笔者的数字识别模型、树莓派上所需安装的依赖包(包括onnxruntime依赖)以及YOLOv5-lite1.4版本的源码
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资源介绍:
所有在树莓派上需要的依赖都在这里,还有笔者训练好的模型
# 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| YOLOv5-Liteeours|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| YOLOXNano|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-Litesours| 416×416|1.66G |1.64M|3.4| 42.0|25.2
009| YOLOv5-Litecours| 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-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-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|-|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·
#### @v5lite-e:
Model|Size|Backbone|Head|Framework|Design for
:---:|:---:|:---:|:---:|:---:|:---
v5Lite-e.pt|1.7m|shufflenetv2(Megvii)|v5Litee-head|Pytorch|Arm-cpu
v5Lite-e.bin
v5Lite-e.param|1.7m|shufflenetv2|v5Litee-head|ncnn|Arm-cpu
v5Lite-e-int8.bin
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
v5Lite-e-fp32.tnnproto|2.9m|shufflenetv2|v5Litee-head|tnn|arm-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
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
#### @v5lite-c:
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
#### @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|v5Liteg-head|Tensorrt|x86-gpu / arm-gpu / arm-npu
v5lite-g-int8.tmfile|8.7m|Repvgg|v5Liteg-head|Tengine| 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) |
>> |──────`ncnn-fp16`: | [Baidu Drive]() | [Google Drive](https://drive.google.com/drive/folders/1w4mThJmqjhT1deIXMQAQ5xjWI3JNyzUl?usp=sharing) |
>> |──────`ncnn-int8`: | [Baidu Drive]() | [Google Drive](https://drive.google.com/drive/folders/1YNtNVWlRqN8Dwc_9AtRkN0LFkDeJ92gN?usp=sharing) |
>> |──────`mnn-fp32`: | [Baidu Drive]() | [Google Drive](https://drive.google.com/drive/folders/1Kha3vQF-7qc5i-GFryInStgTFisGL5vq?usp=sharing) |
>> └──────tnn-fp32`: | [Baidu Drive]() | [Google Drive](https://drive.google.com/drive/folders/1VWmI2BC9MjH7BsrOz4VlSDVnZMXaxGOE?usp=sharing) |
> - [ ] `v5lite-s.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) |
> - [ ] `v5lite-c.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) |
> - [ ] `v5lite-g.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)
## Thanks for contributors
We welcome your comments! We want to make contributing to YOLOv5-Lite as easy and transparent as possible. Thanks to a
资源文件列表:
YOLO数字识别程序+树莓派依赖包.zip 大约有955个文件