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https://github.com/dog-qiuqiu/Yolo-FastestV2
:zap: Based on Yolo's low-power, ultra-lightweight universal target detection algorithm, the parameter is only 250k, and the speed of the smart phone mobile terminal can reach ~300fps+
https://github.com/dog-qiuqiu/Yolo-FastestV2
Last synced: 7 days ago
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:zap: Based on Yolo's low-power, ultra-lightweight universal target detection algorithm, the parameter is only 250k, and the speed of the smart phone mobile terminal can reach ~300fps+
- Host: GitHub
- URL: https://github.com/dog-qiuqiu/Yolo-FastestV2
- Owner: dog-qiuqiu
- Created: 2021-07-16T09:40:54.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2024-01-17T12:55:13.000Z (10 months ago)
- Last Synced: 2024-08-01T03:42:59.968Z (3 months ago)
- Language: Python
- Homepage:
- Size: 11.7 MB
- Stars: 793
- Watchers: 16
- Forks: 192
- Open Issues: 69
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
- awesome-yolo-object-detection - dog-qiuqiu/Yolo-FastestV2 - qiuqiu/Yolo-FastestV2?style=social"/> : Yolo-FastestV2:更快，更轻，移动端可达300FPS，参数量仅250k。 "知乎「马雪浩」《[Yolo-FastestV2:更快，更轻，移动端可达300FPS，参数量仅250k](https://zhuanlan.zhihu.com/p/400474142)》"。 (Lighter and Deployment Frameworks)
- awesome-yolo-object-detection - dog-qiuqiu/Yolo-FastestV2 - qiuqiu/Yolo-FastestV2?style=social"/> : Yolo-FastestV2:更快，更轻，移动端可达300FPS，参数量仅250k。 "知乎「马雪浩」《[Yolo-FastestV2:更快，更轻，移动端可达300FPS，参数量仅250k](https://zhuanlan.zhihu.com/p/400474142)》"。 (Lighter and Deployment Frameworks)
README
# ***FastestDet: it has higher accuracy and faster speed than Yolo-fastest https://github.com/dog-qiuqiu/FastestDet***
# :zap:Yolo-FastestV2:zap:[](https://zenodo.org/badge/latestdoi/386585431)
* ***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
```
/>
## 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