Class imabalance dataloader about ultralytics

[glenn-jocher]

@deepukr007 hi there,

Thank you for your kind words and for using YOLOv8! It's great to hear that you've managed to create a custom sampler and dataloader to address the class imbalance in your dataset. To train the YOLO model using your custom dataloader, you can follow these steps:

1. Custom Training Loop: You will need to create a custom training loop that utilizes your custom dataloader. Here's a basic example to get you started:

   from ultralytics import YOLO
   import torch
   
   # Load your model
   model = YOLO('yolov8n.yaml')  # or 'yolov8n.pt' for a pretrained model
   
   # Your custom dataloader
   dataloader = build_yolo_dataloader(...)
   
   # Define optimizer and loss function
   optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
   criterion = torch.nn.CrossEntropyLoss()
   
   # Training loop
   for epoch in range(num_epochs):
       model.train()
       for images, targets in dataloader:
           optimizer.zero_grad()
           outputs = model(images)
           loss = criterion(outputs, targets)
           loss.backward()
           optimizer.step()
       print(f"Epoch [{epoch+1}/{num_epochs}], Loss: {loss.item():.4f}")

2. Integration with YOLOv8 Training: If you prefer to integrate your custom dataloader with the existing YOLOv8 training pipeline, you might need to modify the training script to accept your custom dataloader. This involves editing the training script to replace the default dataloader with your custom one.

3. Ensure Compatibility: Make sure that your custom dataloader outputs data in the format expected by the YOLO model. Typically, this includes images and corresponding labels in the correct format.

If you encounter any issues or need further assistance, please provide a minimum reproducible code example so we can better understand the problem. You can find more details on creating a minimum reproducible example here.

Feel free to reach out if you have any more questions. Happy training! 🚀