000026 rate, 0.588923 seconds, 499712 images, 0.049880 hours left\n",
            "Loaded: 0.000041 seconds\n",
            "\n",
            " (next mAP calculation at 7900 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7809: 0.051507, 0.049000 avg loss, 0.000026 rate, 0.605342 seconds, 499776 images, 0.049696 hours left\n",
            "Loaded: 0.000065 seconds\n",
            "\n",
            " (next mAP calculation at 7900 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7810: 0.044777, 0.048578 avg loss, 0.000026 rate, 0.600558 seconds, 499840 images, 0.049520 hours left\n",
            "Loaded: 0.000038 seconds\n",
            "\n",
            " (next mAP calculation at 7900 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7811: 0.079444, 0.051664 avg loss, 0.000026 rate, 0.636596 seconds, 499904 images, 0.049342 hours left\n",
            "Loaded: 0.000055 seconds\n",
            "\n",
            " (next mAP calculation at 7900 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7812: 0.034082, 0.049906 avg loss, 0.000026 rate, 0.608588 seconds, 499968 images, 0.049183 hours left\n",
            "Loaded: 0.000072 seconds\n",
            "\n",
            " (next mAP calculation at 7900 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7813: 0.044006, 0.049316 avg loss, 0.000026 rate, 0.581937 seconds, 500032 images, 0.049009 hours left\n",
            "Loaded: 0.000044 seconds\n",
            "\n",
            " (next mAP calculation at 7900 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7814: 0.061847, 0.050569 avg loss, 0.000026 rate, 0.582844 seconds, 500096 images, 0.048821 hours left\n",
            "Loaded: 0.000058 seconds\n",
            "\n",
            " (next mAP calculation at 7900 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7815: 0.038404, 0.049353 avg loss, 0.000026 rate, 0.577179 seconds, 500160 images, 0.048634 hours left\n",
            "Loaded: 0.000049 seconds\n",
            "\n",
            " (next mAP calculation at 7900 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7816: 0.045846, 0.049002 avg loss, 0.000026 rate, 0.569973 seconds, 500224 images, 0.048444 hours left\n",
            "Loaded: 0.000043 seconds\n",
            "\n",
            " (next mAP calculation at 7900 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7817: 0.047548, 0.048857 avg loss, 0.000026 rate, 0.606113 seconds, 500288 images, 0.048251 hours left\n",
            "Loaded: 0.000045 seconds\n",
            "\n",
            " (next mAP calculation at 7900 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7818: 0.028189, 0.046790 avg loss, 0.000026 rate, 0.605349 seconds, 500352 images, 0.048077 hours left\n",
            "Loaded: 0.000062 seconds\n",
            "\n",
            " (next mAP calculation at 7900 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7819: 0.067804, 0.048891 avg loss, 0.000026 rate, 0.629329 seconds, 500416 images, 0.047902 hours left\n",
            "Loaded: 0.000043 seconds\n",
            "\n",
            " (next mAP calculation at 7900 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7820: 0.042534, 0.048255 avg loss, 0.000026 rate, 0.619556 seconds, 500480 images, 0.047739 hours left\n",
            "Loaded: 0.000045 seconds\n",
            "\n",
            " (next mAP calculation at 7900 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7821: 0.036233, 0.047053 avg loss, 0.000026 rate, 0.579628 seconds, 500544 images, 0.047572 hours left\n",
            "Loaded: 0.000048 seconds\n",
            "\n",
            " (next mAP calculation at 7900 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7822: 0.047873, 0.047135 avg loss, 0.000026 rate, 0.606369 seconds, 500608 images, 0.047384 hours left\n",
            "Loaded: 0.000047 seconds\n",
            "\n",
            " (next mAP calculation at 7900 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7823: 0.060302, 0.048452 avg loss, 0.000026 rate, 0.602278 seconds, 500672 images, 0.047210 hours left\n",
            "Loaded: 0.000046 seconds\n",
            "\n",
            " (next mAP calculation at 7900 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7824: 0.073825, 0.050989 avg loss, 0.000026 rate, 0.637815 seconds, 500736 images, 0.047034 hours left\n",
            "Loaded: 0.000038 seconds\n",
            "\n",
            " (next mAP calculation at 7900 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7825: 0.073048, 0.053195 avg loss, 0.000026 rate, 0.587582 seconds, 500800 images, 0.046876 hours left\n",
            "Loaded: 0.000041 seconds\n",
            "\n",
            " (next mAP calculation at 7900 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7826: 0.040506, 0.051926 avg loss, 0.000026 rate, 0.637040 seconds, 500864 images, 0.046693 hours left\n",
            "Loaded: 0.000041 seconds\n",
            "\n",
            " (next mAP calculation at 7900 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7827: 0.052065, 0.051940 avg loss, 0.000026 rate, 0.574245 seconds, 500928 images, 0.046534 hours left\n",
            "Loaded: 0.000041 seconds\n",
            "\n",
            " (next mAP calculation at 7900 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7828: 0.053429, 0.052089 avg loss, 0.000026 rate, 0.599331 seconds, 500992 images, 0.046344 hours left\n",
            "Loaded: 0.000043 seconds\n",
            "\n",
            " (next mAP calculation at 7900 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7829: 0.054591, 0.052339 avg loss, 0.000026 rate, 0.603172 seconds, 501056 images, 0.046167 hours left\n",
            "Loaded: 0.000057 seconds\n",
            "\n",
            " (next mAP calculation at 7900 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7830: 0.059334, 0.053039 avg loss, 0.000026 rate, 0.610734 seconds, 501120 images, 0.045992 hours left\n",
            "Loaded: 0.000068 seconds\n",
            "\n",
            " (next mAP calculation at 7900 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7831: 0.063596, 0.054094 avg loss, 0.000026 rate, 0.599742 seconds, 501184 images, 0.045821 hours left\n",
            "Loaded: 0.000043 seconds\n",
            "\n",
            " (next mAP calculation at 7900 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7832: 0.076056, 0.056291 avg loss, 0.000026 rate, 0.606041 seconds, 501248 images, 0.045644 hours left\n",
            "Loaded: 0.000051 seconds\n",
            "\n",
            " (next mAP calculation at 7900 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7833: 0.036559, 0.054317 avg loss, 0.000026 rate, 0.650282 seconds, 501312 images, 0.045471 hours left\n",
            "Loaded: 0.000043 seconds\n",
            "\n",
            " (next mAP calculation at 7900 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7834: 0.054220, 0.054308 avg loss, 0.000026 rate, 0.606496 seconds, 501376 images, 0.045318 hours left\n",
            "Loaded: 0.000066 seconds\n",
            "\n",
            " (next mAP calculation at 7900 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7835: 0.038226, 0.052700 avg loss, 0.000026 rate, 0.610677 seconds, 501440 images, 0.045144 hours left\n",
            "Loaded: 0.000042 seconds\n",
            "\n",
            " (next mAP calculation at 7900 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7836: 0.035212, 0.050951 avg loss, 0.000026 rate, 0.614800 seconds, 501504 images, 0.044973 hours left\n",
            "Loaded: 0.000081 seconds\n",
            "\n",
            " (next mAP calculation at 7900 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7837: 0.063238, 0.052180 avg loss, 0.000026 rate, 0.607753 seconds, 501568 images, 0.044803 hours left\n",
            "Loaded: 0.000043 seconds\n",
            "\n",
            " (next mAP calculation at 7900 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7838: 0.045850, 0.051547 avg loss, 0.000026 rate, 0.599521 seconds, 501632 images, 0.044630 hours left\n",
            "Loaded: 0.000072 seconds\n",
            "\n",
            " (next mAP calculation at 7900 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7839: 0.015560, 0.047948 avg loss, 0.000026 rate, 0.656830 seconds, 501696 images, 0.044454 hours left\n",
            "Loaded: 0.000056 seconds\n",
            "\n",
            " (next mAP calculation at 7900 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7840: 0.065107, 0.049664 avg loss, 0.000026 rate, 0.613119 seconds, 501760 images, 0.044303 hours left\n",
            "Loaded: 0.000082 seconds\n",
            "\n",
            " (next mAP calculation at 7900 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7841: 0.034682, 0.048166 avg loss, 0.000026 rate, 0.614180 seconds, 501824 images, 0.044132 hours left\n",
            "Loaded: 0.000103 seconds\n",
            "\n",
            " (next mAP calculation at 7900 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7842: 0.039915, 0.047341 avg loss, 0.000026 rate, 0.601511 seconds, 501888 images, 0.043962 hours left\n",
            "Loaded: 0.000036 seconds\n",
            "\n",
            " (next mAP calculation at 7900 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7843: 0.029159, 0.045522 avg loss, 0.000026 rate, 0.612914 seconds, 501952 images, 0.043787 hours left\n",
            "Loaded: 0.000091 seconds\n",
            "\n",
            " (next mAP calculation at 7900 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7844: 0.060612, 0.047031 avg loss, 0.000026 rate, 0.605254 seconds, 502016 images, 0.043616 hours left\n",
            "Loaded: 0.000067 seconds\n",
            "\n",
            " (next mAP calculation at 7900 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7845: 0.038443, 0.046173 avg loss, 0.000026 rate, 0.612052 seconds, 502080 images, 0.043442 hours left\n",
            "Loaded: 0.000076 seconds\n",
            "\n",
            " (next mAP calculation at 7900 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7846: 0.112750, 0.052830 avg loss, 0.000026 rate, 0.573001 seconds, 502144 images, 0.043272 hours left\n",
            "Loaded: 0.000048 seconds\n",
            "\n",
            " (next mAP calculation at 7900 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7847: 0.029511, 0.050498 avg loss, 0.000026 rate, 0.627207 seconds, 502208 images, 0.043084 hours left\n",
            "Loaded: 0.000034 seconds\n",
            "\n",
            " (next mAP calculation at 7900 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7848: 0.056940, 0.051142 avg loss, 0.000026 rate, 0.610137 seconds, 502272 images, 0.042920 hours left\n",
            "Loaded: 0.000040 seconds\n",
            "\n",
            " (next mAP calculation at 7900 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7849: 0.071501, 0.053178 avg loss, 0.000026 rate, 0.590839 seconds, 502336 images, 0.042748 hours left\n",
            "Loaded: 0.000045 seconds\n",
            "\n",
            " (next mAP calculation at 7900 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7850: 0.085139, 0.056374 avg loss, 0.000026 rate, 0.633364 seconds, 502400 images, 0.042569 hours left\n",
            "Loaded: 0.000044 seconds\n",
            "\n",
            " (next mAP calculation at 7900 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7851: 0.070334, 0.057770 avg loss, 0.000026 rate, 0.614433 seconds, 502464 images, 0.042407 hours left\n",
            "Loaded: 0.000122 seconds\n",
            "\n",
            " (next mAP calculation at 7900 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7852: 0.055914, 0.057585 avg loss, 0.000026 rate, 0.616976 seconds, 502528 images, 0.042237 hours left\n",
            "Loaded: 0.000069 seconds\n",
            "\n",
            " (next mAP calculation at 7900 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7853: 0.099727, 0.061799 avg loss, 0.000026 rate, 0.651029 seconds, 502592 images, 0.042068 hours left\n",
            "Loaded: 0.000084 seconds\n",
            "\n",
            " (next mAP calculation at 7900 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7854: 0.047393, 0.060358 avg loss, 0.000026 rate, 0.645371 seconds, 502656 images, 0.041914 hours left\n",
            "Loaded: 0.000077 seconds\n",
            "\n",
            " (next mAP calculation at 7900 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7855: 0.065570, 0.060880 avg loss, 0.000026 rate, 0.598139 seconds, 502720 images, 0.041756 hours left\n",
            "Loaded: 0.000044 seconds\n",
            "\n",
            " (next mAP calculation at 7900 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7856: 0.026556, 0.057447 avg loss, 0.000026 rate, 0.661959 seconds, 502784 images, 0.041580 hours left\n",
            "Loaded: 0.000073 seconds\n",
            "\n",
            " (next mAP calculation at 7900 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7857: 0.051773, 0.056880 avg loss, 0.000026 rate, 0.606416 seconds, 502848 images, 0.041429 hours left\n",
            "Loaded: 0.000041 seconds\n",
            "\n",
            " (next mAP calculation at 7900 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7858: 0.064794, 0.057671 avg loss, 0.000026 rate, 0.575838 seconds, 502912 images, 0.041255 hours left\n",
            "Loaded: 0.000064 seconds\n",
            "\n",
            " (next mAP calculation at 7900 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7859: 0.030448, 0.054949 avg loss, 0.000026 rate, 0.605909 seconds, 502976 images, 0.041070 hours left\n",
            "Loaded: 0.000048 seconds\n",
            "\n",
            " (next mAP calculation at 7900 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7860: 0.096287, 0.059083 avg loss, 0.000026 rate, 0.599147 seconds, 503040 images, 0.040896 hours left\n",
            "Loaded: 0.000042 seconds\n",
            "\n",
            " (next mAP calculation at 7900 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7861: 0.059914, 0.059166 avg loss, 0.000026 rate, 0.615923 seconds, 503104 images, 0.040721 hours left\n",
            "Loaded: 0.000066 seconds\n",
            "\n",
            " (next mAP calculation at 7900 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7862: 0.035359, 0.056785 avg loss, 0.000026 rate, 0.595879 seconds, 503168 images, 0.040551 hours left\n",
            "Loaded: 0.000043 seconds\n",
            "\n",
            " (next mAP calculation at 7900 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7863: 0.037569, 0.054864 avg loss, 0.000026 rate, 0.601735 seconds, 503232 images, 0.040374 hours left\n",
            "Loaded: 0.000039 seconds\n",
            "\n",
            " (next mAP calculation at 7900 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7864: 0.039505, 0.053328 avg loss, 0.000026 rate, 0.603080 seconds, 503296 images, 0.040199 hours left\n",
            "Loaded: 0.000042 seconds\n",
            "\n",
            " (next mAP calculation at 7900 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7865: 0.015428, 0.049538 avg loss, 0.000026 rate, 0.574983 seconds, 503360 images, 0.040025 hours left\n",
            "Loaded: 0.000037 seconds\n",
            "\n",
            " (next mAP calculation at 7900 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7866: 0.072445, 0.051828 avg loss, 0.000026 rate, 0.637254 seconds, 503424 images, 0.039841 hours left\n",
            "Loaded: 0.000044 seconds\n",
            "\n",
            " (next mAP calculation at 7900 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7867: 0.071827, 0.053828 avg loss, 0.000026 rate, 0.606033 seconds, 503488 images, 0.039679 hours left\n",
            "Loaded: 0.000051 seconds\n",
            "\n",
            " (next mAP calculation at 7900 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7868: 0.074926, 0.055938 avg loss, 0.000026 rate, 0.586279 seconds, 503552 images, 0.039507 hours left\n",
            "Loaded: 0.000082 seconds\n",
            "\n",
            " (next mAP calculation at 7900 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7869: 0.053035, 0.055648 avg loss, 0.000026 rate, 0.707115 seconds, 503616 images, 0.039326 hours left\n",
            "Loaded: 0.000049 seconds\n",
            "\n",
            " (next mAP calculation at 7900 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7870: 0.089577, 0.059041 avg loss, 0.000026 rate, 0.622866 seconds, 503680 images, 0.039191 hours left\n",
            "Loaded: 0.000046 seconds\n",
            "\n",
            " (next mAP calculation at 7900 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7871: 0.068188, 0.059955 avg loss, 0.000026 rate, 0.608128 seconds, 503744 images, 0.039024 hours left\n",
            "Loaded: 0.000047 seconds\n",
            "\n",
            " (next mAP calculation at 7900 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7872: 0.048249, 0.058785 avg loss, 0.000026 rate, 0.667418 seconds, 503808 images, 0.038851 hours left\n",
            "Loaded: 0.000047 seconds\n",
            "\n",
            " (next mAP calculation at 7900 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7873: 0.034497, 0.056356 avg loss, 0.000026 rate, 0.614687 seconds, 503872 images, 0.038700 hours left\n",
            "Loaded: 0.000080 seconds\n",
            "\n",
            " (next mAP calculation at 7900 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7874: 0.053178, 0.056038 avg loss, 0.000026 rate, 0.598064 seconds, 503936 images, 0.038530 hours left\n",
            "Loaded: 0.000065 seconds\n",
            "\n",
            " (next mAP calculation at 7900 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7875: 0.039266, 0.054361 avg loss, 0.000026 rate, 0.586549 seconds, 504000 images, 0.038354 hours left\n",
            "Loaded: 0.000037 seconds\n",
            "\n",
            " (next mAP calculation at 7900 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7876: 0.032003, 0.052125 avg loss, 0.000026 rate, 0.589375 seconds, 504064 images, 0.038174 hours left\n",
            "Loaded: 0.000063 seconds\n",
            "\n",
            " (next mAP calculation at 7900 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7877: 0.060737, 0.052986 avg loss, 0.000026 rate, 0.594801 seconds, 504128 images, 0.037995 hours left\n",
            "Loaded: 0.000042 seconds\n",
            "\n",
            " (next mAP calculation at 7900 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7878: 0.078487, 0.055536 avg loss, 0.000026 rate, 0.597389 seconds, 504192 images, 0.037819 hours left\n",
            "Loaded: 0.000044 seconds\n",
            "\n",
            " (next mAP calculation at 7900 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7879: 0.023803, 0.052363 avg loss, 0.000026 rate, 0.637534 seconds, 504256 images, 0.037643 hours left\n",
            "Loaded: 0.000043 seconds\n",
            "\n",
            " (next mAP calculation at 7900 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7880: 0.056761, 0.052803 avg loss, 0.000026 rate, 0.621123 seconds, 504320 images, 0.037481 hours left\n",
            "Loaded: 0.000048 seconds\n",
            "\n",
            " (next mAP calculation at 7900 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7881: 0.048550, 0.052378 avg loss, 0.000026 rate, 0.625403 seconds, 504384 images, 0.037313 hours left\n",
            "Loaded: 0.000045 seconds\n",
            "\n",
            " (next mAP calculation at 7900 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7882: 0.075974, 0.054737 avg loss, 0.000026 rate, 0.647117 seconds, 504448 images, 0.037147 hours left\n",
            "Loaded: 0.000051 seconds\n",
            "\n",
            " (next mAP calculation at 7900 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7883: 0.052356, 0.054499 avg loss, 0.000026 rate, 0.625688 seconds, 504512 images, 0.036987 hours left\n",
            "Loaded: 0.000056 seconds\n",
            "\n",
            " (next mAP calculation at 7900 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7884: 0.043941, 0.053443 avg loss, 0.000026 rate, 0.626460 seconds, 504576 images, 0.036821 hours left\n",
            "Loaded: 0.000042 seconds\n",
            "\n",
            " (next mAP calculation at 7900 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7885: 0.045436, 0.052643 avg loss, 0.000026 rate, 0.599975 seconds, 504640 images, 0.036655 hours left\n",
            "Loaded: 0.000043 seconds\n",
            "\n",
            " (next mAP calculation at 7900 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7886: 0.055451, 0.052923 avg loss, 0.000026 rate, 0.592495 seconds, 504704 images, 0.036480 hours left\n",
            "Loaded: 0.000063 seconds\n",
            "\n",
            " (next mAP calculation at 7900 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7887: 0.065664, 0.054198 avg loss, 0.000026 rate, 0.604152 seconds, 504768 images, 0.036303 hours left\n",
            "Loaded: 0.000063 seconds\n",
            "\n",
            " (next mAP calculation at 7900 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7888: 0.022715, 0.051049 avg loss, 0.000026 rate, 0.606311 seconds, 504832 images, 0.036129 hours left\n",
            "Loaded: 0.000045 seconds\n",
            "\n",
            " (next mAP calculation at 7900 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7889: 0.066292, 0.052574 avg loss, 0.000026 rate, 0.655967 seconds, 504896 images, 0.035957 hours left\n",
            "Loaded: 0.000064 seconds\n",
            "\n",
            " (next mAP calculation at 7900 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7890: 0.054058, 0.052722 avg loss, 0.000026 rate, 0.635006 seconds, 504960 images, 0.035799 hours left\n",
            "Loaded: 0.000045 seconds\n",
            "\n",
            " (next mAP calculation at 7900 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7891: 0.026098, 0.050060 avg loss, 0.000026 rate, 0.623589 seconds, 505024 images, 0.035635 hours left\n",
            "Loaded: 0.000046 seconds\n",
            "\n",
            " (next mAP calculation at 7900 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7892: 0.069981, 0.052052 avg loss, 0.000026 rate, 0.666045 seconds, 505088 images, 0.035468 hours left\n",
            "Loaded: 0.000050 seconds\n",
            "\n",
            " (next mAP calculation at 7900 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7893: 0.062429, 0.053089 avg loss, 0.000026 rate, 0.597099 seconds, 505152 images, 0.035313 hours left\n",
            "Loaded: 0.000040 seconds\n",
            "\n",
            " (next mAP calculation at 7900 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7894: 0.041342, 0.051915 avg loss, 0.000026 rate, 0.616658 seconds, 505216 images, 0.035137 hours left\n",
            "Loaded: 0.000055 seconds\n",
            "\n",
            " (next mAP calculation at 7900 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7895: 0.037744, 0.050498 avg loss, 0.000026 rate, 0.611344 seconds, 505280 images, 0.034968 hours left\n",
            "Loaded: 0.000043 seconds\n",
            "\n",
            " (next mAP calculation at 7900 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7896: 0.061094, 0.051557 avg loss, 0.000026 rate, 0.594926 seconds, 505344 images, 0.034796 hours left\n",
            "Loaded: 0.000066 seconds\n",
            "\n",
            " (next mAP calculation at 7900 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7897: 0.048250, 0.051227 avg loss, 0.000026 rate, 0.627430 seconds, 505408 images, 0.034620 hours left\n",
            "Loaded: 0.000075 seconds\n",
            "\n",
            " (next mAP calculation at 7900 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7898: 0.052939, 0.051398 avg loss, 0.000026 rate, 0.582784 seconds, 505472 images, 0.034453 hours left\n",
            "Loaded: 0.000041 seconds\n",
            "\n",
            " (next mAP calculation at 7900 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7899: 0.048036, 0.051062 avg loss, 0.000026 rate, 0.597284 seconds, 505536 images, 0.034274 hours left\n",
            "Loaded: 0.000041 seconds\n",
            "\n",
            " (next mAP calculation at 7900 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7900: 0.043801, 0.050336 avg loss, 0.000026 rate, 0.573783 seconds, 505600 images, 0.034099 hours left\n",
            "\n",
            " calculation mAP (mean average precision)...\n",
            "8\n",
            " detections_count = 18, unique_truth_count = 8  \n",
            "class_id = 0, name = gundhi, ap = 0.00%   \t (TP = 0, FP = 1) \n",
            "class_id = 1, name = hispaa_insect, ap = 100.00%   \t (TP = 3, FP = 0) \n",
            "class_id = 2, name = stem_bor_lar, ap = 75.00%   \t (TP = 3, FP = 1) \n",
            "class_id = 3, name = stem_bor_moth, ap = 50.00%   \t (TP = 2, FP = 1) \n",
            "\n",
            " for conf_thresh = 0.25, precision = 0.73, recall = 1.00, F1-score = 0.84 \n",
            " for conf_thresh = 0.25, TP = 8, FP = 3, FN = 0, average IoU = 52.16 % \n",
            "\n",
            " IoU threshold = 50 %, used Area-Under-Curve for each unique Recall \n",
            " mean average precision (mAP@0.50) = 0.562500, or 56.25 % \n",
            "Total Detection Time: 0 Seconds\n",
            "\n",
            "Set -points flag:\n",
            " `-points 101` for MS COCO \n",
            " `-points 11` for PascalVOC 2007 (uncomment `difficult` in voc.data) \n",
            " `-points 0` (AUC) for ImageNet, PascalVOC 2010-2012, your custom dataset\n",
            "\n",
            " mean_average_precision (mAP@0.5) = 0.562500 \n",
            "Saving weights to backup//custom-yolov4-tiny-detector_last.weights\n",
            "Loaded: 0.000113 seconds\n",
            "\n",
            " (next mAP calculation at 8000 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7901: 0.052593, 0.050561 avg loss, 0.000026 rate, 0.652763 seconds, 505664 images, 0.033928 hours left\n",
            "Loaded: 0.000049 seconds\n",
            "\n",
            " (next mAP calculation at 8000 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7902: 0.074326, 0.052938 avg loss, 0.000026 rate, 0.649946 seconds, 505728 images, 0.033768 hours left\n",
            "Loaded: 0.000047 seconds\n",
            "\n",
            " (next mAP calculation at 8000 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7903: 0.051232, 0.052767 avg loss, 0.000026 rate, 0.600072 seconds, 505792 images, 0.033607 hours left\n",
            "Loaded: 0.000063 seconds\n",
            "\n",
            " (next mAP calculation at 8000 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7904: 0.079916, 0.055482 avg loss, 0.000026 rate, 0.619904 seconds, 505856 images, 0.033433 hours left\n",
            "Loaded: 0.000042 seconds\n",
            "\n",
            " (next mAP calculation at 8000 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7905: 0.035716, 0.053506 avg loss, 0.000026 rate, 0.604060 seconds, 505920 images, 0.033264 hours left\n",
            "Loaded: 0.000057 seconds\n",
            "\n",
            " (next mAP calculation at 8000 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7906: 0.059590, 0.054114 avg loss, 0.000026 rate, 0.596213 seconds, 505984 images, 0.033091 hours left\n",
            "Loaded: 0.000079 seconds\n",
            "\n",
            " (next mAP calculation at 8000 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7907: 0.052359, 0.053939 avg loss, 0.000026 rate, 0.622712 seconds, 506048 images, 0.032915 hours left\n",
            "Loaded: 0.000069 seconds\n",
            "\n",
            " (next mAP calculation at 8000 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7908: 0.041906, 0.052735 avg loss, 0.000026 rate, 0.624358 seconds, 506112 images, 0.032747 hours left\n",
            "Loaded: 0.000055 seconds\n",
            "\n",
            " (next mAP calculation at 8000 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7909: 0.081248, 0.055586 avg loss, 0.000026 rate, 0.590356 seconds, 506176 images, 0.032579 hours left\n",
            "Loaded: 0.000032 seconds\n",
            "\n",
            " (next mAP calculation at 8000 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7910: 0.060292, 0.056057 avg loss, 0.000026 rate, 0.635145 seconds, 506240 images, 0.032403 hours left\n",
            "Loaded: 0.000066 seconds\n",
            "\n",
            " (next mAP calculation at 8000 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7911: 0.029058, 0.053357 avg loss, 0.000026 rate, 0.592323 seconds, 506304 images, 0.032238 hours left\n",
            "Loaded: 0.000077 seconds\n",
            "\n",
            " (next mAP calculation at 8000 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7912: 0.063662, 0.054388 avg loss, 0.000026 rate, 0.652345 seconds, 506368 images, 0.032062 hours left\n",
            "Loaded: 0.000045 seconds\n",
            "\n",
            " (next mAP calculation at 8000 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7913: 0.067793, 0.055728 avg loss, 0.000026 rate, 0.608771 seconds, 506432 images, 0.031900 hours left\n",
            "Loaded: 0.000045 seconds\n",
            "\n",
            " (next mAP calculation at 8000 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7914: 0.029687, 0.053124 avg loss, 0.000026 rate, 0.634533 seconds, 506496 images, 0.031729 hours left\n",
            "Loaded: 0.000077 seconds\n",
            "\n",
            " (next mAP calculation at 8000 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7915: 0.041498, 0.051961 avg loss, 0.000026 rate, 0.595949 seconds, 506560 images, 0.031563 hours left\n",
            "Loaded: 0.000046 seconds\n",
            "\n",
            " (next mAP calculation at 8000 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7916: 0.044985, 0.051264 avg loss, 0.000026 rate, 0.624045 seconds, 506624 images, 0.031388 hours left\n",
            "Loaded: 0.000053 seconds\n",
            "\n",
            " (next mAP calculation at 8000 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7917: 0.051423, 0.051280 avg loss, 0.000026 rate, 0.615359 seconds, 506688 images, 0.031220 hours left\n",
            "Loaded: 0.000070 seconds\n",
            "\n",
            " (next mAP calculation at 8000 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7918: 0.045207, 0.050673 avg loss, 0.000026 rate, 0.604495 seconds, 506752 images, 0.031049 hours left\n",
            "Loaded: 0.000069 seconds\n",
            "\n",
            " (next mAP calculation at 8000 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7919: 0.051509, 0.050756 avg loss, 0.000026 rate, 0.610971 seconds, 506816 images, 0.030877 hours left\n",
            "Loaded: 0.000064 seconds\n",
            "\n",
            " (next mAP calculation at 8000 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7920: 0.067772, 0.052458 avg loss, 0.000026 rate, 0.619025 seconds, 506880 images, 0.030705 hours left\n",
            "Loaded: 0.000050 seconds\n",
            "\n",
            " (next mAP calculation at 8000 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7921: 0.021702, 0.049382 avg loss, 0.000026 rate, 0.627293 seconds, 506944 images, 0.030536 hours left\n",
            "Loaded: 0.000107 seconds\n",
            "\n",
            " (next mAP calculation at 8000 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7922: 0.054274, 0.049871 avg loss, 0.000026 rate, 0.604453 seconds, 507008 images, 0.030368 hours left\n",
            "Loaded: 0.000070 seconds\n",
            "\n",
            " (next mAP calculation at 8000 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7923: 0.019642, 0.046848 avg loss, 0.000026 rate, 0.685863 seconds, 507072 images, 0.030196 hours left\n",
            "Loaded: 0.000056 seconds\n",
            "\n",
            " (next mAP calculation at 8000 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7924: 0.047007, 0.046864 avg loss, 0.000026 rate, 0.641580 seconds, 507136 images, 0.030040 hours left\n",
            "Loaded: 0.000043 seconds\n",
            "\n",
            " (next mAP calculation at 8000 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7925: 0.058052, 0.047983 avg loss, 0.000026 rate, 0.616399 seconds, 507200 images, 0.029875 hours left\n",
            "Loaded: 0.000059 seconds\n",
            "\n",
            " (next mAP calculation at 8000 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7926: 0.022189, 0.045404 avg loss, 0.000026 rate, 0.619161 seconds, 507264 images, 0.029705 hours left\n",
            "Loaded: 0.000086 seconds\n",
            "\n",
            " (next mAP calculation at 8000 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7927: 0.026769, 0.043540 avg loss, 0.000026 rate, 0.682024 seconds, 507328 images, 0.029535 hours left\n",
            "Loaded: 0.000060 seconds\n",
            "\n",
            " (next mAP calculation at 8000 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7928: 0.046366, 0.043823 avg loss, 0.000026 rate, 0.592579 seconds, 507392 images, 0.029378 hours left\n",
            "Loaded: 0.000044 seconds\n",
            "\n",
            " (next mAP calculation at 8000 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7929: 0.074184, 0.046859 avg loss, 0.000026 rate, 0.648183 seconds, 507456 images, 0.029203 hours left\n",
            "Loaded: 0.000042 seconds\n",
            "\n",
            " (next mAP calculation at 8000 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7930: 0.060236, 0.048197 avg loss, 0.000026 rate, 0.626267 seconds, 507520 images, 0.029039 hours left\n",
            "Loaded: 0.000073 seconds\n",
            "\n",
            " (next mAP calculation at 8000 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7931: 0.074561, 0.050833 avg loss, 0.000026 rate, 0.581459 seconds, 507584 images, 0.028870 hours left\n",
            "Loaded: 0.000045 seconds\n",
            "\n",
            " (next mAP calculation at 8000 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7932: 0.054922, 0.051242 avg loss, 0.000026 rate, 0.608584 seconds, 507648 images, 0.028693 hours left\n",
            "Loaded: 0.000050 seconds\n",
            "\n",
            " (next mAP calculation at 8000 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7933: 0.058100, 0.051928 avg loss, 0.000026 rate, 0.572540 seconds, 507712 images, 0.028521 hours left\n",
            "Loaded: 0.000068 seconds\n",
            "\n",
            " (next mAP calculation at 8000 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7934: 0.076849, 0.054420 avg loss, 0.000026 rate, 0.582378 seconds, 507776 images, 0.028342 hours left\n",
            "Loaded: 0.000041 seconds\n",
            "\n",
            " (next mAP calculation at 8000 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7935: 0.055078, 0.054486 avg loss, 0.000026 rate, 0.581663 seconds, 507840 images, 0.028166 hours left\n",
            "Loaded: 0.000042 seconds\n",
            "\n",
            " (next mAP calculation at 8000 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7936: 0.029457, 0.051983 avg loss, 0.000026 rate, 0.601652 seconds, 507904 images, 0.027989 hours left\n",
            "Loaded: 0.000041 seconds\n",
            "\n",
            " (next mAP calculation at 8000 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7937: 0.036500, 0.050435 avg loss, 0.000026 rate, 0.592859 seconds, 507968 images, 0.027816 hours left\n",
            "Loaded: 0.000032 seconds\n",
            "\n",
            " (next mAP calculation at 8000 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7938: 0.073229, 0.052714 avg loss, 0.000026 rate, 0.574453 seconds, 508032 images, 0.027642 hours left\n",
            "Loaded: 0.000045 seconds\n",
            "\n",
            " (next mAP calculation at 8000 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7939: 0.049027, 0.052345 avg loss, 0.000026 rate, 0.649404 seconds, 508096 images, 0.027464 hours left\n",
            "Loaded: 0.000049 seconds\n",
            "\n",
            " (next mAP calculation at 8000 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7940: 0.058682, 0.052979 avg loss, 0.000026 rate, 0.640947 seconds, 508160 images, 0.027300 hours left\n",
            "Loaded: 0.000082 seconds\n",
            "\n",
            " (next mAP calculation at 8000 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7941: 0.046850, 0.052366 avg loss, 0.000026 rate, 0.614412 seconds, 508224 images, 0.027134 hours left\n",
            "Loaded: 0.000042 seconds\n",
            "\n",
            " (next mAP calculation at 8000 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7942: 0.027972, 0.049927 avg loss, 0.000026 rate, 0.595962 seconds, 508288 images, 0.026963 hours left\n",
            "Loaded: 0.000046 seconds\n",
            "\n",
            " (next mAP calculation at 8000 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7943: 0.071699, 0.052104 avg loss, 0.000026 rate, 0.604792 seconds, 508352 images, 0.026789 hours left\n",
            "Loaded: 0.000066 seconds\n",
            "\n",
            " (next mAP calculation at 8000 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7944: 0.034882, 0.050382 avg loss, 0.000026 rate, 0.608801 seconds, 508416 images, 0.026617 hours left\n",
            "Loaded: 0.000036 seconds\n",
            "\n",
            " (next mAP calculation at 8000 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7945: 0.021068, 0.047450 avg loss, 0.000026 rate, 0.583952 seconds, 508480 images, 0.026446 hours left\n",
            "Loaded: 0.000045 seconds\n",
            "\n",
            " (next mAP calculation at 8000 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7946: 0.033292, 0.046034 avg loss, 0.000026 rate, 0.633463 seconds, 508544 images, 0.026270 hours left\n",
            "Loaded: 0.000069 seconds\n",
            "\n",
            " (next mAP calculation at 8000 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7947: 0.068185, 0.048249 avg loss, 0.000026 rate, 0.616943 seconds, 508608 images, 0.026103 hours left\n",
            "Loaded: 0.000071 seconds\n",
            "\n",
            " (next mAP calculation at 8000 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7948: 0.065274, 0.049952 avg loss, 0.000026 rate, 0.634984 seconds, 508672 images, 0.025933 hours left\n",
            "Loaded: 0.000044 seconds\n",
            "\n",
            " (next mAP calculation at 8000 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7949: 0.043477, 0.049304 avg loss, 0.000026 rate, 0.645466 seconds, 508736 images, 0.025765 hours left\n",
            "Loaded: 0.000046 seconds\n",
            "\n",
            " (next mAP calculation at 8000 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7950: 0.056222, 0.049996 avg loss, 0.000026 rate, 0.627521 seconds, 508800 images, 0.025599 hours left\n",
            "Loaded: 0.000137 seconds\n",
            "\n",
            " (next mAP calculation at 8000 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7951: 0.044559, 0.049452 avg loss, 0.000026 rate, 0.563880 seconds, 508864 images, 0.025430 hours left\n",
            "Loaded: 0.000072 seconds\n",
            "\n",
            " (next mAP calculation at 8000 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7952: 0.033250, 0.047832 avg loss, 0.000026 rate, 0.609238 seconds, 508928 images, 0.025252 hours left\n",
            "Loaded: 0.000043 seconds\n",
            "\n",
            " (next mAP calculation at 8000 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7953: 0.070527, 0.050102 avg loss, 0.000026 rate, 0.631171 seconds, 508992 images, 0.025081 hours left\n",
            "Loaded: 0.000065 seconds\n",
            "\n",
            " (next mAP calculation at 8000 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7954: 0.060192, 0.051111 avg loss, 0.000026 rate, 0.606311 seconds, 509056 images, 0.024913 hours left\n",
            "Loaded: 0.000065 seconds\n",
            "\n",
            " (next mAP calculation at 8000 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7955: 0.076120, 0.053612 avg loss, 0.000026 rate, 0.582556 seconds, 509120 images, 0.024741 hours left\n",
            "Loaded: 0.000049 seconds\n",
            "\n",
            " (next mAP calculation at 8000 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7956: 0.044115, 0.052662 avg loss, 0.000026 rate, 0.639549 seconds, 509184 images, 0.024567 hours left\n",
            "Loaded: 0.000043 seconds\n",
            "\n",
            " (next mAP calculation at 8000 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7957: 0.079757, 0.055372 avg loss, 0.000026 rate, 0.545500 seconds, 509248 images, 0.024399 hours left\n",
            "Loaded: 0.000053 seconds\n",
            "\n",
            " (next mAP calculation at 8000 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7958: 0.038598, 0.053694 avg loss, 0.000026 rate, 0.596208 seconds, 509312 images, 0.024220 hours left\n",
            "Loaded: 0.000056 seconds\n",
            "\n",
            " (next mAP calculation at 8000 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7959: 0.037724, 0.052097 avg loss, 0.000026 rate, 0.613942 seconds, 509376 images, 0.024048 hours left\n",
            "Loaded: 0.000051 seconds\n",
            "\n",
            " (next mAP calculation at 8000 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7960: 0.070277, 0.053915 avg loss, 0.000026 rate, 0.604628 seconds, 509440 images, 0.023877 hours left\n",
            "Loaded: 0.000041 seconds\n",
            "\n",
            " (next mAP calculation at 8000 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7961: 0.052749, 0.053798 avg loss, 0.000026 rate, 0.622595 seconds, 509504 images, 0.023705 hours left\n",
            "Loaded: 0.000041 seconds\n",
            "\n",
            " (next mAP calculation at 8000 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7962: 0.068592, 0.055278 avg loss, 0.000026 rate, 0.588635 seconds, 509568 images, 0.023536 hours left\n",
            "Loaded: 0.000035 seconds\n",
            "\n",
            " (next mAP calculation at 8000 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7963: 0.064803, 0.056230 avg loss, 0.000026 rate, 0.617133 seconds, 509632 images, 0.023363 hours left\n",
            "Loaded: 0.000064 seconds\n",
            "\n",
            " (next mAP calculation at 8000 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7964: 0.051942, 0.055801 avg loss, 0.000026 rate, 0.580417 seconds, 509696 images, 0.023192 hours left\n",
            "Loaded: 0.000044 seconds\n",
            "\n",
            " (next mAP calculation at 8000 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7965: 0.058503, 0.056072 avg loss, 0.000026 rate, 0.619965 seconds, 509760 images, 0.023019 hours left\n",
            "Loaded: 0.000045 seconds\n",
            "\n",
            " (next mAP calculation at 8000 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7966: 0.050042, 0.055469 avg loss, 0.000026 rate, 0.622532 seconds, 509824 images, 0.022849 hours left\n",
            "Loaded: 0.000053 seconds\n",
            "\n",
            " (next mAP calculation at 8000 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7967: 0.031129, 0.053035 avg loss, 0.000026 rate, 0.674258 seconds, 509888 images, 0.022679 hours left\n",
            "Loaded: 0.000049 seconds\n",
            "\n",
            " (next mAP calculation at 8000 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7968: 0.038547, 0.051586 avg loss, 0.000026 rate, 0.596878 seconds, 509952 images, 0.022514 hours left\n",
            "Loaded: 0.000059 seconds\n",
            "\n",
            " (next mAP calculation at 8000 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7969: 0.091111, 0.055538 avg loss, 0.000026 rate, 0.658652 seconds, 510016 images, 0.022342 hours left\n",
            "Loaded: 0.000043 seconds\n",
            "\n",
            " (next mAP calculation at 8000 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7970: 0.057156, 0.055700 avg loss, 0.000026 rate, 0.605248 seconds, 510080 images, 0.022175 hours left\n",
            "Loaded: 0.000058 seconds\n",
            "\n",
            " (next mAP calculation at 8000 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7971: 0.027577, 0.052888 avg loss, 0.000026 rate, 0.605431 seconds, 510144 images, 0.022004 hours left\n",
            "Loaded: 0.000041 seconds\n",
            "\n",
            " (next mAP calculation at 8000 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7972: 0.043902, 0.051989 avg loss, 0.000026 rate, 0.602561 seconds, 510208 images, 0.021833 hours left\n",
            "Loaded: 0.000073 seconds\n",
            "\n",
            " (next mAP calculation at 8000 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7973: 0.078274, 0.054618 avg loss, 0.000026 rate, 0.583167 seconds, 510272 images, 0.021661 hours left\n",
            "Loaded: 0.000040 seconds\n",
            "\n",
            " (next mAP calculation at 8000 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7974: 0.060191, 0.055175 avg loss, 0.000026 rate, 0.614990 seconds, 510336 images, 0.021488 hours left\n",
            "Loaded: 0.000059 seconds\n",
            "\n",
            " (next mAP calculation at 8000 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7975: 0.047205, 0.054378 avg loss, 0.000026 rate, 0.597187 seconds, 510400 images, 0.021318 hours left\n",
            "Loaded: 0.000042 seconds\n",
            "\n",
            " (next mAP calculation at 8000 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7976: 0.047620, 0.053702 avg loss, 0.000026 rate, 0.658711 seconds, 510464 images, 0.021146 hours left\n",
            "Loaded: 0.000046 seconds\n",
            "\n",
            " (next mAP calculation at 8000 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7977: 0.094116, 0.057744 avg loss, 0.000026 rate, 0.597066 seconds, 510528 images, 0.020979 hours left\n",
            "Loaded: 0.000040 seconds\n",
            "\n",
            " (next mAP calculation at 8000 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7978: 0.033229, 0.055292 avg loss, 0.000026 rate, 0.617729 seconds, 510592 images, 0.020807 hours left\n",
            "Loaded: 0.000064 seconds\n",
            "\n",
            " (next mAP calculation at 8000 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7979: 0.038501, 0.053613 avg loss, 0.000026 rate, 0.605217 seconds, 510656 images, 0.020637 hours left\n",
            "Loaded: 0.000053 seconds\n",
            "\n",
            " (next mAP calculation at 8000 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7980: 0.086272, 0.056879 avg loss, 0.000026 rate, 0.587754 seconds, 510720 images, 0.020466 hours left\n",
            "Loaded: 0.000038 seconds\n",
            "\n",
            " (next mAP calculation at 8000 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7981: 0.059141, 0.057105 avg loss, 0.000026 rate, 0.609972 seconds, 510784 images, 0.020294 hours left\n",
            "Loaded: 0.000094 seconds\n",
            "\n",
            " (next mAP calculation at 8000 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7982: 0.044360, 0.055831 avg loss, 0.000026 rate, 0.600738 seconds, 510848 images, 0.020123 hours left\n",
            "Loaded: 0.000058 seconds\n",
            "\n",
            " (next mAP calculation at 8000 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7983: 0.046302, 0.054878 avg loss, 0.000026 rate, 0.578774 seconds, 510912 images, 0.019952 hours left\n",
            "Loaded: 0.000044 seconds\n",
            "\n",
            " (next mAP calculation at 8000 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7984: 0.072247, 0.056615 avg loss, 0.000026 rate, 0.587954 seconds, 510976 images, 0.019780 hours left\n",
            "Loaded: 0.000045 seconds\n",
            "\n",
            " (next mAP calculation at 8000 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7985: 0.048720, 0.055825 avg loss, 0.000026 rate, 0.643706 seconds, 511040 images, 0.019608 hours left\n",
            "Loaded: 0.000068 seconds\n",
            "\n",
            " (next mAP calculation at 8000 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7986: 0.059716, 0.056214 avg loss, 0.000026 rate, 0.636998 seconds, 511104 images, 0.019439 hours left\n",
            "Loaded: 0.000042 seconds\n",
            "\n",
            " (next mAP calculation at 8000 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7987: 0.049994, 0.055592 avg loss, 0.000026 rate, 0.584090 seconds, 511168 images, 0.019269 hours left\n",
            "Loaded: 0.000044 seconds\n",
            "\n",
            " (next mAP calculation at 8000 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7988: 0.044462, 0.054479 avg loss, 0.000026 rate, 0.607956 seconds, 511232 images, 0.019097 hours left\n",
            "Loaded: 0.000045 seconds\n",
            "\n",
            " (next mAP calculation at 8000 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7989: 0.030953, 0.052127 avg loss, 0.000026 rate, 0.617474 seconds, 511296 images, 0.018927 hours left\n",
            "Loaded: 0.000047 seconds\n",
            "\n",
            " (next mAP calculation at 8000 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7990: 0.047348, 0.051649 avg loss, 0.000026 rate, 0.631357 seconds, 511360 images, 0.018756 hours left\n",
            "Loaded: 0.000070 seconds\n",
            "\n",
            " (next mAP calculation at 8000 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7991: 0.047642, 0.051248 avg loss, 0.000026 rate, 0.618481 seconds, 511424 images, 0.018586 hours left\n",
            "Loaded: 0.000040 seconds\n",
            "\n",
            " (next mAP calculation at 8000 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7992: 0.050582, 0.051181 avg loss, 0.000026 rate, 0.605949 seconds, 511488 images, 0.018416 hours left\n",
            "Loaded: 0.000042 seconds\n",
            "\n",
            " (next mAP calculation at 8000 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7993: 0.049817, 0.051045 avg loss, 0.000026 rate, 0.626216 seconds, 511552 images, 0.018245 hours left\n",
            "Loaded: 0.000072 seconds\n",
            "\n",
            " (next mAP calculation at 8000 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7994: 0.054113, 0.051352 avg loss, 0.000026 rate, 0.617862 seconds, 511616 images, 0.018075 hours left\n",
            "Loaded: 0.000046 seconds\n",
            "\n",
            " (next mAP calculation at 8000 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7995: 0.078125, 0.054029 avg loss, 0.000026 rate, 0.569300 seconds, 511680 images, 0.017904 hours left\n",
            "Loaded: 0.000043 seconds\n",
            "\n",
            " (next mAP calculation at 8000 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7996: 0.049797, 0.053606 avg loss, 0.000026 rate, 0.632217 seconds, 511744 images, 0.017733 hours left\n",
            "Loaded: 0.000042 seconds\n",
            "\n",
            " (next mAP calculation at 8000 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7997: 0.026509, 0.050896 avg loss, 0.000026 rate, 0.608897 seconds, 511808 images, 0.017563 hours left\n",
            "Loaded: 0.000043 seconds\n",
            "\n",
            " (next mAP calculation at 8000 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7998: 0.065384, 0.052345 avg loss, 0.000026 rate, 0.623346 seconds, 511872 images, 0.017392 hours left\n",
            "Loaded: 0.000065 seconds\n",
            "\n",
            " (next mAP calculation at 8000 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 7999: 0.114270, 0.058538 avg loss, 0.000026 rate, 0.596690 seconds, 511936 images, 0.017222 hours left\n",
            "Loaded: 0.000043 seconds\n",
            "\n",
            " (next mAP calculation at 8000 iterations) \n",
            " Last accuracy mAP@0.5 = 56.25 %, best = 62.50 % \n",
            " 8000: 0.046856, 0.057369 avg loss, 0.000026 rate, 0.630900 seconds, 512000 images, 0.017051 hours left\n",
            "\n",
            " calculation mAP (mean average precision)...\n",
            "8\n",
            " detections_count = 18, unique_truth_count = 8  \n",
            "class_id = 0, name = gundhi, ap = 0.00%   \t (TP = 0, FP = 1) \n",
            "class_id = 1, name = hispaa_insect, ap = 100.00%   \t (TP = 3, FP = 0) \n",
            "class_id = 2, name = stem_bor_lar, ap = 75.00%   \t (TP = 3, FP = 1) \n",
            "class_id = 3, name = stem_bor_moth, ap = 50.00%   \t (TP = 2, FP = 1) \n",
            "\n",
            " for conf_thresh = 0.25, precision = 0.73, recall = 1.00, F1-score = 0.84 \n",
            " for conf_thresh = 0.25, TP = 8, FP = 3, FN = 0, average IoU = 52.20 % \n",
            "\n",
            " IoU threshold = 50 %, used Area-Under-Curve for each unique Recall \n",
            " mean average precision (mAP@0.50) = 0.562500, or 56.25 % \n",
            "Total Detection Time: 0 Seconds\n",
            "\n",
            "Set -points flag:\n",
            " `-points 101` for MS COCO \n",
            " `-points 11` for PascalVOC 2007 (uncomment `difficult` in voc.data) \n",
            " `-points 0` (AUC) for ImageNet, PascalVOC 2010-2012, your custom dataset\n",
            "\n",
            " mean_average_precision (mAP@0.5) = 0.562500 \n",
            "Saving weights to backup//custom-yolov4-tiny-detector_8000.weights\n",
            "Saving weights to backup//custom-yolov4-tiny-detector_last.weights\n",
            "Saving weights to backup//custom-yolov4-tiny-detector_final.weights\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nBnwpBV5ZXxQ"
      },
      "source": [
        "## Infer Custom Objects with Saved YOLOv4 Weights"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FzoJQQw8Zdco"
      },
      "source": [
        "#define utility function\n",
        "def imShow(path):\n",
        "  import cv2\n",
        "  import matplotlib.pyplot as plt\n",
        "  %matplotlib inline\n",
        "\n",
        "  image = cv2.imread(path)\n",
        "  height, width = image.shape[:2]\n",
        "  resized_image = cv2.resize(image,(3*width, 3*height), interpolation = cv2.INTER_CUBIC)\n",
        "\n",
        "  fig = plt.gcf()\n",
        "  fig.set_size_inches(18, 10)\n",
        "  plt.axis(\"off\")\n",
        "  #plt.rcParams['figure.figsize'] = [10, 5]\n",
        "  plt.imshow(cv2.cvtColor(resized_image, cv2.COLOR_BGR2RGB))\n",
        "  plt.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e3dJB6NZv4kh",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1f2b3296-16cf-4ab4-9b7e-7b8e1ce80e04"
      },
      "source": [
        "#check if weigths have saved yet\n",
        "#backup houses the last weights for our detector\n",
        "#(file yolo-obj_last.weights will be saved to the build\\darknet\\x64\\backup\\ for each 100 iterations)\n",
        "#(file yolo-obj_xxxx.weights will be saved to the build\\darknet\\x64\\backup\\ for each 1000 iterations)\n",
        "#After training is complete - get result yolo-obj_final.weights from path build\\darknet\\x64\\bac\n",
        "!ls backup\n",
        "#if it is empty you haven't trained for long enough yet, you need to train for at least 100 iterations"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "custom-yolov4-tiny-detector_1000.weights\n",
            "custom-yolov4-tiny-detector_2000.weights\n",
            "custom-yolov4-tiny-detector_3000.weights\n",
            "custom-yolov4-tiny-detector_4000.weights\n",
            "custom-yolov4-tiny-detector_5000.weights\n",
            "custom-yolov4-tiny-detector_6000.weights\n",
            "custom-yolov4-tiny-detector_7000.weights\n",
            "custom-yolov4-tiny-detector_8000.weights\n",
            "custom-yolov4-tiny-detector_best.weights\n",
            "custom-yolov4-tiny-detector_final.weights\n",
            "custom-yolov4-tiny-detector_last.weights\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0gwfboR8Es-W",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8c6c35eb-3b74-415d-e42d-572f3e62de54"
      },
      "source": [
        "#save final weights to google drive\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.activity.readonly&response_type=code\n",
            "\n",
            "Enter your authorization code:\n",
            "4/1AY0e-g70yss4v6b5fRBLM20IHYgEabAMWTB2CrJ05-uPVeUHqRT2kAX5WDI\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FV7y6ZIP7aSx"
      },
      "source": [
        "# Darknet Weights\n",
        "!cp /content/darknet/backup/custom-yolov4-tiny-detector_final.weights \"/content/drive/My Drive\""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x-_E3O5Mf4Mf"
      },
      "source": [
        "#coco.names is hardcoded somewhere in the detector\n",
        "%cp data/obj.names data/coco.names"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NjKzw2TvZrOQ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 231
        },
        "outputId": "a4672823-a74f-4f3c-b744-0ede565ac068"
      },
      "source": [
        "\n",
        "#/test has images that we can test our detector on\n",
        "test_images = [f for f in os.listdir('test') if f.endswith('.jpg')]\n",
        "import random\n",
        "img_path = \"test/\" + random.choice(test_images);\n",
        "\n",
        "#test out our detector!\n",
        "!./darknet detect cfg/custom-yolov4-tiny-detector.cfg backup/custom-yolov4-tiny-detector_best.weights {img_path} -dont-show\n",
        "imShow('predictions.jpg')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "FileNotFoundError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-38-86a3231b28c0>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;31m#/test has images that we can test our detector on\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mtest_images\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mf\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mf\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlistdir\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'test'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mendswith\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'.jpg'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mrandom\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mimg_path\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"test/\"\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchoice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest_images\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m;\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'test'"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d5m2lgRUHBWj"
      },
      "source": [
        "#3. Convert the weights to TensorFlow's .pb representation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cHHTvG8bHLiG"
      },
      "source": [
        "Darknet produces a .weights file specific to Darknet. If we want to use the YOLOv4 model in TensorFlow, we'll need to convert it.\n",
        "\n",
        "To do this, we'll use the following tool: https://github.com/hunglc007/tensorflow-yolov4-tflite."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rJvP691HHrOm"
      },
      "source": [
        "## Install and Configure"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r3GUvX21Hyoe"
      },
      "source": [
        "First, we'll clone the repository."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GX277FMyH1Sz",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1f1c3721-7ab7-4c3c-c6fe-51109c229312"
      },
      "source": [
        "%cd /content\n",
        "!git clone https://github.com/hunglc007/tensorflow-yolov4-tflite.git\n",
        "%cd /content/tensorflow-yolov4-tflite"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content\n",
            "Cloning into 'tensorflow-yolov4-tflite'...\n",
            "remote: Enumerating objects: 780, done.\u001b[K\n",
            "remote: Total 780 (delta 0), reused 0 (delta 0), pack-reused 780\u001b[K\n",
            "Receiving objects: 100% (780/780), 197.64 MiB | 20.09 MiB/s, done.\n",
            "Resolving deltas: 100% (371/371), done.\n",
            "/content/tensorflow-yolov4-tflite\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PMRigsA_FC74"
      },
      "source": [
        "Then, we'll change the labels from the default COCO to our own custom ones."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ntIqg1-x9i3a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "33520c0a-6ae3-4389-e501-57b62eb20a07"
      },
      "source": [
        "!cp /content/darknet/data/obj.names /content/tensorflow-yolov4-tflite/data/classes/\n",
        "!ls /content/tensorflow-yolov4-tflite/data/classes/"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "coco.names  obj.names  voc.names  yymnist.names\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "59XOp9cA-G0S"
      },
      "source": [
        "!sed -i \"s/coco.names/obj.names/g\" /content/tensorflow-yolov4-tflite/core/config.py"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aWIyKzuYLeC0"
      },
      "source": [
        "## Convert"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ncsA58syFMVf"
      },
      "source": [
        "Time to convert! We'll convert to both a regular TensorFlow SavedModel and to TensorFlow Lite. For TensorFlow Lite, we'll convert to a different TensorFlow SavedModel beforehand."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wturTKvcLgix",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0686dda0-8304-462a-927b-357b169351ee"
      },
      "source": [
        "%cd /content/tensorflow-yolov4-tflite\n",
        "# Regular TensorFlow SavedModel\n",
        "!python save_model.py \\\n",
        "  --weights /content/darknet/backup/custom-yolov4-tiny-detector_final.weights \\\n",
        "  --output ./checkpoints/yolov4-tiny-416 \\\n",
        "  --input_size 416 \\\n",
        "  --model yolov4 \\\n",
        "  --tiny \\\n",
        "\n",
        "# SavedModel to convert to TFLite\n",
        "!python save_model.py \\\n",
        "  --weights /content/darknet/backup/custom-yolov4-tiny-detector_final.weights \\\n",
        "  --output ./checkpoints/yolov4-tiny-pretflite-416 \\\n",
        "  --input_size 416 \\\n",
        "  --model yolov4 \\\n",
        "  --tiny \\\n",
        "  --framework tflite "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/tensorflow-yolov4-tflite\n",
            "2020-11-17 13:45:34.660867: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1\n",
            "2020-11-17 13:45:36.231856: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcuda.so.1\n",
            "2020-11-17 13:45:36.247565: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-11-17 13:45:36.248599: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1716] Found device 0 with properties: \n",
            "pciBusID: 0000:00:04.0 name: Tesla P100-PCIE-16GB computeCapability: 6.0\n",
            "coreClock: 1.3285GHz coreCount: 56 deviceMemorySize: 15.90GiB deviceMemoryBandwidth: 681.88GiB/s\n",
            "2020-11-17 13:45:36.248653: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1\n",
            "2020-11-17 13:45:36.251150: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcublas.so.10\n",
            "2020-11-17 13:45:36.368858: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcufft.so.10\n",
            "2020-11-17 13:45:36.369366: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcurand.so.10\n",
            "2020-11-17 13:45:36.630152: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcusolver.so.10\n",
            "2020-11-17 13:45:36.646913: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcusparse.so.10\n",
            "2020-11-17 13:45:36.651463: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudnn.so.7\n",
            "2020-11-17 13:45:36.651604: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-11-17 13:45:36.652555: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-11-17 13:45:36.653387: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1858] Adding visible gpu devices: 0\n",
            "2020-11-17 13:45:36.721896: I tensorflow/core/platform/profile_utils/cpu_utils.cc:104] CPU Frequency: 2300000000 Hz\n",
            "2020-11-17 13:45:36.722131: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x2142bc0 initialized for platform Host (this does not guarantee that XLA will be used). Devices:\n",
            "2020-11-17 13:45:36.722181: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Host, Default Version\n",
            "2020-11-17 13:45:36.825266: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-11-17 13:45:36.826282: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x2142d80 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n",
            "2020-11-17 13:45:36.826319: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Tesla P100-PCIE-16GB, Compute Capability 6.0\n",
            "2020-11-17 13:45:36.826574: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-11-17 13:45:36.827467: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1716] Found device 0 with properties: \n",
            "pciBusID: 0000:00:04.0 name: Tesla P100-PCIE-16GB computeCapability: 6.0\n",
            "coreClock: 1.3285GHz coreCount: 56 deviceMemorySize: 15.90GiB deviceMemoryBandwidth: 681.88GiB/s\n",
            "2020-11-17 13:45:36.827525: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1\n",
            "2020-11-17 13:45:36.827590: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcublas.so.10\n",
            "2020-11-17 13:45:36.827629: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcufft.so.10\n",
            "2020-11-17 13:45:36.827677: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcurand.so.10\n",
            "2020-11-17 13:45:36.827715: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcusolver.so.10\n",
            "2020-11-17 13:45:36.827751: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcusparse.so.10\n",
            "2020-11-17 13:45:36.827787: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudnn.so.7\n",
            "2020-11-17 13:45:36.827891: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-11-17 13:45:36.828807: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-11-17 13:45:36.829660: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1858] Adding visible gpu devices: 0\n",
            "2020-11-17 13:45:36.833599: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1\n",
            "2020-11-17 13:45:40.830952: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1257] Device interconnect StreamExecutor with strength 1 edge matrix:\n",
            "2020-11-17 13:45:40.831014: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1263]      0 \n",
            "2020-11-17 13:45:40.831033: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1276] 0:   N \n",
            "2020-11-17 13:45:40.835518: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-11-17 13:45:40.836436: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-11-17 13:45:40.837314: W tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:39] Overriding allow_growth setting because the TF_FORCE_GPU_ALLOW_GROWTH environment variable is set. Original config value was 0.\n",
            "2020-11-17 13:45:40.837386: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1402] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 14968 MB memory) -> physical GPU (device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0)\n",
            "Model: \"functional_1\"\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "input_1 (InputLayer)            [(None, 416, 416, 3) 0                                            \n",
            "__________________________________________________________________________________________________\n",
            "zero_padding2d (ZeroPadding2D)  (None, 417, 417, 3)  0           input_1[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d (Conv2D)                 (None, 208, 208, 32) 864         zero_padding2d[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization (BatchNorma (None, 208, 208, 32) 128         conv2d[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_LeakyRelu (TensorFl [(None, 208, 208, 32 0           batch_normalization[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "zero_padding2d_1 (ZeroPadding2D (None, 209, 209, 32) 0           tf_op_layer_LeakyRelu[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_1 (Conv2D)               (None, 104, 104, 64) 18432       zero_padding2d_1[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_1 (BatchNor (None, 104, 104, 64) 256         conv2d_1[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_LeakyRelu_1 (Tensor [(None, 104, 104, 64 0           batch_normalization_1[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_2 (Conv2D)               (None, 104, 104, 64) 36864       tf_op_layer_LeakyRelu_1[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_2 (BatchNor (None, 104, 104, 64) 256         conv2d_2[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_LeakyRelu_2 (Tensor [(None, 104, 104, 64 0           batch_normalization_2[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_split (TensorFlowOp [(None, 104, 104, 32 0           tf_op_layer_LeakyRelu_2[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_3 (Conv2D)               (None, 104, 104, 32) 9216        tf_op_layer_split[0][1]          \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_3 (BatchNor (None, 104, 104, 32) 128         conv2d_3[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_LeakyRelu_3 (Tensor [(None, 104, 104, 32 0           batch_normalization_3[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_4 (Conv2D)               (None, 104, 104, 32) 9216        tf_op_layer_LeakyRelu_3[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_4 (BatchNor (None, 104, 104, 32) 128         conv2d_4[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_LeakyRelu_4 (Tensor [(None, 104, 104, 32 0           batch_normalization_4[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_concat (TensorFlowO [(None, 104, 104, 64 0           tf_op_layer_LeakyRelu_4[0][0]    \n",
            "                                                                 tf_op_layer_LeakyRelu_3[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_5 (Conv2D)               (None, 104, 104, 64) 4096        tf_op_layer_concat[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_5 (BatchNor (None, 104, 104, 64) 256         conv2d_5[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_LeakyRelu_5 (Tensor [(None, 104, 104, 64 0           batch_normalization_5[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_concat_1 (TensorFlo [(None, 104, 104, 12 0           tf_op_layer_LeakyRelu_2[0][0]    \n",
            "                                                                 tf_op_layer_LeakyRelu_5[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d (MaxPooling2D)    (None, 52, 52, 128)  0           tf_op_layer_concat_1[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_6 (Conv2D)               (None, 52, 52, 128)  147456      max_pooling2d[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_6 (BatchNor (None, 52, 52, 128)  512         conv2d_6[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_LeakyRelu_6 (Tensor [(None, 52, 52, 128) 0           batch_normalization_6[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_split_1 (TensorFlow [(None, 52, 52, 64), 0           tf_op_layer_LeakyRelu_6[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_7 (Conv2D)               (None, 52, 52, 64)   36864       tf_op_layer_split_1[0][1]        \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_7 (BatchNor (None, 52, 52, 64)   256         conv2d_7[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_LeakyRelu_7 (Tensor [(None, 52, 52, 64)] 0           batch_normalization_7[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_8 (Conv2D)               (None, 52, 52, 64)   36864       tf_op_layer_LeakyRelu_7[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_8 (BatchNor (None, 52, 52, 64)   256         conv2d_8[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_LeakyRelu_8 (Tensor [(None, 52, 52, 64)] 0           batch_normalization_8[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_concat_2 (TensorFlo [(None, 52, 52, 128) 0           tf_op_layer_LeakyRelu_8[0][0]    \n",
            "                                                                 tf_op_layer_LeakyRelu_7[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_9 (Conv2D)               (None, 52, 52, 128)  16384       tf_op_layer_concat_2[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_9 (BatchNor (None, 52, 52, 128)  512         conv2d_9[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_LeakyRelu_9 (Tensor [(None, 52, 52, 128) 0           batch_normalization_9[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_concat_3 (TensorFlo [(None, 52, 52, 256) 0           tf_op_layer_LeakyRelu_6[0][0]    \n",
            "                                                                 tf_op_layer_LeakyRelu_9[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_1 (MaxPooling2D)  (None, 26, 26, 256)  0           tf_op_layer_concat_3[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_10 (Conv2D)              (None, 26, 26, 256)  589824      max_pooling2d_1[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_10 (BatchNo (None, 26, 26, 256)  1024        conv2d_10[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_LeakyRelu_10 (Tenso [(None, 26, 26, 256) 0           batch_normalization_10[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_split_2 (TensorFlow [(None, 26, 26, 128) 0           tf_op_layer_LeakyRelu_10[0][0]   \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_11 (Conv2D)              (None, 26, 26, 128)  147456      tf_op_layer_split_2[0][1]        \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_11 (BatchNo (None, 26, 26, 128)  512         conv2d_11[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_LeakyRelu_11 (Tenso [(None, 26, 26, 128) 0           batch_normalization_11[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_12 (Conv2D)              (None, 26, 26, 128)  147456      tf_op_layer_LeakyRelu_11[0][0]   \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_12 (BatchNo (None, 26, 26, 128)  512         conv2d_12[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_LeakyRelu_12 (Tenso [(None, 26, 26, 128) 0           batch_normalization_12[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_concat_4 (TensorFlo [(None, 26, 26, 256) 0           tf_op_layer_LeakyRelu_12[0][0]   \n",
            "                                                                 tf_op_layer_LeakyRelu_11[0][0]   \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_13 (Conv2D)              (None, 26, 26, 256)  65536       tf_op_layer_concat_4[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_13 (BatchNo (None, 26, 26, 256)  1024        conv2d_13[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_LeakyRelu_13 (Tenso [(None, 26, 26, 256) 0           batch_normalization_13[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_concat_5 (TensorFlo [(None, 26, 26, 512) 0           tf_op_layer_LeakyRelu_10[0][0]   \n",
            "                                                                 tf_op_layer_LeakyRelu_13[0][0]   \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_2 (MaxPooling2D)  (None, 13, 13, 512)  0           tf_op_layer_concat_5[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_14 (Conv2D)              (None, 13, 13, 512)  2359296     max_pooling2d_2[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_14 (BatchNo (None, 13, 13, 512)  2048        conv2d_14[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_LeakyRelu_14 (Tenso [(None, 13, 13, 512) 0           batch_normalization_14[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_15 (Conv2D)              (None, 13, 13, 256)  131072      tf_op_layer_LeakyRelu_14[0][0]   \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_15 (BatchNo (None, 13, 13, 256)  1024        conv2d_15[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_LeakyRelu_15 (Tenso [(None, 13, 13, 256) 0           batch_normalization_15[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_18 (Conv2D)              (None, 13, 13, 128)  32768       tf_op_layer_LeakyRelu_15[0][0]   \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_17 (BatchNo (None, 13, 13, 128)  512         conv2d_18[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_LeakyRelu_17 (Tenso [(None, 13, 13, 128) 0           batch_normalization_17[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_ResizeBilinear (Ten [(None, 26, 26, 128) 0           tf_op_layer_LeakyRelu_17[0][0]   \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_concat_6 (TensorFlo [(None, 26, 26, 384) 0           tf_op_layer_ResizeBilinear[0][0] \n",
            "                                                                 tf_op_layer_LeakyRelu_13[0][0]   \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_19 (Conv2D)              (None, 26, 26, 256)  884736      tf_op_layer_concat_6[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_16 (Conv2D)              (None, 13, 13, 512)  1179648     tf_op_layer_LeakyRelu_15[0][0]   \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_18 (BatchNo (None, 26, 26, 256)  1024        conv2d_19[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_16 (BatchNo (None, 13, 13, 512)  2048        conv2d_16[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_LeakyRelu_18 (Tenso [(None, 26, 26, 256) 0           batch_normalization_18[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_LeakyRelu_16 (Tenso [(None, 13, 13, 512) 0           batch_normalization_16[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_20 (Conv2D)              (None, 26, 26, 27)   6939        tf_op_layer_LeakyRelu_18[0][0]   \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_17 (Conv2D)              (None, 13, 13, 27)   13851       tf_op_layer_LeakyRelu_16[0][0]   \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Shape (TensorFlowOp [(4,)]               0           conv2d_20[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Shape_1 (TensorFlow [(4,)]               0           conv2d_17[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_strided_slice (Tens [()]                 0           tf_op_layer_Shape[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_strided_slice_1 (Te [()]                 0           tf_op_layer_Shape_1[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Reshape/shape (Tens [(5,)]               0           tf_op_layer_strided_slice[0][0]  \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Reshape_3/shape (Te [(5,)]               0           tf_op_layer_strided_slice_1[0][0]\n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Reshape (TensorFlow [(None, 26, 26, 3, 9 0           conv2d_20[0][0]                  \n",
            "                                                                 tf_op_layer_Reshape/shape[0][0]  \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Reshape_3 (TensorFl [(None, 13, 13, 3, 9 0           conv2d_17[0][0]                  \n",
            "                                                                 tf_op_layer_Reshape_3/shape[0][0]\n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_split_3 (TensorFlow [(None, 26, 26, 3, 2 0           tf_op_layer_Reshape[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_split_4 (TensorFlow [(None, 13, 13, 3, 2 0           tf_op_layer_Reshape_3[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Sigmoid (TensorFlow [(None, 26, 26, 3, 2 0           tf_op_layer_split_3[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Tile/multiples (Ten [(5,)]               0           tf_op_layer_strided_slice[0][0]  \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Sigmoid_3 (TensorFl [(None, 13, 13, 3, 2 0           tf_op_layer_split_4[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Tile_1/multiples (T [(5,)]               0           tf_op_layer_strided_slice_1[0][0]\n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Mul (TensorFlowOpLa [(None, 26, 26, 3, 2 0           tf_op_layer_Sigmoid[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Tile (TensorFlowOpL [(None, 26, 26, 3, 2 0           tf_op_layer_Tile/multiples[0][0] \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Mul_4 (TensorFlowOp [(None, 13, 13, 3, 2 0           tf_op_layer_Sigmoid_3[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Tile_1 (TensorFlowO [(None, 13, 13, 3, 2 0           tf_op_layer_Tile_1/multiples[0][0\n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Sub (TensorFlowOpLa [(None, 26, 26, 3, 2 0           tf_op_layer_Mul[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Cast (TensorFlowOpL [(None, 26, 26, 3, 2 0           tf_op_layer_Tile[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Sub_1 (TensorFlowOp [(None, 13, 13, 3, 2 0           tf_op_layer_Mul_4[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Cast_1 (TensorFlowO [(None, 13, 13, 3, 2 0           tf_op_layer_Tile_1[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_AddV2 (TensorFlowOp [(None, 26, 26, 3, 2 0           tf_op_layer_Sub[0][0]            \n",
            "                                                                 tf_op_layer_Cast[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Exp (TensorFlowOpLa [(None, 26, 26, 3, 2 0           tf_op_layer_split_3[0][1]        \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_AddV2_1 (TensorFlow [(None, 13, 13, 3, 2 0           tf_op_layer_Sub_1[0][0]          \n",
            "                                                                 tf_op_layer_Cast_1[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Exp_1 (TensorFlowOp [(None, 13, 13, 3, 2 0           tf_op_layer_split_4[0][1]        \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Mul_1 (TensorFlowOp [(None, 26, 26, 3, 2 0           tf_op_layer_AddV2[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Mul_2 (TensorFlowOp [(None, 26, 26, 3, 2 0           tf_op_layer_Exp[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Mul_5 (TensorFlowOp [(None, 13, 13, 3, 2 0           tf_op_layer_AddV2_1[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Mul_6 (TensorFlowOp [(None, 13, 13, 3, 2 0           tf_op_layer_Exp_1[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_concat_7 (TensorFlo [(None, 26, 26, 3, 4 0           tf_op_layer_Mul_1[0][0]          \n",
            "                                                                 tf_op_layer_Mul_2[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Reshape_2/shape (Te [(3,)]               0           tf_op_layer_strided_slice[0][0]  \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_concat_8 (TensorFlo [(None, 13, 13, 3, 4 0           tf_op_layer_Mul_5[0][0]          \n",
            "                                                                 tf_op_layer_Mul_6[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Reshape_5/shape (Te [(3,)]               0           tf_op_layer_strided_slice_1[0][0]\n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Sigmoid_1 (TensorFl [(None, 26, 26, 3, 1 0           tf_op_layer_split_3[0][2]        \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Sigmoid_2 (TensorFl [(None, 26, 26, 3, 4 0           tf_op_layer_split_3[0][3]        \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Sigmoid_4 (TensorFl [(None, 13, 13, 3, 1 0           tf_op_layer_split_4[0][2]        \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Sigmoid_5 (TensorFl [(None, 13, 13, 3, 4 0           tf_op_layer_split_4[0][3]        \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Reshape_2 (TensorFl [(None, None, 4)]    0           tf_op_layer_concat_7[0][0]       \n",
            "                                                                 tf_op_layer_Reshape_2/shape[0][0]\n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Reshape_5 (TensorFl [(None, None, 4)]    0           tf_op_layer_concat_8[0][0]       \n",
            "                                                                 tf_op_layer_Reshape_5/shape[0][0]\n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Mul_3 (TensorFlowOp [(None, 26, 26, 3, 4 0           tf_op_layer_Sigmoid_1[0][0]      \n",
            "                                                                 tf_op_layer_Sigmoid_2[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Reshape_1/shape (Te [(3,)]               0           tf_op_layer_strided_slice[0][0]  \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Mul_7 (TensorFlowOp [(None, 13, 13, 3, 4 0           tf_op_layer_Sigmoid_4[0][0]      \n",
            "                                                                 tf_op_layer_Sigmoid_5[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Reshape_4/shape (Te [(3,)]               0           tf_op_layer_strided_slice_1[0][0]\n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_concat_9 (TensorFlo [(None, None, 4)]    0           tf_op_layer_Reshape_2[0][0]      \n",
            "                                                                 tf_op_layer_Reshape_5[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Reshape_1 (TensorFl [(None, None, 4)]    0           tf_op_layer_Mul_3[0][0]          \n",
            "                                                                 tf_op_layer_Reshape_1/shape[0][0]\n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Reshape_4 (TensorFl [(None, None, 4)]    0           tf_op_layer_Mul_7[0][0]          \n",
            "                                                                 tf_op_layer_Reshape_4/shape[0][0]\n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Shape_2 (TensorFlow [(3,)]               0           tf_op_layer_concat_9[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_concat_10 (TensorFl [(None, None, 4)]    0           tf_op_layer_Reshape_1[0][0]      \n",
            "                                                                 tf_op_layer_Reshape_4[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_strided_slice_2 (Te [(2,)]               0           tf_op_layer_Shape_2[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Max (TensorFlowOpLa [(None, None)]       0           tf_op_layer_concat_10[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Shape_3 (TensorFlow [(3,)]               0           tf_op_layer_concat_9[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Prod (TensorFlowOpL [()]                 0           tf_op_layer_strided_slice_2[0][0]\n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Shape_4 (TensorFlow [(3,)]               0           tf_op_layer_concat_9[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_GreaterEqual (Tenso [(None, None)]       0           tf_op_layer_Max[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_strided_slice_3 (Te [(0,)]               0           tf_op_layer_Shape_3[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_concat_11/values_1  [(1,)]               0           tf_op_layer_Prod[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_strided_slice_4 (Te [(1,)]               0           tf_op_layer_Shape_4[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Reshape_7 (TensorFl [(None,)]            0           tf_op_layer_GreaterEqual[0][0]   \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_concat_11 (TensorFl [(2,)]               0           tf_op_layer_strided_slice_3[0][0]\n",
            "                                                                 tf_op_layer_concat_11/values_1[0]\n",
            "                                                                 tf_op_layer_strided_slice_4[0][0]\n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Where (TensorFlowOp [(None, 1)]          0           tf_op_layer_Reshape_7[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Reshape_6 (TensorFl [(None, 4)]          0           tf_op_layer_concat_9[0][0]       \n",
            "                                                                 tf_op_layer_concat_11[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Squeeze (TensorFlow [(None,)]            0           tf_op_layer_Where[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_GatherV2 (TensorFlo [(None, 4)]          0           tf_op_layer_Reshape_6[0][0]      \n",
            "                                                                 tf_op_layer_Squeeze[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Shape_8 (TensorFlow [(3,)]               0           tf_op_layer_concat_10[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Shape_9 (TensorFlow [(2,)]               0           tf_op_layer_GatherV2[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Shape_5 (TensorFlow [(3,)]               0           tf_op_layer_concat_10[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_strided_slice_8 (Te [()]                 0           tf_op_layer_Shape_8[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_strided_slice_9 (Te [()]                 0           tf_op_layer_Shape_9[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_strided_slice_5 (Te [(2,)]               0           tf_op_layer_Shape_5[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Reshape_10/shape (T [(3,)]               0           tf_op_layer_strided_slice_8[0][0]\n",
            "                                                                 tf_op_layer_strided_slice_9[0][0]\n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Shape_6 (TensorFlow [(3,)]               0           tf_op_layer_concat_10[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Prod_1 (TensorFlowO [()]                 0           tf_op_layer_strided_slice_5[0][0]\n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Shape_7 (TensorFlow [(3,)]               0           tf_op_layer_concat_10[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Reshape_10 (TensorF [(None, None, None)] 0           tf_op_layer_GatherV2[0][0]       \n",
            "                                                                 tf_op_layer_Reshape_10/shape[0][0\n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_strided_slice_6 (Te [(0,)]               0           tf_op_layer_Shape_6[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_concat_12/values_1  [(1,)]               0           tf_op_layer_Prod_1[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_strided_slice_7 (Te [(1,)]               0           tf_op_layer_Shape_7[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Reshape_9 (TensorFl [(None,)]            0           tf_op_layer_GreaterEqual[0][0]   \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_split_5 (TensorFlow [(None, None, 2), (N 0           tf_op_layer_Reshape_10[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_concat_12 (TensorFl [(2,)]               0           tf_op_layer_strided_slice_6[0][0]\n",
            "                                                                 tf_op_layer_concat_12/values_1[0]\n",
            "                                                                 tf_op_layer_strided_slice_7[0][0]\n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Where_1 (TensorFlow [(None, 1)]          0           tf_op_layer_Reshape_9[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_strided_slice_13 (T [(None, None, 2)]    0           tf_op_layer_split_5[0][1]        \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Reshape_8 (TensorFl [(None, 4)]          0           tf_op_layer_concat_10[0][0]      \n",
            "                                                                 tf_op_layer_concat_12[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Squeeze_1 (TensorFl [(None,)]            0           tf_op_layer_Where_1[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_strided_slice_12 (T [(None, None, 2)]    0           tf_op_layer_split_5[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_RealDiv (TensorFlow [(None, None, 2)]    0           tf_op_layer_strided_slice_13[0][0\n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_RealDiv_2 (TensorFl [(None, None, 2)]    0           tf_op_layer_strided_slice_13[0][0\n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_GatherV2_1 (TensorF [(None, 4)]          0           tf_op_layer_Reshape_8[0][0]      \n",
            "                                                                 tf_op_layer_Squeeze_1[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Sub_2 (TensorFlowOp [(None, None, 2)]    0           tf_op_layer_strided_slice_12[0][0\n",
            "                                                                 tf_op_layer_RealDiv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_AddV2_2 (TensorFlow [(None, None, 2)]    0           tf_op_layer_strided_slice_12[0][0\n",
            "                                                                 tf_op_layer_RealDiv_2[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Shape_10 (TensorFlo [(3,)]               0           tf_op_layer_concat_10[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Shape_11 (TensorFlo [(2,)]               0           tf_op_layer_GatherV2_1[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_RealDiv_1 (TensorFl [(None, None, 2)]    0           tf_op_layer_Sub_2[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_RealDiv_3 (TensorFl [(None, None, 2)]    0           tf_op_layer_AddV2_2[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_strided_slice_10 (T [()]                 0           tf_op_layer_Shape_10[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_strided_slice_11 (T [()]                 0           tf_op_layer_Shape_11[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_strided_slice_14 (T [(None, None, 1)]    0           tf_op_layer_RealDiv_1[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_strided_slice_15 (T [(None, None, 1)]    0           tf_op_layer_RealDiv_1[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_strided_slice_16 (T [(None, None, 1)]    0           tf_op_layer_RealDiv_3[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_strided_slice_17 (T [(None, None, 1)]    0           tf_op_layer_RealDiv_3[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Reshape_11/shape (T [(3,)]               0           tf_op_layer_strided_slice_10[0][0\n",
            "                                                                 tf_op_layer_strided_slice_11[0][0\n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_concat_13 (TensorFl [(None, None, 4)]    0           tf_op_layer_strided_slice_14[0][0\n",
            "                                                                 tf_op_layer_strided_slice_15[0][0\n",
            "                                                                 tf_op_layer_strided_slice_16[0][0\n",
            "                                                                 tf_op_layer_strided_slice_17[0][0\n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Reshape_11 (TensorF [(None, None, None)] 0           tf_op_layer_GatherV2_1[0][0]     \n",
            "                                                                 tf_op_layer_Reshape_11/shape[0][0\n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_concat_14 (TensorFl [(None, None, None)] 0           tf_op_layer_concat_13[0][0]      \n",
            "                                                                 tf_op_layer_Reshape_11[0][0]     \n",
            "==================================================================================================\n",
            "Total params: 5,887,254\n",
            "Trainable params: 5,881,046\n",
            "Non-trainable params: 6,208\n",
            "__________________________________________________________________________________________________\n",
            "2020-11-17 13:45:48.429110: W tensorflow/python/util/util.cc:348] Sets are not currently considered sequences, but this may change in the future, so consider avoiding using them.\n",
            "INFO:tensorflow:Assets written to: ./checkpoints/yolov4-tiny-416/assets\n",
            "I1117 13:45:57.015631 139682929182592 builder_impl.py:775] Assets written to: ./checkpoints/yolov4-tiny-416/assets\n",
            "2020-11-17 13:45:58.764794: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1\n",
            "2020-11-17 13:46:00.350657: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcuda.so.1\n",
            "2020-11-17 13:46:00.364261: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-11-17 13:46:00.365070: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1716] Found device 0 with properties: \n",
            "pciBusID: 0000:00:04.0 name: Tesla P100-PCIE-16GB computeCapability: 6.0\n",
            "coreClock: 1.3285GHz coreCount: 56 deviceMemorySize: 15.90GiB deviceMemoryBandwidth: 681.88GiB/s\n",
            "2020-11-17 13:46:00.365118: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1\n",
            "2020-11-17 13:46:00.366742: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcublas.so.10\n",
            "2020-11-17 13:46:00.377020: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcufft.so.10\n",
            "2020-11-17 13:46:00.377366: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcurand.so.10\n",
            "2020-11-17 13:46:00.379241: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcusolver.so.10\n",
            "2020-11-17 13:46:00.380087: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcusparse.so.10\n",
            "2020-11-17 13:46:00.383681: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudnn.so.7\n",
            "2020-11-17 13:46:00.383797: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-11-17 13:46:00.384589: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-11-17 13:46:00.385385: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1858] Adding visible gpu devices: 0\n",
            "2020-11-17 13:46:00.391938: I tensorflow/core/platform/profile_utils/cpu_utils.cc:104] CPU Frequency: 2300000000 Hz\n",
            "2020-11-17 13:46:00.392145: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x162cbc0 initialized for platform Host (this does not guarantee that XLA will be used). Devices:\n",
            "2020-11-17 13:46:00.392197: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Host, Default Version\n",
            "2020-11-17 13:46:00.482207: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-11-17 13:46:00.483175: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x162cd80 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n",
            "2020-11-17 13:46:00.483211: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Tesla P100-PCIE-16GB, Compute Capability 6.0\n",
            "2020-11-17 13:46:00.483405: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-11-17 13:46:00.484177: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1716] Found device 0 with properties: \n",
            "pciBusID: 0000:00:04.0 name: Tesla P100-PCIE-16GB computeCapability: 6.0\n",
            "coreClock: 1.3285GHz coreCount: 56 deviceMemorySize: 15.90GiB deviceMemoryBandwidth: 681.88GiB/s\n",
            "2020-11-17 13:46:00.484246: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1\n",
            "2020-11-17 13:46:00.484305: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcublas.so.10\n",
            "2020-11-17 13:46:00.484341: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcufft.so.10\n",
            "2020-11-17 13:46:00.484376: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcurand.so.10\n",
            "2020-11-17 13:46:00.484410: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcusolver.so.10\n",
            "2020-11-17 13:46:00.484451: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcusparse.so.10\n",
            "2020-11-17 13:46:00.484484: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudnn.so.7\n",
            "2020-11-17 13:46:00.484587: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-11-17 13:46:00.485401: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-11-17 13:46:00.486121: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1858] Adding visible gpu devices: 0\n",
            "2020-11-17 13:46:00.486181: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1\n",
            "2020-11-17 13:46:01.091043: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1257] Device interconnect StreamExecutor with strength 1 edge matrix:\n",
            "2020-11-17 13:46:01.091107: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1263]      0 \n",
            "2020-11-17 13:46:01.091126: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1276] 0:   N \n",
            "2020-11-17 13:46:01.091320: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-11-17 13:46:01.092312: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-11-17 13:46:01.093087: W tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:39] Overriding allow_growth setting because the TF_FORCE_GPU_ALLOW_GROWTH environment variable is set. Original config value was 0.\n",
            "2020-11-17 13:46:01.093153: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1402] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 14968 MB memory) -> physical GPU (device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0)\n",
            "Model: \"functional_1\"\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "input_1 (InputLayer)            [(None, 416, 416, 3) 0                                            \n",
            "__________________________________________________________________________________________________\n",
            "zero_padding2d (ZeroPadding2D)  (None, 417, 417, 3)  0           input_1[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d (Conv2D)                 (None, 208, 208, 32) 864         zero_padding2d[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization (BatchNorma (None, 208, 208, 32) 128         conv2d[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_LeakyRelu (TensorFl [(None, 208, 208, 32 0           batch_normalization[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "zero_padding2d_1 (ZeroPadding2D (None, 209, 209, 32) 0           tf_op_layer_LeakyRelu[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_1 (Conv2D)               (None, 104, 104, 64) 18432       zero_padding2d_1[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_1 (BatchNor (None, 104, 104, 64) 256         conv2d_1[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_LeakyRelu_1 (Tensor [(None, 104, 104, 64 0           batch_normalization_1[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_2 (Conv2D)               (None, 104, 104, 64) 36864       tf_op_layer_LeakyRelu_1[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_2 (BatchNor (None, 104, 104, 64) 256         conv2d_2[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_LeakyRelu_2 (Tensor [(None, 104, 104, 64 0           batch_normalization_2[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_split (TensorFlowOp [(None, 104, 104, 32 0           tf_op_layer_LeakyRelu_2[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_3 (Conv2D)               (None, 104, 104, 32) 9216        tf_op_layer_split[0][1]          \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_3 (BatchNor (None, 104, 104, 32) 128         conv2d_3[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_LeakyRelu_3 (Tensor [(None, 104, 104, 32 0           batch_normalization_3[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_4 (Conv2D)               (None, 104, 104, 32) 9216        tf_op_layer_LeakyRelu_3[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_4 (BatchNor (None, 104, 104, 32) 128         conv2d_4[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_LeakyRelu_4 (Tensor [(None, 104, 104, 32 0           batch_normalization_4[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_concat (TensorFlowO [(None, 104, 104, 64 0           tf_op_layer_LeakyRelu_4[0][0]    \n",
            "                                                                 tf_op_layer_LeakyRelu_3[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_5 (Conv2D)               (None, 104, 104, 64) 4096        tf_op_layer_concat[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_5 (BatchNor (None, 104, 104, 64) 256         conv2d_5[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_LeakyRelu_5 (Tensor [(None, 104, 104, 64 0           batch_normalization_5[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_concat_1 (TensorFlo [(None, 104, 104, 12 0           tf_op_layer_LeakyRelu_2[0][0]    \n",
            "                                                                 tf_op_layer_LeakyRelu_5[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d (MaxPooling2D)    (None, 52, 52, 128)  0           tf_op_layer_concat_1[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_6 (Conv2D)               (None, 52, 52, 128)  147456      max_pooling2d[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_6 (BatchNor (None, 52, 52, 128)  512         conv2d_6[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_LeakyRelu_6 (Tensor [(None, 52, 52, 128) 0           batch_normalization_6[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_split_1 (TensorFlow [(None, 52, 52, 64), 0           tf_op_layer_LeakyRelu_6[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_7 (Conv2D)               (None, 52, 52, 64)   36864       tf_op_layer_split_1[0][1]        \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_7 (BatchNor (None, 52, 52, 64)   256         conv2d_7[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_LeakyRelu_7 (Tensor [(None, 52, 52, 64)] 0           batch_normalization_7[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_8 (Conv2D)               (None, 52, 52, 64)   36864       tf_op_layer_LeakyRelu_7[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_8 (BatchNor (None, 52, 52, 64)   256         conv2d_8[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_LeakyRelu_8 (Tensor [(None, 52, 52, 64)] 0           batch_normalization_8[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_concat_2 (TensorFlo [(None, 52, 52, 128) 0           tf_op_layer_LeakyRelu_8[0][0]    \n",
            "                                                                 tf_op_layer_LeakyRelu_7[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_9 (Conv2D)               (None, 52, 52, 128)  16384       tf_op_layer_concat_2[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_9 (BatchNor (None, 52, 52, 128)  512         conv2d_9[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_LeakyRelu_9 (Tensor [(None, 52, 52, 128) 0           batch_normalization_9[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_concat_3 (TensorFlo [(None, 52, 52, 256) 0           tf_op_layer_LeakyRelu_6[0][0]    \n",
            "                                                                 tf_op_layer_LeakyRelu_9[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_1 (MaxPooling2D)  (None, 26, 26, 256)  0           tf_op_layer_concat_3[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_10 (Conv2D)              (None, 26, 26, 256)  589824      max_pooling2d_1[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_10 (BatchNo (None, 26, 26, 256)  1024        conv2d_10[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_LeakyRelu_10 (Tenso [(None, 26, 26, 256) 0           batch_normalization_10[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_split_2 (TensorFlow [(None, 26, 26, 128) 0           tf_op_layer_LeakyRelu_10[0][0]   \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_11 (Conv2D)              (None, 26, 26, 128)  147456      tf_op_layer_split_2[0][1]        \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_11 (BatchNo (None, 26, 26, 128)  512         conv2d_11[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_LeakyRelu_11 (Tenso [(None, 26, 26, 128) 0           batch_normalization_11[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_12 (Conv2D)              (None, 26, 26, 128)  147456      tf_op_layer_LeakyRelu_11[0][0]   \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_12 (BatchNo (None, 26, 26, 128)  512         conv2d_12[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_LeakyRelu_12 (Tenso [(None, 26, 26, 128) 0           batch_normalization_12[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_concat_4 (TensorFlo [(None, 26, 26, 256) 0           tf_op_layer_LeakyRelu_12[0][0]   \n",
            "                                                                 tf_op_layer_LeakyRelu_11[0][0]   \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_13 (Conv2D)              (None, 26, 26, 256)  65536       tf_op_layer_concat_4[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_13 (BatchNo (None, 26, 26, 256)  1024        conv2d_13[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_LeakyRelu_13 (Tenso [(None, 26, 26, 256) 0           batch_normalization_13[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_concat_5 (TensorFlo [(None, 26, 26, 512) 0           tf_op_layer_LeakyRelu_10[0][0]   \n",
            "                                                                 tf_op_layer_LeakyRelu_13[0][0]   \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_2 (MaxPooling2D)  (None, 13, 13, 512)  0           tf_op_layer_concat_5[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_14 (Conv2D)              (None, 13, 13, 512)  2359296     max_pooling2d_2[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_14 (BatchNo (None, 13, 13, 512)  2048        conv2d_14[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_LeakyRelu_14 (Tenso [(None, 13, 13, 512) 0           batch_normalization_14[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_15 (Conv2D)              (None, 13, 13, 256)  131072      tf_op_layer_LeakyRelu_14[0][0]   \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_15 (BatchNo (None, 13, 13, 256)  1024        conv2d_15[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_LeakyRelu_15 (Tenso [(None, 13, 13, 256) 0           batch_normalization_15[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_18 (Conv2D)              (None, 13, 13, 128)  32768       tf_op_layer_LeakyRelu_15[0][0]   \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_17 (BatchNo (None, 13, 13, 128)  512         conv2d_18[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_LeakyRelu_17 (Tenso [(None, 13, 13, 128) 0           batch_normalization_17[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_ResizeBilinear (Ten [(None, 26, 26, 128) 0           tf_op_layer_LeakyRelu_17[0][0]   \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_concat_6 (TensorFlo [(None, 26, 26, 384) 0           tf_op_layer_ResizeBilinear[0][0] \n",
            "                                                                 tf_op_layer_LeakyRelu_13[0][0]   \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_19 (Conv2D)              (None, 26, 26, 256)  884736      tf_op_layer_concat_6[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_16 (Conv2D)              (None, 13, 13, 512)  1179648     tf_op_layer_LeakyRelu_15[0][0]   \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_18 (BatchNo (None, 26, 26, 256)  1024        conv2d_19[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_16 (BatchNo (None, 13, 13, 512)  2048        conv2d_16[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_LeakyRelu_18 (Tenso [(None, 26, 26, 256) 0           batch_normalization_18[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_LeakyRelu_16 (Tenso [(None, 13, 13, 512) 0           batch_normalization_16[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_20 (Conv2D)              (None, 26, 26, 27)   6939        tf_op_layer_LeakyRelu_18[0][0]   \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_17 (Conv2D)              (None, 13, 13, 27)   13851       tf_op_layer_LeakyRelu_16[0][0]   \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_split_3 (TensorFlow [(None, 26, 26, 2),  0           conv2d_20[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_split_4 (TensorFlow [(None, 13, 13, 2),  0           conv2d_17[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Sigmoid_3 (TensorFl [(None, 26, 26, 2)]  0           tf_op_layer_split_3[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Sigmoid_4 (TensorFl [(None, 26, 26, 2)]  0           tf_op_layer_split_3[0][3]        \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Sigmoid_5 (TensorFl [(None, 26, 26, 2)]  0           tf_op_layer_split_3[0][6]        \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Sigmoid_9 (TensorFl [(None, 13, 13, 2)]  0           tf_op_layer_split_4[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Sigmoid_10 (TensorF [(None, 13, 13, 2)]  0           tf_op_layer_split_4[0][3]        \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Sigmoid_11 (TensorF [(None, 13, 13, 2)]  0           tf_op_layer_split_4[0][6]        \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Mul_6 (TensorFlowOp [(None, 26, 26, 2)]  0           tf_op_layer_Sigmoid_3[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Mul_8 (TensorFlowOp [(None, 26, 26, 2)]  0           tf_op_layer_Sigmoid_4[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Mul_10 (TensorFlowO [(None, 26, 26, 2)]  0           tf_op_layer_Sigmoid_5[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Mul_18 (TensorFlowO [(None, 13, 13, 2)]  0           tf_op_layer_Sigmoid_9[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Mul_20 (TensorFlowO [(None, 13, 13, 2)]  0           tf_op_layer_Sigmoid_10[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Mul_22 (TensorFlowO [(None, 13, 13, 2)]  0           tf_op_layer_Sigmoid_11[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Sub (TensorFlowOpLa [(None, 26, 26, 2)]  0           tf_op_layer_Mul_6[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Sub_1 (TensorFlowOp [(None, 26, 26, 2)]  0           tf_op_layer_Mul_8[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Sub_2 (TensorFlowOp [(None, 26, 26, 2)]  0           tf_op_layer_Mul_10[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Sub_3 (TensorFlowOp [(None, 13, 13, 2)]  0           tf_op_layer_Mul_18[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Sub_4 (TensorFlowOp [(None, 13, 13, 2)]  0           tf_op_layer_Mul_20[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Sub_5 (TensorFlowOp [(None, 13, 13, 2)]  0           tf_op_layer_Mul_22[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_AddV2 (TensorFlowOp [(None, 26, 26, 2)]  0           tf_op_layer_Sub[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_AddV2_1 (TensorFlow [(None, 26, 26, 2)]  0           tf_op_layer_Sub_1[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_AddV2_2 (TensorFlow [(None, 26, 26, 2)]  0           tf_op_layer_Sub_2[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Exp (TensorFlowOpLa [(None, 26, 26, 2)]  0           tf_op_layer_split_3[0][1]        \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Exp_1 (TensorFlowOp [(None, 26, 26, 2)]  0           tf_op_layer_split_3[0][4]        \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Exp_2 (TensorFlowOp [(None, 26, 26, 2)]  0           tf_op_layer_split_3[0][7]        \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_AddV2_3 (TensorFlow [(None, 13, 13, 2)]  0           tf_op_layer_Sub_3[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_AddV2_4 (TensorFlow [(None, 13, 13, 2)]  0           tf_op_layer_Sub_4[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_AddV2_5 (TensorFlow [(None, 13, 13, 2)]  0           tf_op_layer_Sub_5[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Exp_3 (TensorFlowOp [(None, 13, 13, 2)]  0           tf_op_layer_split_4[0][1]        \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Exp_4 (TensorFlowOp [(None, 13, 13, 2)]  0           tf_op_layer_split_4[0][4]        \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Exp_5 (TensorFlowOp [(None, 13, 13, 2)]  0           tf_op_layer_split_4[0][7]        \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Sigmoid (TensorFlow [(None, 26, 26, 5)]  0           tf_op_layer_split_3[0][2]        \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Sigmoid_1 (TensorFl [(None, 26, 26, 5)]  0           tf_op_layer_split_3[0][5]        \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Sigmoid_2 (TensorFl [(None, 26, 26, 5)]  0           tf_op_layer_split_3[0][8]        \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Sigmoid_6 (TensorFl [(None, 13, 13, 5)]  0           tf_op_layer_split_4[0][2]        \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Sigmoid_7 (TensorFl [(None, 13, 13, 5)]  0           tf_op_layer_split_4[0][5]        \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Sigmoid_8 (TensorFl [(None, 13, 13, 5)]  0           tf_op_layer_split_4[0][8]        \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Mul_7 (TensorFlowOp [(None, 26, 26, 2)]  0           tf_op_layer_AddV2[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Mul_9 (TensorFlowOp [(None, 26, 26, 2)]  0           tf_op_layer_AddV2_1[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Mul_11 (TensorFlowO [(None, 26, 26, 2)]  0           tf_op_layer_AddV2_2[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Mul_3 (TensorFlowOp [(None, 26, 26, 2)]  0           tf_op_layer_Exp[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Mul_4 (TensorFlowOp [(None, 26, 26, 2)]  0           tf_op_layer_Exp_1[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Mul_5 (TensorFlowOp [(None, 26, 26, 2)]  0           tf_op_layer_Exp_2[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Mul_19 (TensorFlowO [(None, 13, 13, 2)]  0           tf_op_layer_AddV2_3[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Mul_21 (TensorFlowO [(None, 13, 13, 2)]  0           tf_op_layer_AddV2_4[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Mul_23 (TensorFlowO [(None, 13, 13, 2)]  0           tf_op_layer_AddV2_5[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Mul_15 (TensorFlowO [(None, 13, 13, 2)]  0           tf_op_layer_Exp_3[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Mul_16 (TensorFlowO [(None, 13, 13, 2)]  0           tf_op_layer_Exp_4[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Mul_17 (TensorFlowO [(None, 13, 13, 2)]  0           tf_op_layer_Exp_5[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_strided_slice (Tens [(None, 26, 26, 1)]  0           tf_op_layer_Sigmoid[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_strided_slice_1 (Te [(None, 26, 26, 4)]  0           tf_op_layer_Sigmoid[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_strided_slice_2 (Te [(None, 26, 26, 1)]  0           tf_op_layer_Sigmoid_1[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_strided_slice_3 (Te [(None, 26, 26, 4)]  0           tf_op_layer_Sigmoid_1[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_strided_slice_4 (Te [(None, 26, 26, 1)]  0           tf_op_layer_Sigmoid_2[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_strided_slice_5 (Te [(None, 26, 26, 4)]  0           tf_op_layer_Sigmoid_2[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_strided_slice_6 (Te [(None, 13, 13, 1)]  0           tf_op_layer_Sigmoid_6[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_strided_slice_7 (Te [(None, 13, 13, 4)]  0           tf_op_layer_Sigmoid_6[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_strided_slice_8 (Te [(None, 13, 13, 1)]  0           tf_op_layer_Sigmoid_7[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_strided_slice_9 (Te [(None, 13, 13, 4)]  0           tf_op_layer_Sigmoid_7[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_strided_slice_10 (T [(None, 13, 13, 1)]  0           tf_op_layer_Sigmoid_8[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_strided_slice_11 (T [(None, 13, 13, 4)]  0           tf_op_layer_Sigmoid_8[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Reshape_6 (TensorFl [(1, None, 2)]       0           tf_op_layer_Mul_7[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Reshape_7 (TensorFl [(1, None, 2)]       0           tf_op_layer_Mul_9[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Reshape_8 (TensorFl [(1, None, 2)]       0           tf_op_layer_Mul_11[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Reshape_3 (TensorFl [(1, None, 2)]       0           tf_op_layer_Mul_3[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Reshape_4 (TensorFl [(1, None, 2)]       0           tf_op_layer_Mul_4[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Reshape_5 (TensorFl [(1, None, 2)]       0           tf_op_layer_Mul_5[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Reshape_15 (TensorF [(1, None, 2)]       0           tf_op_layer_Mul_19[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Reshape_16 (TensorF [(1, None, 2)]       0           tf_op_layer_Mul_21[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Reshape_17 (TensorF [(1, None, 2)]       0           tf_op_layer_Mul_23[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Reshape_12 (TensorF [(1, None, 2)]       0           tf_op_layer_Mul_15[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Reshape_13 (TensorF [(1, None, 2)]       0           tf_op_layer_Mul_16[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Reshape_14 (TensorF [(1, None, 2)]       0           tf_op_layer_Mul_17[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Mul (TensorFlowOpLa [(None, 26, 26, 4)]  0           tf_op_layer_strided_slice[0][0]  \n",
            "                                                                 tf_op_layer_strided_slice_1[0][0]\n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Mul_1 (TensorFlowOp [(None, 26, 26, 4)]  0           tf_op_layer_strided_slice_2[0][0]\n",
            "                                                                 tf_op_layer_strided_slice_3[0][0]\n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Mul_2 (TensorFlowOp [(None, 26, 26, 4)]  0           tf_op_layer_strided_slice_4[0][0]\n",
            "                                                                 tf_op_layer_strided_slice_5[0][0]\n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Mul_12 (TensorFlowO [(None, 13, 13, 4)]  0           tf_op_layer_strided_slice_6[0][0]\n",
            "                                                                 tf_op_layer_strided_slice_7[0][0]\n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Mul_13 (TensorFlowO [(None, 13, 13, 4)]  0           tf_op_layer_strided_slice_8[0][0]\n",
            "                                                                 tf_op_layer_strided_slice_9[0][0]\n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Mul_14 (TensorFlowO [(None, 13, 13, 4)]  0           tf_op_layer_strided_slice_10[0][0\n",
            "                                                                 tf_op_layer_strided_slice_11[0][0\n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_concat_9 (TensorFlo [(1, None, 2)]       0           tf_op_layer_Reshape_6[0][0]      \n",
            "                                                                 tf_op_layer_Reshape_7[0][0]      \n",
            "                                                                 tf_op_layer_Reshape_8[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_concat_8 (TensorFlo [(1, None, 2)]       0           tf_op_layer_Reshape_3[0][0]      \n",
            "                                                                 tf_op_layer_Reshape_4[0][0]      \n",
            "                                                                 tf_op_layer_Reshape_5[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_concat_13 (TensorFl [(1, None, 2)]       0           tf_op_layer_Reshape_15[0][0]     \n",
            "                                                                 tf_op_layer_Reshape_16[0][0]     \n",
            "                                                                 tf_op_layer_Reshape_17[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_concat_12 (TensorFl [(1, None, 2)]       0           tf_op_layer_Reshape_12[0][0]     \n",
            "                                                                 tf_op_layer_Reshape_13[0][0]     \n",
            "                                                                 tf_op_layer_Reshape_14[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Reshape (TensorFlow [(1, None, 4)]       0           tf_op_layer_Mul[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Reshape_1 (TensorFl [(1, None, 4)]       0           tf_op_layer_Mul_1[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Reshape_2 (TensorFl [(1, None, 4)]       0           tf_op_layer_Mul_2[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Reshape_9 (TensorFl [(1, None, 4)]       0           tf_op_layer_Mul_12[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Reshape_10 (TensorF [(1, None, 4)]       0           tf_op_layer_Mul_13[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_Reshape_11 (TensorF [(1, None, 4)]       0           tf_op_layer_Mul_14[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_concat_10 (TensorFl [(1, None, 4)]       0           tf_op_layer_concat_9[0][0]       \n",
            "                                                                 tf_op_layer_concat_8[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_concat_14 (TensorFl [(1, None, 4)]       0           tf_op_layer_concat_13[0][0]      \n",
            "                                                                 tf_op_layer_concat_12[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_concat_7 (TensorFlo [(1, None, 4)]       0           tf_op_layer_Reshape[0][0]        \n",
            "                                                                 tf_op_layer_Reshape_1[0][0]      \n",
            "                                                                 tf_op_layer_Reshape_2[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_concat_11 (TensorFl [(1, None, 4)]       0           tf_op_layer_Reshape_9[0][0]      \n",
            "                                                                 tf_op_layer_Reshape_10[0][0]     \n",
            "                                                                 tf_op_layer_Reshape_11[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_concat_15 (TensorFl [(1, None, 4)]       0           tf_op_layer_concat_10[0][0]      \n",
            "                                                                 tf_op_layer_concat_14[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "tf_op_layer_concat_16 (TensorFl [(1, None, 4)]       0           tf_op_layer_concat_7[0][0]       \n",
            "                                                                 tf_op_layer_concat_11[0][0]      \n",
            "==================================================================================================\n",
            "Total params: 5,887,254\n",
            "Trainable params: 5,881,046\n",
            "Non-trainable params: 6,208\n",
            "__________________________________________________________________________________________________\n",
            "2020-11-17 13:46:08.354368: W tensorflow/python/util/util.cc:348] Sets are not currently considered sequences, but this may change in the future, so consider avoiding using them.\n",
            "INFO:tensorflow:Assets written to: ./checkpoints/yolov4-tiny-pretflite-416/assets\n",
            "I1117 13:46:16.754100 140227389720448 builder_impl.py:775] Assets written to: ./checkpoints/yolov4-tiny-pretflite-416/assets\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3Jdcq5seHD22"
      },
      "source": [
        "#4. Convert the TensorFlow weights to TensorFlow Lite"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i5BApg9dOwpM"
      },
      "source": [
        "From the generated TensorFlow SavedModel, we will convert to .tflite"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uggKjSzoOrMK",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "52ac2e66-fe00-4472-e0c4-6f5d8624bf26"
      },
      "source": [
        "%cd /content/tensorflow-yolov4-tflite\n",
        "!python convert_tflite.py --weights ./checkpoints/yolov4-tiny-pretflite-416 --output ./checkpoints/yolov4-tiny-416.tflite"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/tensorflow-yolov4-tflite\n",
            "2020-11-17 13:46:30.856207: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1\n",
            "2020-11-17 13:46:32.391236: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcuda.so.1\n",
            "2020-11-17 13:46:32.405547: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-11-17 13:46:32.406410: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1716] Found device 0 with properties: \n",
            "pciBusID: 0000:00:04.0 name: Tesla P100-PCIE-16GB computeCapability: 6.0\n",
            "coreClock: 1.3285GHz coreCount: 56 deviceMemorySize: 15.90GiB deviceMemoryBandwidth: 681.88GiB/s\n",
            "2020-11-17 13:46:32.406454: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1\n",
            "2020-11-17 13:46:32.408000: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcublas.so.10\n",
            "2020-11-17 13:46:32.409771: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcufft.so.10\n",
            "2020-11-17 13:46:32.410100: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcurand.so.10\n",
            "2020-11-17 13:46:32.412216: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcusolver.so.10\n",
            "2020-11-17 13:46:32.413212: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcusparse.so.10\n",
            "2020-11-17 13:46:32.417261: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudnn.so.7\n",
            "2020-11-17 13:46:32.417378: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-11-17 13:46:32.418311: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-11-17 13:46:32.419123: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1858] Adding visible gpu devices: 0\n",
            "2020-11-17 13:46:32.425483: I tensorflow/core/platform/profile_utils/cpu_utils.cc:104] CPU Frequency: 2300000000 Hz\n",
            "2020-11-17 13:46:32.425693: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x14b6bc0 initialized for platform Host (this does not guarantee that XLA will be used). Devices:\n",
            "2020-11-17 13:46:32.425729: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Host, Default Version\n",
            "2020-11-17 13:46:32.512971: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-11-17 13:46:32.513833: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x14b6d80 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n",
            "2020-11-17 13:46:32.513866: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Tesla P100-PCIE-16GB, Compute Capability 6.0\n",
            "2020-11-17 13:46:32.514038: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-11-17 13:46:32.514829: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1716] Found device 0 with properties: \n",
            "pciBusID: 0000:00:04.0 name: Tesla P100-PCIE-16GB computeCapability: 6.0\n",
            "coreClock: 1.3285GHz coreCount: 56 deviceMemorySize: 15.90GiB deviceMemoryBandwidth: 681.88GiB/s\n",
            "2020-11-17 13:46:32.514879: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1\n",
            "2020-11-17 13:46:32.514939: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcublas.so.10\n",
            "2020-11-17 13:46:32.514973: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcufft.so.10\n",
            "2020-11-17 13:46:32.515017: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcurand.so.10\n",
            "2020-11-17 13:46:32.515050: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcusolver.so.10\n",
            "2020-11-17 13:46:32.515082: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcusparse.so.10\n",
            "2020-11-17 13:46:32.515113: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudnn.so.7\n",
            "2020-11-17 13:46:32.515230: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-11-17 13:46:32.516138: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-11-17 13:46:32.516849: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1858] Adding visible gpu devices: 0\n",
            "2020-11-17 13:46:32.516896: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1\n",
            "2020-11-17 13:46:33.128216: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1257] Device interconnect StreamExecutor with strength 1 edge matrix:\n",
            "2020-11-17 13:46:33.128278: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1263]      0 \n",
            "2020-11-17 13:46:33.128311: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1276] 0:   N \n",
            "2020-11-17 13:46:33.128608: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-11-17 13:46:33.129632: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-11-17 13:46:33.130423: W tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:39] Overriding allow_growth setting because the TF_FORCE_GPU_ALLOW_GROWTH environment variable is set. Original config value was 0.\n",
            "2020-11-17 13:46:33.130483: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1402] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 14968 MB memory) -> physical GPU (device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0)\n",
            "2020-11-17 13:46:37.970147: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-11-17 13:46:37.971008: I tensorflow/core/grappler/devices.cc:69] Number of eligible GPUs (core count >= 8, compute capability >= 0.0): 1\n",
            "2020-11-17 13:46:37.971233: I tensorflow/core/grappler/clusters/single_machine.cc:356] Starting new session\n",
            "2020-11-17 13:46:37.971883: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-11-17 13:46:37.972749: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1716] Found device 0 with properties: \n",
            "pciBusID: 0000:00:04.0 name: Tesla P100-PCIE-16GB computeCapability: 6.0\n",
            "coreClock: 1.3285GHz coreCount: 56 deviceMemorySize: 15.90GiB deviceMemoryBandwidth: 681.88GiB/s\n",
            "2020-11-17 13:46:37.972814: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1\n",
            "2020-11-17 13:46:37.972887: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcublas.so.10\n",
            "2020-11-17 13:46:37.972940: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcufft.so.10\n",
            "2020-11-17 13:46:37.973001: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcurand.so.10\n",
            "2020-11-17 13:46:37.973053: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcusolver.so.10\n",
            "2020-11-17 13:46:37.973106: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcusparse.so.10\n",
            "2020-11-17 13:46:37.973144: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudnn.so.7\n",
            "2020-11-17 13:46:37.973242: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-11-17 13:46:37.974122: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-11-17 13:46:37.975007: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1858] Adding visible gpu devices: 0\n",
            "2020-11-17 13:46:37.975058: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1257] Device interconnect StreamExecutor with strength 1 edge matrix:\n",
            "2020-11-17 13:46:37.975077: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1263]      0 \n",
            "2020-11-17 13:46:37.975112: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1276] 0:   N \n",
            "2020-11-17 13:46:37.975265: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-11-17 13:46:37.976134: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-11-17 13:46:37.977087: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1402] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 14968 MB memory) -> physical GPU (device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0)\n",
            "2020-11-17 13:46:38.005548: I tensorflow/core/grappler/optimizers/meta_optimizer.cc:816] Optimization results for grappler item: graph_to_optimize\n",
            "2020-11-17 13:46:38.005599: I tensorflow/core/grappler/optimizers/meta_optimizer.cc:818]   function_optimizer: Graph size after: 647 nodes (544), 657 edges (553), time = 14.042ms.\n",
            "2020-11-17 13:46:38.005620: I tensorflow/core/grappler/optimizers/meta_optimizer.cc:818]   function_optimizer: function_optimizer did nothing. time = 0.482ms.\n",
            "I1117 13:46:38.852302 140653191419776 lite.py:624] Using experimental converter: If you encountered a problem please file a bug. You can opt-out by setting experimental_new_converter=False\n",
            "2020-11-17 13:46:39.022922: W tensorflow/compiler/mlir/lite/python/tf_tfl_flatbuffer_helpers.cc:313] Ignored output_format.\n",
            "2020-11-17 13:46:39.022987: W tensorflow/compiler/mlir/lite/python/tf_tfl_flatbuffer_helpers.cc:316] Ignored drop_control_dependency.\n",
            "2020-11-17 13:46:39.123010: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-11-17 13:46:39.124068: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1716] Found device 0 with properties: \n",
            "pciBusID: 0000:00:04.0 name: Tesla P100-PCIE-16GB computeCapability: 6.0\n",
            "coreClock: 1.3285GHz coreCount: 56 deviceMemorySize: 15.90GiB deviceMemoryBandwidth: 681.88GiB/s\n",
            "2020-11-17 13:46:39.124136: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1\n",
            "2020-11-17 13:46:39.124225: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcublas.so.10\n",
            "2020-11-17 13:46:39.124281: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcufft.so.10\n",
            "2020-11-17 13:46:39.124345: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcurand.so.10\n",
            "2020-11-17 13:46:39.124394: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcusolver.so.10\n",
            "2020-11-17 13:46:39.124446: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcusparse.so.10\n",
            "2020-11-17 13:46:39.124497: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudnn.so.7\n",
            "2020-11-17 13:46:39.124603: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-11-17 13:46:39.125528: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-11-17 13:46:39.126396: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1858] Adding visible gpu devices: 0\n",
            "2020-11-17 13:46:39.126463: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1257] Device interconnect StreamExecutor with strength 1 edge matrix:\n",
            "2020-11-17 13:46:39.126492: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1263]      0 \n",
            "2020-11-17 13:46:39.126519: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1276] 0:   N \n",
            "2020-11-17 13:46:39.126652: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-11-17 13:46:39.127610: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-11-17 13:46:39.128471: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1402] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 14968 MB memory) -> physical GPU (device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0)\n",
            "I1117 13:46:39.477798 140653191419776 convert_tflite.py:48] model saved to: ./checkpoints/yolov4-tiny-416.tflite\n",
            "I1117 13:46:39.556870 140653191419776 convert_tflite.py:53] tflite model loaded\n",
            "[{'name': 'input_1', 'index': 0, 'shape': array([  1, 416, 416,   3], dtype=int32), 'shape_signature': array([ -1, 416, 416,   3], dtype=int32), 'dtype': <class 'numpy.float32'>, 'quantization': (0.0, 0), 'quantization_parameters': {'scales': array([], dtype=float32), 'zero_points': array([], dtype=int32), 'quantized_dimension': 0}, 'sparsity_parameters': {}}]\n",
            "[{'name': 'Identity', 'index': 233, 'shape': array([   1, 2535,    4], dtype=int32), 'shape_signature': array([ 1, -1,  4], dtype=int32), 'dtype': <class 'numpy.float32'>, 'quantization': (0.0, 0), 'quantization_parameters': {'scales': array([], dtype=float32), 'zero_points': array([], dtype=int32), 'quantized_dimension': 0}, 'sparsity_parameters': {}}, {'name': 'Identity_1', 'index': 212, 'shape': array([   1, 2535,    4], dtype=int32), 'shape_signature': array([ 1, -1,  4], dtype=int32), 'dtype': <class 'numpy.float32'>, 'quantization': (0.0, 0), 'quantization_parameters': {'scales': array([], dtype=float32), 'zero_points': array([], dtype=int32), 'quantized_dimension': 0}, 'sparsity_parameters': {}}]\n",
            "[array([[[ 10.212377 ,   5.9344354,  31.472723 ,  14.663492 ],\n",
            "        [ 24.444035 ,   6.40177  ,  53.239906 ,  16.99858  ],\n",
            "        [ 40.28573  ,   5.607392 ,  82.06701  ,  17.130598 ],\n",
            "        ...,\n",
            "        [329.24512  , 389.58636  , 428.5636   , 213.73412  ],\n",
            "        [362.6996   , 391.05682  , 424.98953  , 228.35379  ],\n",
            "        [394.5913   , 395.64343  , 395.7347   , 253.74648  ]]],\n",
            "      dtype=float32), array([[[9.50818139e-05, 6.64808795e-06, 1.08475342e-05, 1.72526052e-05],\n",
            "        [1.48358537e-04, 5.68912719e-06, 7.77752211e-06, 2.14417287e-05],\n",
            "        [8.84597539e-05, 5.48706794e-06, 4.45091746e-06, 1.40210077e-05],\n",
            "        ...,\n",
            "        [7.12270243e-09, 1.45808352e-08, 1.56846740e-11, 3.23432586e-10],\n",
            "        [5.12886889e-09, 1.05976214e-08, 1.22278064e-11, 1.94101207e-10],\n",
            "        [2.86341750e-09, 3.16413007e-09, 2.35796018e-11, 2.99813008e-10]]],\n",
            "      dtype=float32)]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jOJz36iOGS0G",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4f6709dd-7fac-42be-dbf5-f86621f3cd27"
      },
      "source": [
        "!ls /content/darknet/test"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            " 2w2w2w2w_jpg.rf.51547d31b73f7f1324c979b8abc6cd1d.jpg\n",
            " 2w2w2w2w_jpg.rf.51547d31b73f7f1324c979b8abc6cd1d.txt\n",
            " _darknet.labels\n",
            "'GundhiBug (28)_jpg.rf.6a9642d7c508c058991df399c58172ee.jpg'\n",
            "'GundhiBug (28)_jpg.rf.6a9642d7c508c058991df399c58172ee.txt'\n",
            "'GundhiBug (37)_jpg.rf.cde703d084b80863bba9dc7da3c66093.jpg'\n",
            "'GundhiBug (37)_jpg.rf.cde703d084b80863bba9dc7da3c66093.txt'\n",
            " Gundhi_jpg.rf.880285202d09384bbb56045118a2f734.jpg\n",
            " Gundhi_jpg.rf.880285202d09384bbb56045118a2f734.txt\n",
            "'images (22)_jpg.rf.e74ec3932620191f56c0133acc71c2b7.jpg'\n",
            "'images (22)_jpg.rf.e74ec3932620191f56c0133acc71c2b7.txt'\n",
            "'images (39)_jpg.rf.5bae34e79cc5f50410f89528eecb2413.jpg'\n",
            "'images (39)_jpg.rf.5bae34e79cc5f50410f89528eecb2413.txt'\n",
            "'OIP (4)_jpg.rf.94b1736443fa72a35e83d28f239f2119.jpg'\n",
            "'OIP (4)_jpg.rf.94b1736443fa72a35e83d28f239f2119.txt'\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vUPDhisfF-b5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "39d50f12-b702-4410-9093-c2a6d17f4092"
      },
      "source": [
        "# Verify\n",
        "%cd /content/tensorflow-yolov4-tflite\n",
        "!python detect.py --weights ./checkpoints/yolov4-tiny-416 --size 416 --model yolov4 \\\n",
        "  --image /content/darknet/test/images (22)_jpg.rf.e74ec3932620191f56c0133acc71c2b7.jpg \\\n",
        "  # --framework tflite"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/tensorflow-yolov4-tflite\n",
            "/bin/bash: -c: line 0: syntax error near unexpected token `('\n",
            "/bin/bash: -c: line 0: `python detect.py --weights ./checkpoints/yolov4-tiny-416 --size 416 --model yolov4   --image /content/darknet/test/images (22)_jpg.rf.e74ec3932620191f56c0133acc71c2b7.jpg   # --framework tflite'\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L3PL0QTpIZwe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 552
        },
        "outputId": "8627dedd-ad78-43c3-dce3-1f742f803d56"
      },
      "source": [
        "%cd /content/tensorflow-yolov4-tflite/\n",
        "!ls\n",
        "from IPython.display import Image\n",
        "Image('/content/tensorflow-yolov4-tflite/result.png')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/tensorflow-yolov4-tflite\n",
            "android\t\t    core\t    mAP\t\t\t  save_model.py\n",
            "benchmarks.py\t    data\t    README.md\t\t  scripts\n",
            "checkpoints\t    detect.py\t    requirements-gpu.txt  train.py\n",
            "CODE_OF_CONDUCT.md  detectvideo.py  requirements.txt\n",
            "convert_tflite.py   evaluate.py     result-int8.png\n",
            "convert_trt.py\t    LICENSE\t    result.png\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HaJjTg2HPEX3"
      },
      "source": [
        "# Save your Model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Fv9Zc5loPbL5"
      },
      "source": [
        "You can save your model to your Google Drive for further use."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "he7-AJfJPh-K"
      },
      "source": [
        "# Choose what to copy\n",
        "\n",
        "# TensorFlow SavedModel\n",
        "!cp -r /content/tensorflow-yolov4-tflite/checkpoints/yolov4-tiny-416/ \"/content/drive/My Drive\"\n",
        "# TensorFlow Lite\n",
        "!cp /content/tensorflow-yolov4-tflite/checkpoints/yolov4-tiny-416.tflite \"/content/drive/My Drive\""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A6FzOh-tdg0u"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}