Topics / #augmentation

Augmentation projects on GitHub

Data augmentation techniques for enriching training images and other inputs.

As of , the most-starred Augmentation projects on GitHub are Fabric (44.3K stars), LifeOS (19.5K stars) and vision (18K stars). GitHub.Locker tracks 6 Augmentation repositories with 123.2K stars combined. The fastest-growing on October 16 was Fabric, with +21 stars in a day.

6Projects
123.2KStars in total
+40Stars in a day

Hot right now

Most stars gained in a day · Oct 16

The all-time top

6 projects · by stars

  1. #1 · danielmiessler/Fabric

    Fabric

    Fabric is an open-source framework that organizes AI prompts (called Patterns) by real-world task, so you can apply AI to specific problems from the command line or via a REST API.

    44.3K
  2. #2 · danielmiessler/LifeOS

    LifeOS

    LifeOS is a general-purpose AI harness for life and work: it captures who you are, what you care about, and where you're trying to go, then uses AI that knows you to move you from your Current State to your Ideal State.

    19.5K
  3. #3 · pytorch/vision

    vision

    Torchvision is PyTorch's computer vision package: popular datasets, ready-made model architectures, and common image transformations for vision work.

    18K
  1. 4

    albumentations

    15.3Kalbumentations-team/albumentations · Python

    Albumentations is a fast, flexible Python library for image augmentation used in training computer vision models, offering a huge set of pixel-level and spatial-level transforms for images, masks, bounding boxes, keypoints, and even 3D volumes.

  2. 5

    imgaug

    14.7Kaleju/imgaug · Python

    imgaug is a Python library that augments images for machine learning by converting a set of input images into a much larger set of slightly altered versions.

  3. 6

    kornia

    11.4Kkornia/kornia · Python

    Kornia is an open-source differentiable computer vision library for Python: 500+ image processing, geometry and augmentation operators plus pre-trained AI models, all built on PyTorch.

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