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Real time traffic vehicles instance segmentation with YOLACT++

This work is inspired from Immersive Limit.

im3

YOLACT (You Only Look At Coefficients) is a simple, fully convolutional model for real-time instance segmentation. The latest version of it which was released on 16th Decemeber 2019, is called YOLACT++. It currently supports two backbone networks : Resnet50-FPN and Resnet101-FPN. I have used Resnet50 version for this project. It is originally trained on a very sophisticated graphics card, i.e., Titan Xp. That is the reason why I have not trained the model on my local computer and just used the pre-trained weights provided by the authors of this technology.

Check out YOLACT and YOLACT++ papers:

YOLACT : https://arxiv.org/abs/1904.02689

YOLACT++ : https://arxiv.org/abs/1912.06218

Also, check out the original github repo of YOLACT here.

Initial setup

Run the following commands for the initial setup:

# Cython needs to be installed before pycocotools
!pip install cython
!pip install opencv-python pillow pycocotools matplotlib

# Downgrade torch to accommodate DCNv2
!pip install torchvision==0.5.0
!pip install torch==1.4.0

# Clone the yolact repo
!git clone https://github.com/dbolya/yolact.git

# The DCNv2 external library is needed for this to work
%cd /content/yolact/external/DCNv2

# Build DCNv2
!python setup.py build develop

Download the pre-trained weights

In order to run inference, we'll need some pre-trained weights. The creator of the GitHub repo shared them on Google Drive. We're going to use a helpful tool made by chentinghao to easily access the Drive file from Colab.

# Make sure we're in the top folder
%cd /content

# Clone the repo
!git clone https://github.com/chentinghao/download_google_drive.git

# Create a new directory for the pre-trained weights
!mkdir -p /content/yolact/weights

# Download the pre-trained weights file
!python ./download_google_drive/download_gdrive.py 1ZPu1YR2UzGHQD0o1rEqy-j5bmEm3lbyP ./yolact/weights/yolact_plus_resnet50_54_800000.pth

Other regular data science libs

import cv2
import numpy as np
from matplotlib import pyplot as plt
from pathlib import Path

Training

I have used a video of cars and buses going by on a busy street. It also has pedestrians walking by the streets in a crowded fashion. The video is attached here.

After the training process was over, I got a pretty good result of an instance segmented video that is attached here.