How to integrate a custom dataloader for training?

[alevangel]

I have a custom PyTorch Dataloader, how can use it to train a new model? Where are the templates tu use?

PS this dataloader generates synthetic images on the fly.

[glenn-jocher]

@alevangel hello there! To integrate your custom PyTorch DataLoader for training with YOLOv8, you'll need to make sure that your DataLoader outputs the required data format expected by the YOLOv8 training pipeline. This typically includes images, targets, image paths, and shapes.

Once you have your DataLoader ready, you can modify the dataset loading part of the training script to use your custom DataLoader instead of the default one. Look for the section in the training script where datasets are instantiated and replace the default DataLoader with your own.

Keep in mind that your DataLoader should be compatible with the multi-processing environment if you plan to use distributed training. Also, ensure that your synthetic images and annotations are correctly formatted to match the expectations of the loss functions and metrics used during training.

For more detailed guidance on the expected data formats and integration steps, please refer to the documentation on custom datasets and training at https://docs.ultralytics.com. Good luck with your synthetic image generation, and happy training! 🚀

[lesept777]

Hi, are there any examples available that would make this process easier?

[pderrenger]

Hello! 😊

Absolutely, integrating a custom PyTorch DataLoader for training with YOLOv8 is straightforward. You can directly use your DataLoader in the training script. Here's a quick example to guide you:

from ultralytics import YOLO
from your_custom_dataloader import YourCustomDataLoader

# Initialize your custom DataLoader
data_loader = YourCustomDataLoader(your_args_here)

# Load a YOLOv8 model
model = YOLO('yolov8n.pt')  # or your custom model

# Train the model using your DataLoader
model.train(data_loader=data_loader)

Make sure your DataLoader returns the data in the format expected by YOLOv8 (images, targets, paths, shapes, etc.). If you need further assistance or have specific requirements, feel free to ask!

Happy coding! 🚀

[tonyreina]

Just to make sure. I can do something like:

from torch.utils.data import DataLoader

my_train_dataloader = DataLoader(training_data, batch_size=64, shuffle=True)

model.train(data_loader=my_train_dataloader)

I just need the dataloader object to return KITTI-formatted images/labels/bboxes/etc.

Correct?

[pderrenger]

@tonyreina yes, that's correct! As long as your DataLoader returns the data in the expected format (images, labels, bounding boxes, etc., formatted as required by the model), you can use it directly in the model.train() method like you've shown. Just ensure that the data format aligns with what YOLOv8 expects for training. If everything is set up correctly, your snippet should work perfectly. Happy training! 🚀

[halqadasi]

@pderrenger I tried to use it with data_loader parameter and it throws an error:

Traceback (most recent call last):

  File "/usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell.py", line 3553, in run_code
    exec(code_obj, self.user_global_ns, self.user_ns)

  File ["<ipython-input-8-f70492c9994e>"](https://localhost:8080/#), line 5, in <cell line: 5>
    model.train(

  File "/usr/local/lib/python3.10/dist-packages/ultralytics/engine/model.py", line 799, in train
    self.trainer = (trainer or self._smart_load("trainer"))(overrides=args, _callbacks=self.callbacks)

  File "/usr/local/lib/python3.10/dist-packages/ultralytics/engine/trainer.py", line 101, in __init__
    self.args = get_cfg(cfg, overrides)

  File "/usr/local/lib/python3.10/dist-packages/ultralytics/cfg/__init__.py", line 294, in get_cfg
    check_dict_alignment(cfg, overrides)

  File "/usr/local/lib/python3.10/dist-packages/ultralytics/cfg/__init__.py", line 482, in check_dict_alignment
    raise SyntaxError(string + CLI_HELP_MSG) from e

  File "<string>", line unknown
SyntaxError: 'data_loader' is not a valid YOLO argument.