How to Train a YOLOv8 Object Detection Model | BasicCAT — Computer-Aided Translation (CAT) Tools

YOLOv8 is the latest YOLO object detection model. The inference and training in YOLOv8 are very easy to get started.

ImageTrans v2.10.0 added support for YOLOv8 model. It can use Java to call OpenCV’s DNN module for object detection. An object detection annotation data manager is also provided so that we can export an ImageTrans project to a YOLO format training dataset or import the dataset to an ImageTrans project, which makes it easy to train our own model according to the needs.

Here are the detailed steps to do this:

Open an ImageTrans project and use automatic or manual methods to complete the annotations of the objects (mainly text areas) in images.
Open Object Detection Annotation Data Manager through menu->tools.

Export the data to a directory. The data will be stored in YOLO format according to the following structure.

├─images
│  ├─train
│  │      image1.jpg
│  │      image2.jpg
│  │
│  └─val
│         image3.jpg
│         image4.jpg
│
├─labels
│  │
│  ├─train
│  │      image1.txt
│  │      image2.txt
│  │
│  └─val
│         image3.txt
│         image4.txt
│
├─balloon.yaml

Install Python and follow YOLOv8’s documentation to install YOLO.
Create a new train.py file in the directory of the exported data and execute it using Python to start training the model:
```
from ultralytics import YOLO
model = YOLO('yolov8n.pt')  # load a pretrained model (recommended for training)
# Train the model
results = model.train(data='balloon.yaml', epochs=100, imgsz=640)
```
Execute the above code through the command line:
```
python train.py
```
Usually, training 100 epochs with 20 images can achieve a good result. The training can be done with a CPU.
After the training is completed, we can find the trained model file in runs\detect\train\weights. Here, we use the following code to convert best.pt to the onnx format supported by ImageTrans:
```
from ultralytics import YOLO
model = YOLO('best.pt')
success = model.export(format='onnx')
```
Save the above code as convert.py and execute it from the command line:
```
python convert.py
```
Copy the converted best.onnx to the directory of ImageTrans or the image folder of an ImageTrans project, rename it model.onnx, and enable offline balloon detection in ImageTrans’s preferences. Afterwards, the YOLOv8 object detection model can be called in ImageTrans through balloon detection.

You can find sample datasets and trained models here.

Support for Long Images

Sometimes, the images we need to process are very long. We can crop the images into smaller ones for training and detection.

Example:

webtoon

We can crop images by specifying the width, height, and proportions of sub-images that overlap each other.

How to enable:

For the object detection annotation data manager, we can set it up directly in its interface.
For using the trained model for detection, we can create a configuration file named model.json along with the model, to specify the relevant parameters.

For long-strip comics (webtoon), we can use the following config file to crop the image into several sub-images using the width of the image as the width and height for the cropped image with a 20% overlap ratio of the height:
```
{
   "width":640,
   "height":640,
   "model":"model.onnx",
   "ratio":1,
   "width_overlap":"0",
   "height_overlap":"20"
}
```
For large images, we can use the following config file which uses sliding window to crop the image at a fixed width and height:
```
{
   "width":640,
   "height":640,
   "ratio":1,
   "model":"model.onnx",
   "slidingWindow":{
      "width":1600,
      "height":1600
   }
}
```