:zap:Yolo-FastestV2:zap:

* Simple, fast, compact, easy to transplant * Less resource occupation, excellent single-core performance, lower power consumption * Faster and smaller:Trade 1% loss of accuracy for 40% 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 7 minutes

Evaluating indicator/Benchmark

• Test platform Mi 11 Snapdragon 888 CPU，Based on NCNN
• Reasons for the increase in inference speed: optimization of model memory access
• Suitable for hardware with extremely tight computing resources

How to use

Dependent installation

• PIP pip3 install -r requirements.txt

Test

• Picture test python3 test.py --data data/coco.data --weights modelzoo/coco2017-epoch-0.235624ap-model.pth --img img/dog.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-epoch-0.235624ap-model.pth

Deploy

NCNN