Slow-Fast-pytorch-implementation with Colab notebook

Run the demo on your own data

1.Clone the repository: git clone https://github.com/vaib-saxena/Slow-Fast-pytorch-implementation.git

2.Download Yolo v3 weights: https://drive.google.com/file/d/1SSpVueL6W_4BE3sFDkzAgdMd35Mtl2N5/view?usp=sharing and paste in the directory

3.Download DeepSort re-id weights: https://drive.google.com/file/d/1bwLHXS5TocUfDL2-iLNJLs8WfUOZtg9B/view?usp=sharing and paste in deep\checkpoint directory

4.Download Pre-trained SlowFast Network weights: https://drive.google.com/file/d/1ooE-qh7LBL7kWceZRHPyIIBslWCBwdwy/view?usp=sharing and paste in the directory

5.Modify the weights path and your video path in video_demo.py.

6.Run video_demo.py.

Colab notebook

Dependencies

• python 3 (python2 not sure)
• numpy
• scipy
• opencv-python
• torch >= 1.0.0
• torchvision = 0.2.1
• youtube-dl
• ffmpeg

Reference

• paper: Slow Fast Networks

• code: facebookresearch/SlowFast

• https://github.com/MagicChuyi/SlowFast-Network-pytorch

• paper: Simple Online and Realtime Tracking with a Deep Association Metric

• code: nwojke/deep_sort

• paper: YOLOv3

• code: Joseph Redmon/yolov3