YOLOv5-Lite：Lighter, faster and easier to deploy

Python>=3.6.0 is required with all requirements.txt installed including PyTorch>=1.7:

$ git clone https://github.com/ppogg/YOLOv5-Lite
$ cd YOLOv5-Lite
$ pip install -r requirements.txt

detect.py runs inference on a variety of sources, downloading models automatically from the latest YOLOv5-Lite release and saving results to runs/detect.

$ python detect.py --source 0  # webcam
                            file.jpg  # image 
                            file.mp4  # video
                            path/  # directory
                            path/*.jpg  # glob
                            'https://youtu.be/NUsoVlDFqZg'  # YouTube
                            'rtsp://example.com/media.mp4'  # RTSP, RTMP, HTTP stream

$ python train.py --data coco.yaml --cfg v5lite-e.yaml --weights v5lite-e.pt --batch-size 128
                                         v5lite-s.yaml           v5lite-s.pt              128
                                         v5lite-c.yaml           v5lite-c.pt               96
                                         v5lite-g.yaml           v5lite-g.pt               64

If you use multi-gpu. It's faster several times:

$ python -m torch.distributed.launch --nproc_per_node 2 train.py

Training set and test set distribution （the path with xx.jpg）

train: ../coco/images/train2017/
val: ../coco/images/val2017/

├── images            # xx.jpg example
│   ├── train2017        
│   │   ├── 000001.jpg
│   │   ├── 000002.jpg
│   │   └── 000003.jpg
│   └── val2017         
│       ├── 100001.jpg
│       ├── 100002.jpg
│       └── 100003.jpg
└── labels             # xx.txt example      
    ├── train2017       
    │   ├── 000001.txt
    │   ├── 000002.txt
    │   └── 000003.txt
    └── val2017         
        ├── 100001.txt
        ├── 100002.txt
        └── 100003.txt

Link ：https://github.com/ppogg/AutoLabelImg

You can use LabelImg based YOLOv5-5.0 and YOLOv5-Lite to AutoAnnotate, biubiubiu 🚀 🚀 🚀

Here, the original components of YOLOv5 and the reproduced components of YOLOv5-Lite are organized and stored in the model hub：

$ python main.py --type all

How to deploy

ncnn for arm-cpu

mnn for arm-cpu

openvino x86-cpu or x86-vpu

tensorrt(C++) for arm-gpu or arm-npu or x86-gpu

tensorrt(Python) for arm-gpu or arm-npu or x86-gpu

Android for arm-cpu

Android_demo

This is a Redmi phone, the processor is Snapdragon 730G, and yolov5-lite is used for detection. The performance is as follows:

link: https://github.com/ppogg/YOLOv5-Lite/tree/master/android_demo/ncnn-android-v5lite

Android_v5Lite-s: https://drive.google.com/file/d/1CtohY68N2B9XYuqFLiTp-Nd2kuFWgAUR/view?usp=sharing

Android_v5Lite-g: https://drive.google.com/file/d/1FnvkWxxP_aZwhi000xjIuhJ_OhqOUJcj/view?usp=sharing

new android app:[link] https://pan.baidu.com/s/1PRhW4fI1jq8VboPyishcIQ [keyword] pogg

More detailed explanation

Detailed model link:

What is YOLOv5-Lite S/E model: zhihu link (Chinese): https://zhuanlan.zhihu.com/p/400545131

What is YOLOv5-Lite C model: zhihu link (Chinese): https://zhuanlan.zhihu.com/p/420737659

What is YOLOv5-Lite G model: zhihu link (Chinese): https://zhuanlan.zhihu.com/p/410874403

How to deploy on ncnn with fp16 or int8: csdn link (Chinese): https://blog.csdn.net/weixin_45829462/article/details/119787840

How to deploy on mnn with fp16 or int8: zhihu link (Chinese): https://zhuanlan.zhihu.com/p/672633849

How to deploy on onnxruntime: zhihu link (Chinese): https://zhuanlan.zhihu.com/p/476533259(old version)

How to deploy on tensorrt: zhihu link (Chinese): https://zhuanlan.zhihu.com/p/478630138

How to optimize on tensorrt: zhihu link (Chinese): https://zhuanlan.zhihu.com/p/463074494

Reference

https://github.com/ultralytics/yolov5

https://github.com/megvii-model/ShuffleNet-Series

https://github.com/Tencent/ncnn

Citing YOLOv5-Lite

If you use YOLOv5-Lite in your research, please cite our work and give a star ⭐:

 @misc{yolov5lite2021,
  title = {YOLOv5-Lite: Lighter, faster and easier to deploy},
  author = {Xiangrong Chen and Ziman Gong},
  doi = {10.5281/zenodo.5241425}
  year={2021}
}