# Yolo-FastestV2 **Repository Path**: leizhu_wust/Yolo-FastestV2 ## Basic Information - **Project Name**: Yolo-FastestV2 - **Description**: No description available - **Primary Language**: Unknown - **License**: Not specified - **Default Branch**: main - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 1 - **Created**: 2021-09-15 - **Last Updated**: 2021-11-24 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README # :zap:Yolo-FastestV2:zap:[![DOI](https://zenodo.org/badge/386585431.svg)](https://zenodo.org/badge/latestdoi/386585431) ![image](https://github.com/dog-qiuqiu/Yolo-FastestV2/blob/main/img/demo.png) * ***Simple, fast, compact, easy to transplant*** * ***Less resource occupation, excellent single-core performance, lower power consumption*** * ***Faster and smaller:Trade 0.3% loss of accuracy for 30% increase in inference speed, reducing the amount of parameters by 25%*** * ***Fast training speed, low computing power requirements, training only requires 3GB video memory, gtx1660ti training COCO 1 epoch only takes 4 minutes*** * ***算法介绍：https://zhuanlan.zhihu.com/p/400474142 交流qq群:1062122604*** # Evaluating indicator/Benchmark Network|COCO mAP(0.5)|Resolution|Run Time(4xCore)|Run Time(1xCore)|FLOPs(G)|Params(M) :---:|:---:|:---:|:---:|:---:|:---:|:---: [Yolo-FastestV2](https://github.com/dog-qiuqiu/Yolo-FastestV2/tree/main/modelzoo)|24.10 %|352X352|3.29 ms|5.37 ms|0.212|0.25M [Yolo-FastestV1.1](https://github.com/dog-qiuqiu/Yolo-Fastest/tree/master/ModelZoo/yolo-fastest-1.1_coco)|24.40 %|320X320|4.23 ms|7.54 ms|0.252|0.35M [Yolov4-Tiny](https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov4-tiny.cfg)|40.2%|416X416|26.00ms|55.44ms|6.9|5.77M * ***Test platform Mate 30 Kirin 990 CPU，Based on [NCNN](https://github.com/Tencent/ncnn)*** # Improvement * Different loss weights for different scale output layers * The backbone is replaced with a more lightweight shufflenetV2 * Anchor matching mechanism and loss are replaced by YoloV5, and the classification loss is replaced by softmax cross entropy from sigmoid * Decouple the detection head, distinguish obj (foreground background classification), cls (category classification), reg (detection frame regression) 3 branches, # How to use ## Dependent installation * PIP ``` pip3 install -r requirements.txt ``` ## Test * Picture test ``` python3 test.py --data data/coco.data --weights modelzoo/coco2017-0.241078ap-model.pth --img img/000139.jpg ```
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## How to train ### Building data sets(The dataset is constructed in the same way as darknet yolo) * The format of the data set is the same as that of Darknet Yolo, Each image corresponds to a .txt label file. The label format is also based on Darknet Yolo's data set label format: "category cx cy wh", where category is the category subscript, cx, cy are the coordinates of the center point of the normalized label box, and w, h are the normalized label box The width and height, .txt label file content example as follows: ``` 11 0.344192634561 0.611 0.416430594901 0.262 14 0.509915014164 0.51 0.974504249292 0.972 ``` * The image and its corresponding label file have the same name and are stored in the same directory. The data file structure is as follows: ``` . ├── train │   ├── 000001.jpg │   ├── 000001.txt │   ├── 000002.jpg │   ├── 000002.txt │   ├── 000003.jpg │   └── 000003.txt └── val ├── 000043.jpg ├── 000043.txt ├── 000057.jpg ├── 000057.txt ├── 000070.jpg └── 000070.txt ``` * Generate a dataset path .txt file, the example content is as follows： train.txt ``` /home/qiuqiu/Desktop/dataset/train/000001.jpg /home/qiuqiu/Desktop/dataset/train/000002.jpg /home/qiuqiu/Desktop/dataset/train/000003.jpg ``` val.txt ``` /home/qiuqiu/Desktop/dataset/val/000070.jpg /home/qiuqiu/Desktop/dataset/val/000043.jpg /home/qiuqiu/Desktop/dataset/val/000057.jpg ``` * Generate the .names category label file, the sample content is as follows: category.names ``` person bicycle car motorbike ... ``` * The directory structure of the finally constructed training data set is as follows: ``` . ├── category.names # .names category label file ├── train # train dataset │ ├── 000001.jpg │   ├── 000001.txt │   ├── 000002.jpg │   ├── 000002.txt │   ├── 000003.jpg │   └── 000003.txt ├── train.txt # train dataset path .txt file ├── val # val dataset │   ├── 000043.jpg │   ├── 000043.txt │   ├── 000057.jpg │   ├── 000057.txt │   ├── 000070.jpg │   └── 000070.txt └── val.txt # val dataset path .txt file ``` ### Get anchor bias * Generate anchor based on current dataset ``` python3 genanchors.py --traintxt ./train.txt ``` * The anchors6.txt file will be generated in the current directory,the sample content of the anchors6.txt is as follows: ``` 12.64,19.39, 37.88,51.48, 55.71,138.31, 126.91,78.23, 131.57,214.55, 279.92,258.87 # anchor bias 0.636158 # iou ``` ### Build the training .data configuration file * Reference./data/coco.data ``` [name] model_name=coco # model name [train-configure] epochs=300 # train epichs steps=150,250 # Declining learning rate steps batch_size=64 # batch size subdivisions=1 # Same as the subdivisions of the darknet cfg file learning_rate=0.001 # learning rate [model-configure] pre_weights=None # The path to load the model, if it is none, then restart the training classes=80 # Number of detection categories width=352 # The width of the model input image height=352 # The height of the model input image anchor_num=3 # anchor num anchors=12.64,19.39, 37.88,51.48, 55.71,138.31, 126.91,78.23, 131.57,214.55, 279.92,258.87 #anchor bias [data-configure] train=/media/qiuqiu/D/coco/train2017.txt # train dataset path .txt file val=/media/qiuqiu/D/coco/val2017.txt # val dataset path .txt file names=./data/coco.names # .names category label file ``` ### Train * Perform training tasks ``` python3 train.py --data data/coco.data ``` ### Evaluation * Calculate map evaluation ``` python3 evaluation.py --data data/coco.data --weights modelzoo/coco2017-0.241078ap-model.pth ``` # Deploy ## NCNN * Convert onnx ``` python3 pytorch2onnx.py --data data/coco.data --weights modelzoo/coco2017-0.241078ap-model.pth --output yolo-fastestv2.onnx ``` * onnx-sim ``` python3 -m onnxsim yolo-fastestv2.onnx yolo-fastestv2-opt.onnx ``` * Build NCNN ``` git clone https://github.com/Tencent/ncnn.git cd ncnn mkdir build cd build cmake .. make make install cp -rf ./ncnn/build/install/* ~/Yolo-FastestV2/sample/ncnn ``` * Covert ncnn param and bin ``` cd ncnn/build/tools/onnx ./onnx2ncnn yolo-fastestv2-opt.onnx yolo-fastestv2.param yolo-fastestv2.bin cp yolo-fastestv2* ../ cd ../ ./ncnnoptimize yolo-fastestv2.param yolo-fastestv2.bin yolo-fastestv2-opt.param yolo-fastestv2-opt.bin 1 cp yolo-fastestv2-opt* ~/Yolo-FastestV2/sample/ncnn/model ``` * run sample ``` cd ~/Yolo-FastestV2/sample/ncnn sh build.sh ./demo ``` # Reference * https://github.com/Tencent/ncnn * https://github.com/AlexeyAB/darknet * https://github.com/ultralytics/yolov5 * https://github.com/eriklindernoren/PyTorch-YOLOv3