# YOLOv5-Lite **Repository Path**: seaflyren/YOLOv5-Lite ## Basic Information - **Project Name**: YOLOv5-Lite - **Description**: 轻量级yolo-v5 - **Primary Language**: Python - **License**: GPL-3.0 - **Default Branch**: master - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 0 - **Created**: 2022-01-29 - **Last Updated**: 2022-06-18 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README # YOLOv5-Lite：lighter, faster and easier to deploy ![](https://zenodo.org/badge/DOI/10.5281/zenodo.5241425.svg) ![image](https://user-images.githubusercontent.com/82716366/135564164-3ec169c8-93a7-4ea3-b0dc-40f1059601ef.png) Perform a series of ablation experiments on yolov5 to make it lighter (smaller Flops, lower memory, and fewer parameters) and faster (add shuffle channel, yolov5 head for channel reduce. 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). ## Comparison of ablation experiment results ID|Model | Input_size|Flops| Params | Size（M） |Map@0.5|Map@.5:0.95 :-----:|:-----:|:-----:|:----------:|:----:|:----:|:----:|:----:| 001| yolo-fastest| 320×320|0.25G|0.35M|1.4| 24.4| - 002| NanoDet-m| 320×320| 0.72G|0.95M|1.8|- |20.6 003| yolo-fastest-xl| 320×320|0.72G|0.92M|3.5| 34.3| - 004| YOLOv5-Liteeours|320×320|0.88G|0.90M|2.0| 37.1|21.2| 005| yolov3-tiny| 416×416| 6.96G|6.06M|23.0| 33.1|16.6 006| yolov4-tiny| 416×416| 5.62G|8.86M| 33.7|40.2|21.7 007| YOLOv5-Litesours| 416×416|1.66G |1.64M|3.4| 42.0|25.2 008| YOLOv5-Litecours| 512×512|5.92G |4.57M|9.2| 50.9|32.5| 009| NanoDet-EfficientLite2| 512×512| 7.12G|4.71M|18.3|- |32.6 010| YOLOv5s(6.0)| 640×640| 16.5G|7.23M|14.0| 56.0|37.2 011| YOLOv5-Litegours| 640×640|15.6G |5.39M|10.9| 57.6|39.1| See the wiki: https://github.com/ppogg/YOLOv5-Lite/wiki/Test-the-map-of-models-about-coco ## Comparison on different platforms Equipment|Computing backend|System|Input|Framework|v5Lite-s|v5Lite-c|v5Lite-g|YOLOv5s :---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---: Inter|@i5-10210U|window(x86)|640×640|openvino|-|46ms|-|131ms Nvidia|@RTX 2080Ti|Linux(x86)|640×640|torch|-|-|15ms|14ms Redmi K30|@Snapdragon 730G|Android(arm64)|320×320|ncnn|28ms|-|-|163ms Raspberrypi 4B|@ARM Cortex-A72|Linux(arm64)|320×320|ncnn|84ms|-|-|371ms Raspberrypi 4B|@ARM Cortex-A72|Linux(arm64)|320×320|mnn|76ms|-|-|356ms * The above is a 4-thread test benchmark * Raspberrypi 4B enable bf16s optimization，[Raspberrypi 64 Bit OS](http://downloads.raspberrypi.org/raspios_arm64/images/raspios_arm64-2020-08-24/) ### qq交流群：993965802 ## ·Model Zoo· #### @YOLOv5-Lites: Model|Size|Backbone|Head|Framework|Design for :---:|:---:|:---:|:---:|:---:|:--- v5Lite-s.pt|3.4m|shufflenetv2（Megvii）|v5Lites-head|Pytorch|Arm-cpu v5Lite-s.bin
v5Lite-s.param|3.3m|shufflenetv2|v5Lites-head|ncnn|Arm-cpu v5Lite-s-int8.bin
v5Lite-s-int8.param|1.7m|shufflenetv2|v5Lites-head|ncnn|Arm-cpu v5Lite-s.mnn|3.3m|shufflenetv2|v5Lites-head|mnn|Arm-cpu v5Lite-s-int4.mnn|987k|shufflenetv2|v5Lites-head|mnn|Arm-cpu v5Lite-s-fp16.bin
v5Lite-s-fp16.xml|3.4m|shufflenetv2|v5Lites-head|openvivo|x86-cpu v5Lite-s-fp32.bin
v5Lite-s-fp32.xml|6.8m|shufflenetv2|v5Lites-head|openvivo|x86-cpu v5Lite-s-fp16.tflite|3.3m|shufflenetv2|v5Lites-head|tflite|arm-cpu v5Lite-s-fp32.tflite|6.7m|shufflenetv2|v5Lites-head|tflite|arm-cpu v5Lite-s-int8.tflite|1.8m|shufflenetv2|v5Lites-head|tflite|arm-cpu #### @YOLOv5-Litec: Model|Size|Backbone|Head|Framework|Design for :---:|:---:|:---:|:---:|:---:|:---: v5Lite-c.pt|9m|PPLcnet（Baidu）|v5Litec-head|Pytorch|x86-cpu / x86-vpu v5Lite-c.bin
v5Lite-c.xml|8.7m|PPLcnet|v5Litec-head|openvivo|x86-cpu / x86-vpu #### @YOLOv5-Liteg: Model|Size|Backbone|Head|Framework|Design for :---:|:---:|:---:|:---:|:---:|:---: v5Lite-g.pt|10.9m|Repvgg（Tsinghua）|v5Liteg-head|Pytorch|x86-gpu / arm-gpu / arm-npu v5Lite-g-int8.engine|8.5m|Repvgg|v5Liteg-head|Tensorrt|x86-gpu / arm-gpu / arm-npu v5lite-g-int8.tmfile|8.7m|Repvgg|v5Liteg-head|Tengine| arm-npu > #### Download Link： >> - [ ] `YOLOv5—Lites.pt`: | [Baidu Drive](https://pan.baidu.com/s/1j0n0K1kqfv1Ouwa2QSnzCQ) | [Google Drive](https://drive.google.com/file/d/1ccLTmGB5AkKPjDOyxF3tW7JxGWemph9f/view?usp=sharing) |
>>>> |──────`ncnn-fp16`: | [Baidu Drive](https://pan.baidu.com/s/1kWtwx1C0OTTxbwqJyIyXWg) | [Google Drive](https://drive.google.com/drive/folders/1w4mThJmqjhT1deIXMQAQ5xjWI3JNyzUl?usp=sharing) |
>>>> |──────`ncnn-int8`: | [Baidu Drive](https://pan.baidu.com/s/1QX6-oNynrW-f3i0P0Hqe4w) | [Google Drive](https://drive.google.com/drive/folders/1YNtNVWlRqN8Dwc_9AtRkN0LFkDeJ92gN?usp=sharing) |
>>>> |──────`mnn-fp16`: | [Baidu Drive](https://pan.baidu.com/s/12lOtPTl4xujWm5BbFJh3zA) | [Google Drive](https://drive.google.com/drive/folders/1PpFoZ4b8mVs1GmMxgf0WUtXUWaGK_JZe?usp=sharing) |
>>>> |──────`mnn-int4`: | [Baidu Drive](https://pan.baidu.com/s/11fbjFi18xkq4ltAKUKDOCA) | [Google Drive](https://drive.google.com/drive/folders/1mSU8g94c77KKsHC-07p5V3tJOZYPQ-g6?usp=sharing) |
>>>> └──────`tengine-fp32`: | [Baidu Drive](https://pan.baidu.com/s/123r630O8Fco7X59wFU1crA) | [Google Drive](https://drive.google.com/drive/folders/1VWmI2BC9MjH7BsrOz4VlSDVnZMXaxGOE?usp=sharing) |
>> - [ ] `YOLOv5—Litec.pt`: [Baidu Drive](https://pan.baidu.com/s/1obs6uRB79m8e3uASVR6P1A) | [Google Drive](https://drive.google.com/file/d/1lHYRQKjqKCRXghUjwWkUB0HQ8ccKH6qa/view?usp=sharing) |
>>>> └──────`openvino-fp16`: | [Baidu Drive](https://pan.baidu.com/s/18p8HAyGJdmo2hham250b4A) | [Google Drive](https://drive.google.com/drive/folders/1s4KPSC4B0shG0INmQ6kZuPLnlUKAATyv?usp=sharing) |
>> - [ ] `YOLOv5—Liteg.pt`: | [Baidu Drive](https://pan.baidu.com/s/14zdTiTMI_9yTBgKGbv9pQw) | [Google Drive](https://drive.google.com/file/d/1oftzqOREGqDCerf7DtD5BZp9YWELlkMe/view?usp=sharing) |
Baidu Drive Password: `pogg` #### v5lite-s model: TFLite Float32, Float16, INT8, Dynamic range quantization, ONNX, TFJS, TensorRT, OpenVINO IR FP32/FP16, Myriad Inference Engin Blob, CoreML [https://github.com/PINTO0309/PINTO_model_zoo/tree/main/180_YOLOv5-Lite](https://github.com/PINTO0309/PINTO_model_zoo/tree/main/180_YOLOv5-Lite) #### 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) ##
How to use
Install [**Python>=3.6.0**](https://www.python.org/) is required with all [requirements.txt](https://github.com/ppogg/YOLOv5-Lite/blob/master/requirements.txt) installed including [**PyTorch>=1.7**](https://pytorch.org/get-started/locally/): ```bash $ git clone https://github.com/ppogg/YOLOv5-Lite $ cd YOLOv5-Lite $ pip install -r requirements.txt ```
Inference with detect.py `detect.py` runs inference on a variety of sources, downloading models automatically from the [latest YOLOv5-Lite release](https://github.com/ppogg/YOLOv5-Lite/releases) and saving results to `runs/detect`. ```bash $ 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 ```
Training ```bash $ python train.py --data coco.yaml --cfg v5lite-e.yaml --weights v5lite-e.pt --batch-size 128 v5lite-s.yaml --weights v5lite-s.pt --batch-size 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: ```bash $ python -m torch.distributed.launch --nproc_per_node 2 train.py ```
DataSet Training set and test set distribution （the path with xx.jpg） ```bash train: ../coco/images/train2017/ val: ../coco/images/val2017/ ``` ```bash ├── 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 ```
model hub Here, 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)： ![modelhub](https://user-images.githubusercontent.com/82716366/146787562-e2c1c4c1-726e-4efc-9eae-d92f34333e8d.jpg) Updating ...
## How to deploy [**ncnn**](https://github.com/ppogg/YOLOv5-Lite/blob/master/ncnn/README.md) for arm-cpu [**mnn**](https://github.com/ppogg/YOLOv5-Lite/blob/master/mnn/README.md) for arm-cpu [**openvino**](https://github.com/ppogg/YOLOv5-Lite/blob/master/openvino/README.md) x86-cpu or x86-vpu [**tensorrt**](https://github.com/ppogg/YOLOv5-Lite/tree/master/tensorrt) for arm-gpu or arm-npu or x86-gpu [**Android**](https://github.com/ppogg/YOLOv5-Lite/blob/master/Android/ncnn-android-yolov5/README.md) 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/ncnn_Android 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: [1] https://zhuanlan.zhihu.com/p/400545131 [2] https://zhuanlan.zhihu.com/p/410874403 [3] https://blog.csdn.net/weixin_45829462/article/details/119787840 [4] https://zhuanlan.zhihu.com/p/420737659 ## Reference https://github.com/ultralytics/yolov5 https://github.com/megvii-model/ShuffleNet-Series https://github.com/Tencent/ncnn