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