{"id":15031143,"url":"https://github.com/ppogg/yolov5-lite","last_synced_at":"2025-05-15T07:03:50.806Z","repository":{"id":37401841,"uuid":"396828995","full_name":"ppogg/YOLOv5-Lite","owner":"ppogg","description":"🍅🍅🍅YOLOv5-Lite: Evolved from yolov5 and the size of model is only 900+kb (int8) and 1.7M (fp16). 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It can infer at least 10+ FPS On the Raspberry Pi 4B when input the frame with 320×320) and is easier to deploy (removing the Focus layer and four slice operations, reducing the model quantization accuracy to an acceptable range).\n\n![image](https://user-images.githubusercontent.com/82716366/135564164-3ec169c8-93a7-4ea3-b0dc-40f1059601ef.png)\n\n## Comparison of ablation experiment results\n\n  ID|Model | Input_size|Flops| Params | Size（M） |Map@0.5|Map@.5:0.95\n :-----:|:-----:|:-----:|:----------:|:----:|:----:|:----:|:----:|\n001| yolo-fastest| 320×320|0.25G|0.35M|1.4| 24.4| -\n002| YOLOv5-Lite\u003csub\u003ee\u003c/sub\u003e\u003csup\u003eours\u003c/sup\u003e|320×320|0.73G|0.78M|1.7| 35.1|-|\n003| NanoDet-m| 320×320| 0.72G|0.95M|1.8|- |20.6\n004| yolo-fastest-xl| 320×320|0.72G|0.92M|3.5| 34.3| -\n005| YOLOX\u003csub\u003eNano\u003c/sub\u003e|416×416|1.08G|0.91M|7.3(fp32)| -|25.8|\n006| yolov3-tiny| 416×416| 6.96G|6.06M|23.0| 33.1|16.6\n007| yolov4-tiny| 416×416| 5.62G|8.86M| 33.7|40.2|21.7\n008| YOLOv5-Lite\u003csub\u003es\u003c/sub\u003e\u003csup\u003eours\u003c/sup\u003e| 416×416|1.66G |1.64M|3.4| 42.0|25.2\n009| YOLOv5-Lite\u003csub\u003ec\u003c/sub\u003e\u003csup\u003eours\u003c/sup\u003e| 512×512|5.92G |4.57M|9.2| 50.9|32.5| \n010| NanoDet-EfficientLite2| 512×512| 7.12G|4.71M|18.3|- |32.6\n011| YOLOv5s(6.0)| 640×640| 16.5G|7.23M|14.0| 56.0|37.2\n012| YOLOv5-Lite\u003csub\u003eg\u003c/sub\u003e\u003csup\u003eours\u003c/sup\u003e| 640×640|15.6G |5.39M|10.9| 57.6|39.1| \n\nSee the wiki: https://github.com/ppogg/YOLOv5-Lite/wiki/Test-the-map-of-models-about-coco\n\n## Comparison on different platforms\n\nEquipment|Computing backend|System|Input|Framework|v5lite-e|v5lite-s|v5lite-c|v5lite-g|YOLOv5s\n:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:\nInter|@i5-10210U|window(x86)|640×640|openvino|-|-|46ms|-|131ms\nNvidia|@RTX 2080Ti|Linux(x86)|640×640|torch|-|-|-|15ms|14ms\nRedmi K30|@Snapdragon 730G|Android(armv8)|320×320|ncnn|27ms|38ms|-|-|163ms\nXiaomi 10|@Snapdragon 865|Android(armv8)|320×320|ncnn|10ms|14ms|-|-|163ms\nRaspberrypi 4B|@ARM Cortex-A72|Linux(arm64)|320×320|ncnn|-|84ms|-|-|371ms\nRaspberrypi 4B|@ARM Cortex-A72|Linux(arm64)|320×320|mnn|-|71ms|-|-|356ms\nAXera-Pi|Cortex A7@CPU\u003cbr /\u003e3.6TOPs @NPU|Linux(arm64)|640×640|axpi|-|-|-|22ms|22ms\n\n### The tutorial of 15FPS on Raspberry Pi 4B:\n[https://zhuanlan.zhihu.com/p/672633849](https://zhuanlan.zhihu.com/p/672633849)\n\n* The above is a 4-thread test benchmark\n* Raspberrypi 4B enable bf16s optimization，[Raspberrypi 64 Bit OS](http://downloads.raspberrypi.org/raspios_arm64/images/raspios_arm64-2020-08-24/)\n\n###  qq交流群：993965802\n\n入群答案:剪枝 or 蒸馏 or 量化 or 低秩分解（任意其一均可）\n\n##  ·Model Zoo· \n\n#### @v5lite-e:\n\nModel|Size|Backbone|Head|Framework|Design for\n:---:|:---:|:---:|:---:|:---:|:---\nv5Lite-e.pt|1.7m|shufflenetv2（Megvii）|v5Litee-head|Pytorch|Arm-cpu\nv5Lite-e.bin\u003cbr /\u003ev5Lite-e.param|1.7m|shufflenetv2|v5Litee-head|ncnn|Arm-cpu\nv5Lite-e-int8.bin\u003cbr /\u003ev5Lite-e-int8.param|0.9m|shufflenetv2|v5Litee-head|ncnn|Arm-cpu\nv5Lite-e-fp32.mnn|3.0m|shufflenetv2|v5Litee-head|mnn|Arm-cpu\nv5Lite-e-fp32.tnnmodel\u003cbr /\u003ev5Lite-e-fp32.tnnproto|2.9m|shufflenetv2|v5Litee-head|tnn|arm-cpu\nv5Lite-e-320.onnx|3.1m|shufflenetv2|v5Litee-head|onnxruntime|x86-cpu\n\n#### @v5lite-s:\n\nModel|Size|Backbone|Head|Framework|Design for\n:---:|:---:|:---:|:---:|:---:|:---\nv5Lite-s.pt|3.4m|shufflenetv2（Megvii）|v5Lites-head|Pytorch|Arm-cpu\nv5Lite-s.bin\u003cbr /\u003ev5Lite-s.param|3.3m|shufflenetv2|v5Lites-head|ncnn|Arm-cpu\nv5Lite-s-int8.bin\u003cbr /\u003ev5Lite-s-int8.param|1.7m|shufflenetv2|v5Lites-head|ncnn|Arm-cpu\nv5Lite-s.mnn|3.3m|shufflenetv2|v5Lites-head|mnn|Arm-cpu\nv5Lite-s-int4.mnn|987k|shufflenetv2|v5Lites-head|mnn|Arm-cpu\nv5Lite-s-fp16.bin\u003cbr /\u003ev5Lite-s-fp16.xml|3.4m|shufflenetv2|v5Lites-head|openvivo|x86-cpu\nv5Lite-s-fp32.bin\u003cbr /\u003ev5Lite-s-fp32.xml|6.8m|shufflenetv2|v5Lites-head|openvivo|x86-cpu\nv5Lite-s-fp16.tflite|3.3m|shufflenetv2|v5Lites-head|tflite|arm-cpu\nv5Lite-s-fp32.tflite|6.7m|shufflenetv2|v5Lites-head|tflite|arm-cpu\nv5Lite-s-int8.tflite|1.8m|shufflenetv2|v5Lites-head|tflite|arm-cpu\nv5Lite-s-416.onnx|6.4m|shufflenetv2|v5Lites-head|onnxruntime|x86-cpu\n\n#### @v5lite-c:\n\nModel|Size|Backbone|Head|Framework|Design for\n:---:|:---:|:---:|:---:|:---:|:---:\nv5Lite-c.pt|9m|PPLcnet（Baidu）|v5s-head|Pytorch|x86-cpu / x86-vpu\nv5Lite-c.bin\u003cbr /\u003ev5Lite-c.xml|8.7m|PPLcnet|v5s-head|openvivo|x86-cpu / x86-vpu\nv5Lite-c-512.onnx|18m|PPLcnet|v5s-head|onnxruntime|x86-cpu\n\n#### @v5lite-g:\n\nModel|Size|Backbone|Head|Framework|Design for\n:---:|:---:|:---:|:---:|:---:|:---:\nv5Lite-g.pt|10.9m|Repvgg（Tsinghua）|v5Liteg-head|Pytorch|x86-gpu / arm-gpu / arm-npu\nv5Lite-g-int8.engine|8.5m|Repvgg-yolov5|v5Liteg-head|Tensorrt|x86-gpu / arm-gpu / arm-npu\nv5lite-g-int8.tmfile|8.7m|Repvgg-yolov5|v5Liteg-head|Tengine| arm-npu\nv5Lite-g-640.onnx|21m|Repvgg-yolov5|yolov5-head|onnxruntime|x86-cpu\nv5Lite-g-640.joint|7.1m|Repvgg-yolov5|yolov5-head|axpi|arm-npu\n\n#### Download Link：\n\n\u003e - [ ] `v5lite-e.pt`:   | [Baidu Drive](https://pan.baidu.com/s/1bjXo7KIFkOnB3pxixHeMPQ)  | [Google Drive](https://drive.google.com/file/d/1_DvT_qjznuE-ev_pDdGKwRV3MjZ3Zos8/view?usp=sharing) |\u003cbr\u003e \n\u003e\u003e |──────`ncnn-fp16`:   | [Baidu Drive](https://pan.baidu.com/s/1_QvWvkhHB7kdcRZ6k4at1g)  | [Google Drive](https://drive.google.com/drive/folders/1w4mThJmqjhT1deIXMQAQ5xjWI3JNyzUl?usp=sharing) |\u003cbr\u003e \n\u003e\u003e |──────`ncnn-int8`: | [Baidu Drive](https://pan.baidu.com/s/1JO8qbbVx6zJ-6aq5EgM6PA) | [Google Drive](https://drive.google.com/drive/folders/1YNtNVWlRqN8Dwc_9AtRkN0LFkDeJ92gN?usp=sharing) |\u003cbr\u003e\n\u003e\u003e |──────`mnn-e_bf16`: | [Google Drive](https://drive.google.com/file/d/1_yGGCJFat2bjaKEwxJjrFcU2mGEhH8I3/view?usp=sharing) |\u003cbr\u003e \n\u003e\u003e |──────`mnn-d_bf16`: | [Google Drive](https://drive.google.com/file/d/1TqijmdQtWd6ZUr2CiNLuHCEQnPUw1oVu/view?usp=sharing)|\u003cbr\u003e \n\u003e\u003e └──────`onnx-fp32`: | [Baidu Drive](https://pan.baidu.com/s/1zIqKmOavRIrV8UJxbQWvhA) | [Google Drive](https://drive.google.com/file/d/1ot9eNlFMqMEzt_FHf0SkDHj1fOprOnTU/view?usp=sharing) |\u003cbr\u003e \n\u003e - [ ] `v5lite-s.pt`:   | [Baidu Drive](https://pan.baidu.com/s/1j0n0K1kqfv1Ouwa2QSnzCQ)  | [Google Drive](https://drive.google.com/file/d/1ccLTmGB5AkKPjDOyxF3tW7JxGWemph9f/view?usp=sharing) |\u003cbr\u003e \n\u003e\u003e |──────`ncnn-fp16`:   | [Baidu Drive](https://pan.baidu.com/s/1kWtwx1C0OTTxbwqJyIyXWg)  | [Google Drive](https://drive.google.com/drive/folders/1w4mThJmqjhT1deIXMQAQ5xjWI3JNyzUl?usp=sharing) |\u003cbr\u003e \n\u003e\u003e |──────`ncnn-int8`: | [Baidu Drive](https://pan.baidu.com/s/1QX6-oNynrW-f3i0P0Hqe4w) | [Google Drive](https://drive.google.com/drive/folders/1YNtNVWlRqN8Dwc_9AtRkN0LFkDeJ92gN?usp=sharing) |\u003cbr\u003e \n\u003e\u003e └──────`tengine-fp32`: | [Baidu Drive](https://pan.baidu.com/s/123r630O8Fco7X59wFU1crA) | [Google Drive](https://drive.google.com/drive/folders/1VWmI2BC9MjH7BsrOz4VlSDVnZMXaxGOE?usp=sharing) |\u003cbr\u003e                \n\u003e - [ ] `v5lite-c.pt`: [Baidu Drive](https://pan.baidu.com/s/1obs6uRB79m8e3uASVR6P1A) | [Google Drive](https://drive.google.com/file/d/1lHYRQKjqKCRXghUjwWkUB0HQ8ccKH6qa/view?usp=sharing) |\u003cbr\u003e \n\u003e\u003e └──────`openvino-fp16`: | [Baidu Drive](https://pan.baidu.com/s/18p8HAyGJdmo2hham250b4A) | [Google Drive](https://drive.google.com/drive/folders/1s4KPSC4B0shG0INmQ6kZuPLnlUKAATyv?usp=sharing) |\u003cbr\u003e \n\u003e - [ ] `v5lite-g.pt`: | [Baidu Drive](https://pan.baidu.com/s/14zdTiTMI_9yTBgKGbv9pQw) | [Google Drive](https://drive.google.com/file/d/1oftzqOREGqDCerf7DtD5BZp9YWELlkMe/view?usp=sharing) |\u003cbr\u003e \n\u003e\u003e └──────`axpi-int8`: [Google Drive](https://github.com/AXERA-TECH/ax-models/blob/main/ax620/v5Lite-g-sim-640.joint) |\u003cbr\u003e \n\n\n\nBaidu Drive Password: `pogg`\n\n#### v5lite-s model: TFLite Float32, Float16, INT8, Dynamic range quantization, ONNX, TFJS, TensorRT, OpenVINO IR FP32/FP16, Myriad Inference Engin Blob, CoreML\n[https://github.com/PINTO0309/PINTO_model_zoo/tree/main/180_YOLOv5-Lite](https://github.com/PINTO0309/PINTO_model_zoo/tree/main/180_YOLOv5-Lite)\n\n#### Thanks for PINTO0309:[https://github.com/PINTO0309/PINTO_model_zoo/tree/main/180_YOLOv5-Lite](https://github.com/PINTO0309/PINTO_model_zoo/tree/main/180_YOLOv5-Lite)\n\n\n## \u003cdiv\u003eHow to use\u003c/div\u003e\n\n\u003cdetails open\u003e\n\u003csummary\u003eInstall\u003c/summary\u003e\n\n[**Python\u003e=3.6.0**](https://www.python.org/) is required with all\n[requirements.txt](https://github.com/ppogg/YOLOv5-Lite/blob/master/requirements.txt) installed including\n[**PyTorch\u003e=1.7**](https://pytorch.org/get-started/locally/):\n\u003c!-- $ sudo apt update \u0026\u0026 apt install -y libgl1-mesa-glx libsm6 libxext6 libxrender-dev --\u003e\n\n```bash\n$ git clone https://github.com/ppogg/YOLOv5-Lite\n$ cd YOLOv5-Lite\n$ pip install -r requirements.txt\n```\n\n\u003c/details\u003e\n\n\u003cdetails\u003e\n\u003csummary\u003eInference with detect.py\u003c/summary\u003e\n\n`detect.py` runs inference on a variety of sources, downloading models automatically from\nthe [latest YOLOv5-Lite release](https://github.com/ppogg/YOLOv5-Lite/releases) and saving results to `runs/detect`.\n\n```bash\n$ python detect.py --source 0  # webcam\n                            file.jpg  # image \n                            file.mp4  # video\n                            path/  # directory\n                            path/*.jpg  # glob\n                            'https://youtu.be/NUsoVlDFqZg'  # YouTube\n                            'rtsp://example.com/media.mp4'  # RTSP, RTMP, HTTP stream\n```\n\n\u003c/details\u003e\n\n\u003cdetails open\u003e\n\u003csummary\u003eTraining\u003c/summary\u003e\n\n```bash\n$ python train.py --data coco.yaml --cfg v5lite-e.yaml --weights v5lite-e.pt --batch-size 128\n                                         v5lite-s.yaml           v5lite-s.pt              128\n                                         v5lite-c.yaml           v5lite-c.pt               96\n                                         v5lite-g.yaml           v5lite-g.pt               64\n```\n\n If you use multi-gpu. It's faster several times:\n  \n ```bash\n$ python -m torch.distributed.launch --nproc_per_node 2 train.py\n```\n  \n\u003c/details\u003e  \n\n\u003c/details\u003e\n\n\u003cdetails open\u003e\n\u003csummary\u003eDataSet\u003c/summary\u003e\n\nTraining set and test set distribution （the path with xx.jpg）\n  \n ```bash\ntrain: ../coco/images/train2017/\nval: ../coco/images/val2017/\n```\n```bash\n├── images            # xx.jpg example\n│   ├── train2017        \n│   │   ├── 000001.jpg\n│   │   ├── 000002.jpg\n│   │   └── 000003.jpg\n│   └── val2017         \n│       ├── 100001.jpg\n│       ├── 100002.jpg\n│       └── 100003.jpg\n└── labels             # xx.txt example      \n    ├── train2017       \n    │   ├── 000001.txt\n    │   ├── 000002.txt\n    │   └── 000003.txt\n    └── val2017         \n        ├── 100001.txt\n        ├── 100002.txt\n        └── 100003.txt\n```\n  \n\u003c/details\u003e \n\n\u003cdetails open\u003e\n\u003csummary\u003eAuto LabelImg\u003c/summary\u003e\n\n[**Link** ：https://github.com/ppogg/AutoLabelImg](https://github.com/ppogg/AutoLabelImg)  \n\nYou can use LabelImg based YOLOv5-5.0 and YOLOv5-Lite to AutoAnnotate, biubiubiu 🚀 🚀 🚀 \n\u003cimg src=\"https://user-images.githubusercontent.com/82716366/177030174-dc3a5827-2821-4d8c-8d78-babe83c42fbf.JPG\" width=\"950\"/\u003e\u003cbr/\u003e\n\n  \n\u003c/details\u003e \n\n\u003cdetails open\u003e\n\u003csummary\u003eModel Hub\u003c/summary\u003e\n\nHere, the original components of YOLOv5 and the reproduced components of YOLOv5-Lite are organized and stored in the [model hub](https://github.com/ppogg/YOLOv5-Lite/tree/master/models/hub)：\n\n  ![modelhub](https://user-images.githubusercontent.com/82716366/146787562-e2c1c4c1-726e-4efc-9eae-d92f34333e8d.jpg)\n  \n  \u003cdetails open\u003e\n\u003csummary\u003eHeatmap Analysis\u003c/summary\u003e\n\n  \n   ```bash\n$ python main.py --type all\n```\n  \n![论文插图2](https://user-images.githubusercontent.com/82716366/167449474-3689c2bf-197a-4403-849c-b85db6bcc476.png)\n\n  Updating ...\n\n\u003c/details\u003e\n\n## How to deploy\n\n[**ncnn**](https://github.com/ppogg/YOLOv5-Lite/blob/master/cpp_demo/ncnn/README.md)  for arm-cpu\n\n[**mnn**](https://github.com/ppogg/YOLOv5-Lite/blob/master/cpp_demo/mnn/README.md) for arm-cpu\n\n[**openvino**](https://github.com/ppogg/YOLOv5-Lite/blob/master/python_demo/openvino/README.md) x86-cpu or x86-vpu \n\n[**tensorrt(C++)**](https://github.com/ppogg/YOLOv5-Lite/blob/master/cpp_demo/tensorrt/README.md) for arm-gpu or arm-npu or x86-gpu\n\n[**tensorrt(Python)**](https://github.com/ppogg/YOLOv5-Lite/tree/master/python_demo/tensorrt) for arm-gpu or arm-npu or x86-gpu\n\n[**Android**](https://github.com/ppogg/YOLOv5-Lite/blob/master/android_demo/ncnn-android-v5lite/README.md) for arm-cpu\n\n## Android_demo \n\nThis is a Redmi phone, the processor is Snapdragon 730G, and yolov5-lite is used for detection. The performance is as follows:\n\nlink: https://github.com/ppogg/YOLOv5-Lite/tree/master/android_demo/ncnn-android-v5lite\n\nAndroid_v5Lite-s: https://drive.google.com/file/d/1CtohY68N2B9XYuqFLiTp-Nd2kuFWgAUR/view?usp=sharing\n\nAndroid_v5Lite-g: https://drive.google.com/file/d/1FnvkWxxP_aZwhi000xjIuhJ_OhqOUJcj/view?usp=sharing\n\nnew android app:[link] https://pan.baidu.com/s/1PRhW4fI1jq8VboPyishcIQ [keyword] pogg\n\n\u003cimg src=\"https://user-images.githubusercontent.com/82716366/149959014-5f027b1c-67b6-47e2-976b-59a7c631b0f2.jpg\" width=\"650\"/\u003e\u003cbr/\u003e\n\n## More detailed explanation\n\n#### Detailed model link:\n \n What is YOLOv5-Lite S/E model:\n zhihu link (Chinese): [https://zhuanlan.zhihu.com/p/400545131](https://zhuanlan.zhihu.com/p/400545131)\n \n What is YOLOv5-Lite C model:\n zhihu link (Chinese): [https://zhuanlan.zhihu.com/p/420737659](https://zhuanlan.zhihu.com/p/420737659)\n\n What is YOLOv5-Lite G model:\n zhihu link (Chinese): [https://zhuanlan.zhihu.com/p/410874403](https://zhuanlan.zhihu.com/p/410874403)\n \n How to deploy on ncnn with fp16 or int8:\n csdn link (Chinese): [https://blog.csdn.net/weixin_45829462/article/details/119787840](https://blog.csdn.net/weixin_45829462/article/details/119787840)\n\n How to deploy on mnn with fp16 or int8:\n zhihu link (Chinese): [https://zhuanlan.zhihu.com/p/672633849](https://zhuanlan.zhihu.com/p/672633849)\n \n How to deploy on onnxruntime:\n zhihu link (Chinese): [https://zhuanlan.zhihu.com/p/476533259](https://zhuanlan.zhihu.com/p/476533259)(old version)\n \n How to deploy on tensorrt:\n zhihu link (Chinese): [https://zhuanlan.zhihu.com/p/478630138](https://zhuanlan.zhihu.com/p/478630138)\n \n How to optimize on tensorrt:\n zhihu link (Chinese): [https://zhuanlan.zhihu.com/p/463074494](https://zhuanlan.zhihu.com/p/463074494)\n\n## Reference\n\nhttps://github.com/ultralytics/yolov5\n\nhttps://github.com/megvii-model/ShuffleNet-Series\n\nhttps://github.com/Tencent/ncnn\n\n## Citing YOLOv5-Lite\nIf you use YOLOv5-Lite in your research, please cite our work and give a star ⭐:\n\n```\n @misc{yolov5lite2021,\n  title = {YOLOv5-Lite: Lighter, faster and easier to deploy},\n  author = {Xiangrong Chen and Ziman Gong},\n  doi = {10.5281/zenodo.5241425}\n  year={2021}\n}\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fppogg%2Fyolov5-lite","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fppogg%2Fyolov5-lite","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fppogg%2Fyolov5-lite/lists"}