yolov5-lite

# YOLOv5-Lite：lighter, faster and easier to deploy   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-Lite_e^ours|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-Lite_s^ours| 416×416|1.66G |1.64M|3.4| 42.0|25.2 008| YOLOv5-Lite_c^ours| 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-Lite_g^ours| 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<br />v5Lite-s.param|3.3m|shufflenetv2|v5Lites-head|ncnn|Arm-cpu v5Lite-s-int8.bin<br />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<br />v5Lite-s-fp16.xml|3.4m|shufflenetv2|v5Lites-head|openvivo|x86-cpu v5Lite-s-fp32.bin<br />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<br />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) |<br> >>>> |──────`ncnn-fp16`: | [Baidu Drive](https://pan.baidu.com/s/1kWtwx1C0OTTxbwqJyIyXWg) | [Google Drive](https://drive.google.com/drive/folders/1w4mThJmqjhT1deIXMQAQ5xjWI3JNyzUl?usp=sharing) |<br> >>>> |──────`ncnn-int8`: | [Baidu Drive](https://pan.baidu.com/s/1QX6-oNynrW-f3i0P0Hqe4w) | [Google Drive](https://drive.google.com/drive/folders/1YNtNVWlRqN8Dwc_9AtRkN0LFkDeJ92gN?usp=sharing) |<br> >>>> |──────`mnn-fp16`: | [Baidu Drive](https://pan.baidu.com/s/12lOtPTl4xujWm5BbFJh3zA) | [Google Drive](https://drive.google.com/drive/folders/1PpFoZ4b8mVs1GmMxgf0WUtXUWaGK_JZe?usp=sharing) |<br> >>>> |──────`mnn-int4`: | [Baidu Drive](https://pan.baidu.com/s/11fbjFi18xkq4ltAKUKDOCA) | [Google Drive](https://drive.google.com/drive/folders/1mSU8g94c77KKsHC-07p5V3tJOZYPQ-g6?usp=sharing) |<br> >>>> └──────`tengine-fp32`: | [Baidu Drive](https://pan.baidu.com/s/123r630O8Fco7X59wFU1crA) | [Google Drive](https://drive.google.com/drive/folders/1VWmI2BC9MjH7BsrOz4VlSDVnZMXaxGOE?usp=sharing) |<br> >> - [ ] `YOLOv5—Litec.pt`: [Baidu Drive](https://pan.baidu.com/s/1obs6uRB79m8e3uASVR6P1A) | [Google Drive](https://drive.google.com/file/d/1lHYRQKjqKCRXghUjwWkUB0HQ8ccKH6qa/view?usp=sharing) |<br> >>>> └──────`openvino-fp16`: | [Baidu Drive](https://pan.baidu.com/s/18p8HAyGJdmo2hham250b4A) | [Google Drive](https://drive.google.com/drive/folders/1s4KPSC4B0shG0INmQ6kZuPLnlUKAATyv?usp=sharing) |<br> >> - [ ] `YOLOv5—Liteg.pt`: | [Baidu Drive](https://pan.baidu.com/s/14zdTiTMI_9yTBgKGbv9pQw) | [Google Drive](https://drive.google.com/file/d/1oftzqOREGqDCerf7DtD5BZp9YWELlkMe/view?usp=sharing) |<br> 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) ## <div>How to use</div> <details open> <summary>Install</summary> [**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/): <!-- $ sudo apt update && apt install -y libgl1-mesa-glx libsm6 libxext6 libxrender-dev --> ```bash $ git clone https://github.com/ppogg/YOLOv5-Lite $ cd YOLOv5-Lite $ pip install -r requirements.txt ``` </details> <details> <summary>Inference with detect.py</summary> `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 ``` </details> <details open> <summary>Training</summary> ```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.di