⚡Yolo-FastestV2⚡

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	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

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

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

henbucuoshanghai/Yolo-FastestV2