FastestDet: it has higher accuracy and faster speed than Yolo-fastest https://github.com/dog-qiuqiu/FastestDet
:zap:Yolo-FastestV2:zap:
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Simple, fast, compact, easy to transplant
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Less resource occupation, excellent single-core performance, lower power consumption
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Faster and smaller:Trade 0.3% loss of accuracy for 30% increase in inference speed, reducing the amount of parameters by 25%
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Fast training speed, low computing power requirements, training only requires 3GB video memory, gtx1660ti training COCO 1 epoch only takes 4 minutes
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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 24.10 % 352X352 3.29 ms 5.37 ms 0.212 0.25M Yolo-FastestV1.1 24.40 % 320X320 4.23 ms 7.54 ms 0.252 0.35M Yolov4-Tiny 40.2% 416X416 26.00ms 55.44ms 6.9 5.77M -
Test platform Mate 30 Kirin 990 CPU，Based on NCNN
Improvement
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Different loss weights for different scale output layers
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The backbone is replaced with a more lightweight shufflenetV2
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Anchor matching mechanism and loss are replaced by YoloV5, and the classification loss is replaced by softmax cross entropy from sigmoid
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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.txtTest
- PIP
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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.jpgval.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 fileGet 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 # iouBuild the training .data configuration file
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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 fileTrain
- Perform training tasks
python3 train.py --data data/coco.dataEvaluation
- Calculate map evaluation
python3 evaluation.py --data data/coco.data --weights modelzoo/coco2017-0.241078ap-model.pthDeploy
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 ./demoReference
- https://github.com/Tencent/ncnn
- https://github.com/AlexeyAB/darknet
- https://github.com/ultralytics/yolov5
- https://github.com/eriklindernoren/PyTorch-YOLOv3