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https://github.com/wufan-tb/yolo_slowfast
Yolov5+SlowFast: Realtime Action Detection Based on PytorchVideo
https://github.com/wufan-tb/yolo_slowfast
slowfast yolov5
Last synced: 3 days ago
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Yolov5+SlowFast: Realtime Action Detection Based on PytorchVideo
- Host: GitHub
- URL: https://github.com/wufan-tb/yolo_slowfast
- Owner: wufan-tb
- Created: 2021-12-27T09:06:04.000Z (about 3 years ago)
- Default Branch: master
- Last Pushed: 2023-04-14T01:17:24.000Z (almost 2 years ago)
- Last Synced: 2025-02-10T20:45:55.944Z (10 days ago)
- Topics: slowfast, yolov5
- Language: Python
- Homepage:
- Size: 7.04 MB
- Stars: 484
- Watchers: 4
- Forks: 58
- Open Issues: 25
-
Metadata Files:
- Readme: readme.md
Awesome Lists containing this project
- awesome-yolo-object-detection - wufan-tb/yolo_slowfast - tb/yolo_slowfast?style=social"/> : A realtime action detection frame work based on PytorchVideo. (Applications)
- awesome-yolo-object-detection - wufan-tb/yolo_slowfast - tb/yolo_slowfast?style=social"/> : A realtime action detection frame work based on PytorchVideo. (Applications)
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).
#### 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=3.7.11
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
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