mdbloice/Augmentor

Features

• Computer Vision Dataset Expansion - Increases training set size and diversity by generating multiple synthetic variations of images.
• Image Augmentation - Applies geometric distortions and random erasures to images to improve vision model robustness.
• Transform Sequence Assemblers - Implements sequences of transformations to generate augmented data from a single input image.
• Image Data Preprocessing - Provides utilities to clean and standardize raw image files for use in machine learning processing pipelines.
• Image Dataset Expansion Frameworks - Provides a framework to expand image datasets using synchronized transformations for images and masks.
• ML Training Data Streamers - Streams augmented images and labels directly into training loops to avoid storing intermediate files.
• On-the-Fly Training Transformations - Streams augmented images and labels directly into training loops to avoid writing intermediate files to disk.
• Synchronized Label Augmentations - Applies identical geometric transformations to images and labels to maintain spatial alignment.
• Image Augmentations - A Python library for generating synthetic image variations to increase training dataset diversity.
• Training Sample Streaming - Streams augmented image batches and labels directly into neural network training loops.
• Geometric Mask Synchronization - Ensures that spatial coordinates and rotation parameters are applied identically to both images and their label masks.
• Class-Specific Augmentation Strategies - Allows defining unique processing pipelines for different object categories to handle data imbalances.
• Per-Class Processing Configurations - Provides a configuration system for unique processing pipelines tailored to different object categories.
• Computer Vision Preprocessing - Provides utilities for cleaning and standardizing raw image files to prepare them for training.
• Region Erasers - Implements random region erasing to obscure portions of images, improving model robustness against occlusions.
• Semantic Segmentation Training - Maintains spatial alignment between images and masks during transformations for pixel-level labeling.
• Parallel Image Generation - Uses multi-threading to distribute image processing tasks across CPU cores for faster dataset generation.
• Rotation and Shear transforms - Provides capabilities to rotate and shear images while cropping results to maintain consistent dimensions.
• Coordinate-Based Spatial Distortions - Provides spatial distortions like tilt, skew, and elastic warping to simulate various camera perspectives.
• Elastic Distortion Augmentations - Implements elastic distortions to create realistic image variations while preserving labels and aspect ratios.
• Perspective Warping - Provides geometric transformations to alter image viewpoints and simulate different camera angles through tilt and skew.
• Parallel Image Transformers - Uses multi-threading to accelerate the parallel generation and saving of augmented image datasets across CPU cores.
• Computer Vision - Image augmentation library.
• Developer Tools - Python library for image augmentation in machine learning pipelines.

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