https://github.com/ppogg/YOLOv5-Lite

🍅🍅🍅YOLOv5-Lite: Evolved from yolov5 and the size of model is only 900+kb (int8) and 1.7M (fp16). Reach 15 FPS on the Raspberry Pi 4B~
https://github.com/ppogg/YOLOv5-Lite

android-app mnn mobilenet ncnn onnxruntime openvivo picodet pplcnet pytorch repvgg shufflenetv2 tensorrt tflite transformer yolov5

Last synced: 3 months ago
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🍅🍅🍅YOLOv5-Lite: Evolved from yolov5 and the size of model is only 900+kb (int8) and 1.7M (fp16). Reach 15 FPS on the Raspberry Pi 4B~

Host: GitHub
URL: https://github.com/ppogg/YOLOv5-Lite
Owner: ppogg
License: gpl-3.0
Created: 2021-08-16T14:24:00.000Z (almost 4 years ago)
Default Branch: master
Last Pushed: 2024-06-22T02:13:22.000Z (12 months ago)
Last Synced: 2024-10-29T17:58:01.546Z (8 months ago)
Topics: android-app, mnn, mobilenet, ncnn, onnxruntime, openvivo, picodet, pplcnet, pytorch, repvgg, shufflenetv2, tensorrt, tflite, transformer, yolov5
Language: C++
Homepage:
Size: 67.8 MB
Stars: 2,258
Watchers: 29
Forks: 406
Open Issues: 63
Metadata Files:
- Readme: README.md
- License: LICENSE

Awesome Lists containing this project

awesome-yolo-object-detection - ppogg/YOLOv5-Lite - Lite?style=social"/> : 🍅🍅🍅YOLOv5-Lite: lighter, faster and easier to deploy. Evolved from yolov5 and the size of model is only 930+kb (int8) and 1.7M (fp16). It can reach 10+ FPS on the Raspberry Pi 4B when the input size is 320×320~ (Lighter and Deployment Frameworks)
awesome-yolo-object-detection - ppogg/YOLOv5-Lite - Lite?style=social"/> : 🍅🍅🍅YOLOv5-Lite: lighter, faster and easier to deploy. Evolved from yolov5 and the size of model is only 930+kb (int8) and 1.7M (fp16). It can reach 10+ FPS on the Raspberry Pi 4B when the input size is 320×320~ (Lighter and Deployment Frameworks)
StarryDivineSky - ppogg/YOLOv5-Lite - Lite项目是从YOLOv5演变而来，专注于模型轻量化。其模型大小仅为900+KB (int8) 和 1.7M (fp16)，非常适合资源受限的设备。该项目特色在于体积小巧的同时，在树莓派4B上能达到15 FPS的运行速度。这使其成为嵌入式设备和移动端部署的理想选择。YOLOv5-Lite保留了YOLOv5的核心检测能力，并通过优化网络结构和量化技术来实现轻量化。该项目旨在提供一个高性能、低功耗的目标检测解决方案。它特别适合对模型大小和推理速度有严格要求的应用场景。 (对象检测_分割 / 资源传输下载)

README

        # YOLOv5-Lite：Lighter, faster and easier to deploy   ![](https://zenodo.org/badge/DOI/10.5281/zenodo.5241425.svg)

![论文插图](https://user-images.githubusercontent.com/82716366/167448925-a431d3a4-ad5d-491d-be95-c90701122a54.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).

![image](https://user-images.githubusercontent.com/82716366/135564164-3ec169c8-93a7-4ea3-b0dc-40f1059601ef.png)

## Comparison of ablation experiment results

  ID|Model | Input_size|Flops| Params | Size（M） |[email protected]|[email protected]: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
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
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-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
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-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
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) |
 

>> |──────`ncnn-fp16`:   | [Baidu Drive](https://pan.baidu.com/s/1_QvWvkhHB7kdcRZ6k4at1g)  | [Google Drive](https://drive.google.com/drive/folders/1w4mThJmqjhT1deIXMQAQ5xjWI3JNyzUl?usp=sharing) |
 

>> |──────`ncnn-int8`: | [Baidu Drive](https://pan.baidu.com/s/1JO8qbbVx6zJ-6aq5EgM6PA) | [Google Drive](https://drive.google.com/drive/folders/1YNtNVWlRqN8Dwc_9AtRkN0LFkDeJ92gN?usp=sharing) |


>> |──────`mnn-e_bf16`: | [Google Drive](https://drive.google.com/file/d/1_yGGCJFat2bjaKEwxJjrFcU2mGEhH8I3/view?usp=sharing) |
 

>> |──────`mnn-d_bf16`: | [Google Drive](https://drive.google.com/file/d/1TqijmdQtWd6ZUr2CiNLuHCEQnPUw1oVu/view?usp=sharing)|
 

>> └──────`onnx-fp32`: | [Baidu Drive](https://pan.baidu.com/s/1zIqKmOavRIrV8UJxbQWvhA) | [Google Drive](https://drive.google.com/file/d/1ot9eNlFMqMEzt_FHf0SkDHj1fOprOnTU/view?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) |
 

>> └──────`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) |
 

>> └──────`axpi-int8`: [Google Drive](https://github.com/AXERA-TECH/ax-models/blob/main/ax620/v5Lite-g-sim-640.joint) |
 

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           v5lite-s.pt              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

```

  

 

Auto LabelImg

[**Link** ：https://github.com/ppogg/AutoLabelImg](https://github.com/ppogg/AutoLabelImg)  

You can use LabelImg based YOLOv5-5.0 and YOLOv5-Lite to AutoAnnotate, biubiubiu 🚀 🚀 🚀 




  

 

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)

  

  

Heatmap Analysis

  

   ```bash

$ python main.py --type all

```

  

![论文插图2](https://user-images.githubusercontent.com/82716366/167449474-3689c2bf-197a-4403-849c-b85db6bcc476.png)

  Updating ...

## How to deploy

[**ncnn**](https://github.com/ppogg/YOLOv5-Lite/blob/master/cpp_demo/ncnn/README.md)  for arm-cpu

[**mnn**](https://github.com/ppogg/YOLOv5-Lite/blob/master/cpp_demo/mnn/README.md) for arm-cpu

[**openvino**](https://github.com/ppogg/YOLOv5-Lite/blob/master/python_demo/openvino/README.md) x86-cpu or x86-vpu 

[**tensorrt(C++)**](https://github.com/ppogg/YOLOv5-Lite/blob/master/cpp_demo/tensorrt/README.md) for arm-gpu or arm-npu or x86-gpu

[**tensorrt(Python)**](https://github.com/ppogg/YOLOv5-Lite/tree/master/python_demo/tensorrt) for arm-gpu or arm-npu or x86-gpu

[**Android**](https://github.com/ppogg/YOLOv5-Lite/blob/master/android_demo/ncnn-android-v5lite/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/android_demo/ncnn-android-v5lite

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:

 

 What is YOLOv5-Lite S/E model:

 zhihu link (Chinese): [https://zhuanlan.zhihu.com/p/400545131](https://zhuanlan.zhihu.com/p/400545131)

 

 What is YOLOv5-Lite C model:

 zhihu link (Chinese): [https://zhuanlan.zhihu.com/p/420737659](https://zhuanlan.zhihu.com/p/420737659)

 What is YOLOv5-Lite G model:

 zhihu link (Chinese): [https://zhuanlan.zhihu.com/p/410874403](https://zhuanlan.zhihu.com/p/410874403)

 

 How to deploy on ncnn with fp16 or int8:

 csdn link (Chinese): [https://blog.csdn.net/weixin_45829462/article/details/119787840](https://blog.csdn.net/weixin_45829462/article/details/119787840)

 How to deploy on mnn with fp16 or int8:

 zhihu link (Chinese): [https://zhuanlan.zhihu.com/p/672633849](https://zhuanlan.zhihu.com/p/672633849)

 

 How to deploy on onnxruntime:

 zhihu link (Chinese): [https://zhuanlan.zhihu.com/p/476533259](https://zhuanlan.zhihu.com/p/476533259)(old version)

 

 How to deploy on tensorrt:

 zhihu link (Chinese): [https://zhuanlan.zhihu.com/p/478630138](https://zhuanlan.zhihu.com/p/478630138)

 

 How to optimize on tensorrt:

 zhihu link (Chinese): [https://zhuanlan.zhihu.com/p/463074494](https://zhuanlan.zhihu.com/p/463074494)

## Reference

https://github.com/ultralytics/yolov5

https://github.com/megvii-model/ShuffleNet-Series

https://github.com/Tencent/ncnn

## Citing YOLOv5-Lite

If you use YOLOv5-Lite in your research, please cite our work and give a star ⭐:

```

 @misc{yolov5lite2021,

  title = {YOLOv5-Lite: Lighter, faster and easier to deploy},

  author = {Xiangrong Chen and Ziman Gong},

  doi = {10.5281/zenodo.5241425}

  year={2021}

}

```

ecosyste.ms

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