albu/albumentationsArchived
Albumentations
Albumentations is an image augmentation library and computer vision preprocessing tool designed to expand datasets for deep learning models. It provides a collection of transformations that modify pixel values and spatial geometry to increase the diversity of training samples and improve model generalization.
The library supports both 2D image augmentation and 3D volumetric data augmentation. It handles a variety of labels alongside images, ensuring that bounding boxes, keypoints, and segmentation masks remain accurately aligned when spatial transformations are applied.
The tool incorporates capabilities for pixel-level transforms, spatial-level transforms, and volumetric medical imaging. These operations are managed through pipelines that apply random or deterministic sequences of geometric and color distortions to image datasets.
Features
- Image Augmentation - Implements a wide array of random transformations to artificially increase the diversity of image datasets.
- Spatial Coordinate Synchronization - Updates bounding boxes and keypoints automatically when spatial transformations are applied to images.
- Computer Vision Dataset Expansion - Increases the size and diversity of image datasets by applying random transformations to training samples.
- Computer Vision Preprocessing - Provides a comprehensive set of operations for modifying pixel values and spatial geometry for computer vision pipelines.
- Volumetric Data Augmentation - Applies 3D transformations to volumes and masks to create diverse training samples for spatial imaging.
- Deep Learning Dataset Expanders - Applies random geometric and color distortions to images to improve deep learning model generalization.
- Pixel-Level Transformations - Provides transforms to shift colors, add noise, and apply blurs while preserving label positions.
- Spatial-Level Transformations - Alters image geometry while updating associated masks and bounding boxes to maintain precise alignment.
- 3D Volumetric Augmenters - Acts as a specialized tool for applying spatial transformations to 3D volumes and masks.
- Segmentation Mask Handlers - Provides synchronized handling of segmentation masks to ensure they align perfectly with warped images.
- Augmentation - Determines transformation intensity at runtime by sampling from user-defined probability distributions.
- Overfitting Reduction Techniques - Reduces model overfitting by generating varied versions of training images through pixel and spatial modifications.
- Volumetric Medical Augmentation - Applies 3D transformations to medical spatial volumes and masks to create diverse samples for analysis.
- Vectorized Array Operations - Utilizes vectorized array operations via NumPy for high-performance pixel manipulation and coordinate transforms.
- Transformation Pipelines - Provides a composition-based pipeline for chaining random or deterministic image transformations.
- 计算机视觉 - Fast image augmentation wrapper.
- Deep Learning Frameworks - Fast image augmentation library for deep learning.
- Data Wrangling - High-performance library for image augmentation tasks.
- PyTorch Utilities - Listed in the “PyTorch Utilities” section of the The Incredible Pytorch awesome list.
