# python_yolo_slowfast_demo

**Repository Path**: yuanzhengme/python_yolo_slowfast_demo

## Basic Information

- **Project Name**: python_yolo_slowfast_demo
- **Description**: yolo_slowfast测试
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2025-07-04
- **Last Updated**: 2025-07-08

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Yolov5+SlowFast: Realtime Action Detection

### A realtime action detection frame work based on PytorchVideo. 

#### Here are some details about our modification:

- we choose yolov5 as an object detector instead of Faster R-CNN, it is faster and more convenient
- we use a tracker(deepsort) to allocate action labels to all objects(with same ids) in different frames
- our processing speed reached 24.2 FPS at 30 inference batch size (on a single RTX 2080Ti GPU)

> Relevant infomation: [FAIR/PytorchVideo](https://github.com/facebookresearch/pytorchvideo); [Ultralytics/Yolov5](https://github.com/ultralytics/yolov5)

#### Demo comparison between original(<-left) and ours(->right).

<img src="./demo/ava_slowfast.gif" width="400" /><img src="./demo/yolov5+slowfast.gif" width="400" />

#### Update Log:


- 2023.03.31  fix some bugs(maybe caused by yolov5 version upgrade), support real time testing(test on camera or video stearm).

- 2022.01.24  optimize pre-process method(no need to extract video to image before processing), faster and cleaner.


## Installation

1. clone this repo:

   ```
   git clone https://github.com/wufan-tb/yolo_slowfast
   cd yolo_slowfast
   ```

2. create a new python environment (optional):

   ```
   conda create -n {your_env_name} python=3.9.18
   conda activate {your_env_name}
   ```

3. install requiments:

   ```
   pip install -r requirements.txt
   ```
   
4. download weights file(ckpt.t7) from [[deepsort]](https://drive.google.com/drive/folders/1xhG0kRH1EX5B9_Iz8gQJb7UNnn_riXi6) to this folder:

   ```
   ./deep_sort/deep_sort/deep/checkpoint/
   ```

5. test on your video/camera/stream:


   ```
   python yolo_slowfast.py --input {path to your video/camera/stream}
   ```

   The first time execute this command may take some times to download the yolov5 code and it's weights file from torch.hub, keep your network connection.

   set `--input 0` to test on your local camera, set `--input {stream path, such as "rtsp://xxx" or "rtmp://xxxx"}` to test on viewo stream.


## References

Thanks for these great works:

[1] [Ultralytics/Yolov5](https://github.com/ultralytics/yolov5)

[2] [ZQPei/deepsort](https://github.com/ZQPei/deep_sort_pytorch) 

[3] [FAIR/PytorchVideo](https://github.com/facebookresearch/pytorchvideo)

[4] AVA: A Video Dataset of Spatio-temporally Localized Atomic Visual Actions. [paper](https://arxiv.org/pdf/1705.08421.pdf)

[5] SlowFast Networks for Video Recognition. [paper](https://arxiv.org/pdf/1812.03982.pdf)

## Citation

If you find our work useful, please cite as follow:

```
{   yolo_slowfast,
    author = {Wu Fan},
    title = { A realtime action detection frame work based on PytorchVideo},
    year = {2021},
    url = {\url{https://github.com/wufan-tb/yolo_slowfast}}
}
```

### Stargazers over time

[![Stargazers over time](https://starchart.cc/wufan-tb/yolo_slowfast.svg)](https://starchart.cc/wufan-tb/yolo_slowfast)