YOLOv5-Lite由 yolov5 进化而来，模型大小仅有 900+kb（int8）和 1.7M（fp16） 在 Raspb...

# 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| YOLOv5-Lite_e^ours|320×320|0.73G|0.78M|1.7| 35.1|-| 003| NanoDet-m| 320×320| 0.72G|0.95M|1.8|- |20.6 004| yolo-fastest-xl| 320×320|0.72G|0.92M|3.5| 34.3| - 005| YOLOX_Nano|416×416|1.08G|0.91M|7.3(fp32)| -|25.8| 006| yolov3-tiny| 416×416| 6.96G|6.06M|23.0| 33.1|16.6 007| yolov4-tiny| 416×416| 5.62G|8.86M| 33.7|40.2|21.7 008| YOLOv5-Lite_s^ours| 416×416|1.66G |1.64M|3.4| 42.0|25.2 009| YOLOv5-Lite_c^ours| 512×512|5.92G |4.57M|9.2| 50.9|32.5| 010| NanoDet-EfficientLite2| 512×512| 7.12G|4.71M|18.3|- |32.6 011| YOLOv5s(6.0)| 640×640| 16.5G|7.23M|14.0| 56.0|37.2 012| 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-e|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(armv8)|320×320|ncnn|27ms|38ms|-|-|163ms Xiaomi 10|@Snapdragon 865|Android(armv8)|320×320|ncnn|10ms|14ms|-|-|163ms Raspberrypi 4B|@ARM Cortex-A72|Linux(arm64)|320×320|ncnn|-|84ms|-|-|371ms Raspberrypi 4B|@ARM Cortex-A72|Linux(arm64)|320×320|mnn|-|71ms|-|-|356ms AXera-Pi|Cortex A7@CPU<br />3.6TOPs @NPU|Linux(arm64)|640×640|axpi|-|-|-|22ms|22ms ### The tutorial of 15FPS on Raspberry Pi 4B: [https://zhuanlan.zhihu.com/p/672633849](https://zhuanlan.zhihu.com/p/672633849) * 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 入群答案:剪枝 or 蒸馏 or 量化 or 低秩分解（任意其一均可） ## ·Model Zoo· #### @v5lite-e: Model|Size|Backbone|Head|Framework|Design for :---:|:---:|:---:|:---:|:---:|:--- v5Lite-e.pt|1.7m|shufflenetv2（Megvii）|v5Litee-head|Pytorch|Arm-cpu v5Lite-e.bin<br />v5Lite-e.param|1.7m|shufflenetv2|v5Litee-head|ncnn|Arm-cpu v5Lite-e-int8.bin<br />v5Lite-e-int8.param|0.9m|shufflenetv2|v5Litee-head|ncnn|Arm-cpu v5Lite-e-fp32.mnn|3.0m|shufflenetv2|v5Litee-head|mnn|Arm-cpu v5Lite-e-fp32.tnnmodel<br />v5Lite-e-fp32.tnnproto|2.9m|shufflenetv2|v5Litee-head|tnn|arm-cpu v5Lite-e-320.onnx|3.1m|shufflenetv2|v5Litee-head|onnxruntime|x86-cpu #### @v5lite-s: 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 v5Lite-s-416.onnx|6.4m|shufflenetv2|v5Lites-head|onnxruntime|x86-cpu #### @v5lite-c: Model|Size|Backbone|Head|Framework|Design for :---:|:---:|:---:|:---:|:---:|:---: v5Lite-c.pt|9m|PPLcnet（Baidu）|v5s-head|Pytorch|x86-cpu / x86-vpu v5Lite-c.bin<br />v5Lite-c.xml|8.7m|PPLcnet|v5s-head|openvivo|x86-cpu / x86-vpu v5Lite-c-512.onnx|18m|PPLcnet|v5s-head|onnxruntime|x86-cpu #### @v5lite-g: 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-yolov5|v5Liteg-head|Tensorrt|x86-gpu / arm-gpu / arm-npu v5lite-g-int8.tmfile|8.7m|Repvgg-yolov5|v5Liteg-head|Tengine| arm-npu v5Lite-g-640.onnx|21m|Repvgg-yolov5|yolov5-head|onnxruntime|x86-cpu v5Lite-g-640.joint|7.1m|Repvgg-yolov5|yolov5-head|axpi|arm-npu #### Download Link： > - [ ] `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) |<br> >> |──────`ncnn-fp16`: | [Baidu Drive](https://pan.baidu.com/s/1_QvWvkhHB7kdcRZ6k4at1g) | [Google Drive](https://drive.google.com/drive/folders/1w4mThJmqjhT1deIXMQAQ5xjWI3JNyzUl?usp=sharing) |<br> >> |──────`ncnn-int8`: | [Baidu Drive](https://pan.baidu.com/s/1JO8qbbVx6zJ-6aq5EgM6PA) | [Google Drive](https://drive.google.com/drive/folders/1YNtNVWlRqN8Dwc_9AtRkN0LFkDeJ92gN?usp=sharing) |<br> >> |──────`mnn-e_bf16`: | [Google Drive](https://drive.google.com/file/d/1_yGGCJFat2bjaKEwxJjrFcU2mGEhH8I3/view?usp=sharing) |<br> >> |──────`mnn-d_bf16`: | [Google Drive](https://drive.google.com/file/d/1TqijmdQtWd6ZUr2CiNLuHCEQnPUw1oVu/view?usp=sharing)|<br> >> └──────`onnx-fp32`: | [Baidu Drive](https://pan.baidu.com/s/1zIqKmOavRIrV8UJxbQWvhA) | [Google Drive](https://drive.google.com/file/d/1ot9eNlFMqMEzt_FHf0SkDHj1fOprOnTU/view?usp=sharing) |<br> > - [ ] `v5lite-s.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> >> └──────`tengine-fp32`: | [Baidu Drive](https://pan.baidu.com/s/123r630O8Fco7X59wFU1crA) | [Google Drive](https://drive.google.com/drive/folders/1VWmI2BC9MjH7BsrOz4VlSDVnZMXaxGOE?usp=sharing) |<br> > - [ ] `v5lite-c.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> > - [ ] `v5lite-g.pt`: | [Baidu Drive](https://pan.baidu.com/s/14zdTiTMI_9yTBgKGbv9pQw) | [Google Drive](https://drive.google.com/file/d/1oftzqOREGqDCerf7DtD5BZp9YWELlkMe/view?usp=sharing) |<br> >> └──────`axpi-int8`: [Google Drive](https://github.com/AXERA-TECH/ax-models/blob/main/ax620/v5Lite-g-sim-640.joint) |<br> Baidu Drive Password: `pogg