Similar Dataloader in yolov5 about yolov5 HOT 3 CLOSED

@BehdadSDP You're welcome! 😊 If you have any more questions or need further assistance as you implement this, feel free to reach out. Happy coding and best of luck with your project!

from yolov5.

@BehdadSDP hello 👋,

Great question! In YOLOv5, to ensure that two Dataloaders produce batches with the same images but with domain-specific differences (e.g., clear and foggy versions of the same scene), you would likely need to modify the data loading and augmentation pipeline directly. A practical approach would be to customize the LoadImagesAndLabels class to support paired data loading. This implies that for each batch, your modified loader reads both the original and domain-transformed versions of each image and applies the exact same augmentations to both versions synchronously.

Here's a conceptual outline of what you might consider doing:

1. Modify the dataset class to accept two directories (one for each domain).
2. Ensure that for each batch, the class loads paired images (one from each directory).
3. Apply the same augmentations to both images in the pair. This is crucial and can be achieved by ensuring the randomness in augmentations is controlled (e.g., by setting seeds) for each pair.

This approach requires a good understanding of both the YOLOv5 dataset management code and PyTorch's data handling mechanisms.

For a detailed look at YOLOv5's data loading and processing mechanisms, our documentation is always a helpful resource: https://docs.ultralytics.com/yolov5/.

I hope this sets you on the right path! Let us know how it goes.

from yolov5.

@BehdadSDP hello 👋,

Great question! In YOLOv5, to ensure that two Dataloaders produce batches with the same images but with domain-specific differences (e.g., clear and foggy versions of the same scene), you would likely need to modify the data loading and augmentation pipeline directly. A practical approach would be to customize the LoadImagesAndLabels class to support paired data loading. This implies that for each batch, your modified loader reads both the original and domain-transformed versions of each image and applies the exact same augmentations to both versions synchronously.

Here's a conceptual outline of what you might consider doing:

1. Modify the dataset class to accept two directories (one for each domain).
2. Ensure that for each batch, the class loads paired images (one from each directory).
3. Apply the same augmentations to both images in the pair. This is crucial and can be achieved by ensuring the randomness in augmentations is controlled (e.g., by setting seeds) for each pair.

This approach requires a good understanding of both the YOLOv5 dataset management code and PyTorch's data handling mechanisms.

For a detailed look at YOLOv5's data loading and processing mechanisms, our documentation is always a helpful resource: https://docs.ultralytics.com/yolov5/.

I hope this sets you on the right path! Let us know how it goes.

thank you so much. DONE.

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