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Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow

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Blueprint for Mask R-CNN for Object Detection and Segmentation

This is an implementation to matterport's Mask_RCNN repository. Using python3, Keras, and tensorflow, this model detects, segments and builds bounding boxes around objects.

Instance Segmentation Sample

The repository contains:

  • All files contained in the parent repository
  • An added "CoinCounter" class, which counts the total value of coins in a photo
  • A "Blueprint" directory, which contains a blackbox blueprint_class and a blackbox blueprint_inspect_data .py file.

About the blueprint

The goal of this repository is to create a system for generalized and easily configurable object detection and segmentation. The blueprint class streamlines the processes of training, testing and inference.

Getting started

-0. Clone this repository.

-1. Download the COCO weights. You can optionally download the coin and balloon dataset as well. Place the coco weights in your repository and create a directory called "datasets" for the datasets.

-2. Create a "logs" file to save training progress (weights after every epoch and scalers for tensorboard).

-3. Install all of the dependencies > pip3 install -r requirements

-4. Run the setup.py file > python3 setup.py install

Instance Segmentation Sample

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Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow

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