Next-generation Albumentations: dual-licensed for open-source and commercial use

data-augmentation deep-learning deeplearning image-augmentation image-classification image-processing image-segmentations instance-segmentation maching-learning object-detection python
8 Open Issues Need Help Last updated: Jun 24, 2025

Open Issues Need Help

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AI Summary: The task is to add the `apply_to_images` functionality to the `ToRGB` transformation in the AlbumentationsX library. This involves modifying the existing `ToRGB` class to accept and process image data in a way consistent with other transformations that support batch processing via `apply_to_images`. The fix references a similar issue in the original Albumentations library, suggesting a potential solution path.

Complexity: 3/5
enhancement good first issue

Next-generation Albumentations: dual-licensed for open-source and commercial use

Python
#data-augmentation#deep-learning#deeplearning#image-augmentation#image-classification#image-processing#image-segmentations#instance-segmentation#maching-learning#object-detection#python

AI Summary: The task is to add the `apply_to_images` functionality to the `ZoomBlur` augmentation in the AlbumentationsX library. This involves modifying the existing `ZoomBlur` class to allow for applying the transformation only to image data, while leaving other data types (masks, bounding boxes, keypoints) unchanged, similar to how it's handled in other augmentations.

Complexity: 4/5
enhancement good first issue

Next-generation Albumentations: dual-licensed for open-source and commercial use

Python
#data-augmentation#deep-learning#deeplearning#image-augmentation#image-classification#image-processing#image-segmentations#instance-segmentation#maching-learning#object-detection#python

AI Summary: The task is to add an `apply_to_images` method to the `Affine` transformation in the AlbumentationsX library. This would allow users to apply the affine transformation to multiple images simultaneously, improving efficiency. The request originates from a similar feature request in the original Albumentations library.

Complexity: 4/5
enhancement good first issue

Next-generation Albumentations: dual-licensed for open-source and commercial use

Python
#data-augmentation#deep-learning#deeplearning#image-augmentation#image-classification#image-processing#image-segmentations#instance-segmentation#maching-learning#object-detection#python

AI Summary: The task is to add an `apply_to_images` method to the `BaseDistortion` class in AlbumentationsX. This method should efficiently apply image distortions to multiple images simultaneously, mirroring a similar feature request for the original Albumentations library. The implementation should maintain AlbumentationsX's API compatibility and performance standards.

Complexity: 4/5
enhancement good first issue

Next-generation Albumentations: dual-licensed for open-source and commercial use

Python
#data-augmentation#deep-learning#deeplearning#image-augmentation#image-classification#image-processing#image-segmentations#instance-segmentation#maching-learning#object-detection#python

AI Summary: The task is to add an `apply_to_images` method to the `MaxSizeTransform` class within the AlbumentationsX library. This would allow for consistent application of transformations to images, mirroring existing functionality in other AlbumentationsX transforms. The request originates from a similar feature request for the original Albumentations library.

Complexity: 4/5
enhancement good first issue

Next-generation Albumentations: dual-licensed for open-source and commercial use

Python
#data-augmentation#deep-learning#deeplearning#image-augmentation#image-classification#image-processing#image-segmentations#instance-segmentation#maching-learning#object-detection#python

AI Summary: The task is to add the `apply_to_images` functionality to the `PixelDropout` augmentation in the AlbumentationsX library. This involves modifying the existing `PixelDropout` class to accept and process an `apply_to_images` parameter, ensuring it correctly handles image transformations while maintaining compatibility with other AlbumentationsX features.

Complexity: 4/5
enhancement good first issue

Next-generation Albumentations: dual-licensed for open-source and commercial use

Python
#data-augmentation#deep-learning#deeplearning#image-augmentation#image-classification#image-processing#image-segmentations#instance-segmentation#maching-learning#object-detection#python

AI Summary: Implement the `apply_to_images` method for the `Perspective` transform in AlbumentationsX, mirroring the functionality from the original Albumentations library. This will allow users to apply the perspective transformation to multiple images simultaneously, improving efficiency.

Complexity: 4/5
enhancement good first issue

Next-generation Albumentations: dual-licensed for open-source and commercial use

Python
#data-augmentation#deep-learning#deeplearning#image-augmentation#image-classification#image-processing#image-segmentations#instance-segmentation#maching-learning#object-detection#python

AI Summary: Implement the ability to adjust parameters (brightness, contrast, saturation, etc.) in color spaces other than HSV within the AlbumentationsX library. This involves adding new parameters or modifying existing ones in relevant transformation functions to support alternative color spaces like LAB, YUV, etc., ensuring backward compatibility and thorough testing.

Complexity: 4/5
enhancement good first issue

Next-generation Albumentations: dual-licensed for open-source and commercial use

Python
#data-augmentation#deep-learning#deeplearning#image-augmentation#image-classification#image-processing#image-segmentations#instance-segmentation#maching-learning#object-detection#python