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2 repositorios

Awesome GitHub RepositoriesAugmentation

Preprocessing pipelines that synchronize bounding boxes and masks during image transformations.

Distinct from Object Detection: Focuses on data augmentation for detection pipelines rather than the detection inference process.

Explore 2 awesome GitHub repositories matching artificial intelligence & ml · Augmentation. Refine with filters or upvote what's useful.

Awesome Augmentation GitHub Repositories

Encuentra los mejores repositorios con IA.Buscaremos los repositorios que mejor coincidan usando IA.
  • albumentations-team/albumentationsAvatar de albumentations-team

    albumentations-team/albumentations

    15,308Ver en GitHub↗

    Albumentations is a computer vision image augmentation library designed to increase training data diversity for deep learning models. It provides a toolset for applying geometric and color transformations to images and annotations, including a specialized collection of 3D operations for volumetric data used in medical and scientific imaging. The library functions as an image mask and bounding box transformer, automatically updating masks, bounding boxes, and keypoints when images undergo geometric changes. This ensures that spatial alterations remain synchronized across images and their assoc

    Automatically updates bounding boxes and masks during image transformations to maintain label alignment.

    Python
    Ver en GitHub↗15,308
  • aleju/imgaugAvatar de aleju

    aleju/imgaug

    14,742Ver en GitHub↗

    imgaug is a Python library for machine learning data augmentation and computer vision dataset expansion. It provides tools to increase the volume and variety of training sets by applying random geometric, color, and noise transformations to images. The library ensures spatial consistency by synchronizing transformations across images and their associated annotations, such as bounding boxes, keypoints, and segmentation maps. It uses a compositional pipeline pattern to chain multiple augmentations into sequences and employs deterministic seed management to reproduce specific data samples. The

    Synchronizes the application of identical random transformations across images and their corresponding annotations.

    Python
    Ver en GitHub↗14,742
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