⚡ Yolo-FastestV2 ⚡

• 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