【Yolov5】Deep model training-CPU version

yolo5 model training

1.yolov5’s own model training
2. Train your own model

1.yolov5’s own model training

1.1 git downloads the corresponding source code to the server

Clone the corresponding code and install requirements.txt in the Python>=3.7.0 environment, including PyTorch>=1.7

git clone https://github.com/ultralytics/yolov5  # clone
cd yolov5
pip install -r requirements.txt  # install

1.2 Automatically download models from the latest YOLOv5 version.

Create python files yourself

import torch

# Model
model = torch.hub.load("ultralytics/yolov5", "yolov5s")  # or yolov5n - yolov5x6, custom
# Images
img = "https://ultralytics.com/images/zidane.jpg"  # or file, Path, PIL, OpenCV, numpy, list
# Inference
results = model(img)
# Results
results.print()  # or .show(), .save(), .crop(), .pandas(), etc.

1.3 detect.py inference

Various parameters can be passed here

python detect.py --weights yolov5s.pt --source 0                               # webcam
                                               img.jpg                         # image
                                               vid.mp4                         # video
                                               screen                          # screenshot
                                               path/                           # directory
                                               list.txt                        # list of images
                                               list.streams                    # list of streams
                                               'path/*.jpg'                    # glob
                                               'https://youtu.be/Zgi9g1ksQHc'  # YouTube
                                               'rtsp://example.com/media.mp4'  # RTSP, RTMP, HTTP stream

1.4 train.py for training

python train.py --data coco.yaml --epochs 300 --weights '' --cfg yolov5n.yaml  --batch-size 128
                                                                 yolov5s                    64
                                                                 yolov5m                    40
                                                                 yolov5l                    24
                                                                 yolov5x                    16

1.5 Principle of yolo

By inputting a picture - performing convolution - getting the output result
The Yolo algorithm uses a separate CNN model to achieve end-to-end target detection. The entire system is shown as follows: First Resize the input image to 448x448, then send it to the CNN network, and finally process the network prediction results to obtain the detected target. Compared with the R-CNN algorithm, it is a unified framework, it is faster, and the training process of Yolo is also end-to-end.

Before training, pre-training was performed on ImageNet. The pre-trained classification model uses the first 20 convolutional layers in Figure 8, and then adds an average-pool layer and a fully connected layer. . After pre-training, 4 randomly initialized convolution layers and 2 fully connected layers are added to the 20 convolution layers obtained from pre-training. Since detection tasks generally require higher-definition images, the input of the network is increased from 224x224 to 448x448. The flow of the entire network is shown in the figure below:

2. Train your own model

Directory Structure

2.1 Create a new directory Mydata as shown below under Data

mydata
- images
  - test
  - train (storage original image)
- labels
  - test
  - train (storage labeled images)
After saving the picture

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As shown in the figure, it is a building block diagram

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2.1 Labelimg graphics for data annotation

If lableimg is not installed, you can install it.

2.2 Dataset

# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
path: /home/hfg/Soft/Idea_Project/deep/yolov5/data/mydata/images/train  # dataset root dir
train: /home/hfg/Soft/Idea_Project/deep/yolov5/data/mydata/images/train  # train images (relative to 'path') 128 images
val: /home/hfg/Soft/Idea_Project/deep/yolov5/data/mydata/images/train  # val images (relative to 'path') 128 images
test:  # test images (optional)

# Classes
names:
  0: 积木

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