Augmentor

Augmentor este o bibliotecă și un framework Python de augmentare a imaginilor, conceput pentru a extinde seturile de date de machine learning. Funcționează ca un instrument de preprocesare care generează variații sintetice ale imaginilor pentru a crește diversitatea datelor și ca un streamer de date de antrenament care introduce imagini și etichete augmentate direct în buclele rețelelor neuronale, fără a necesita stocare intermediară pe disc.

Framework-ul menține alinierea spațială între imagini și măștile corespunzătoare, ceea ce este necesar pentru antrenarea segmentării semantice. Suportă diverse transformări geometrice și la nivel de pixel, inclusiv distorsiuni elastice, schimbări de perspectivă prin înclinare și rotire, rotație, forfecare și ștergerea aleatorie a regiunilor.

Sistemul include capabilități pentru strategii de procesare per-clasă pentru a aborda dezechilibrul datelor și utilizează multi-threading pentru a accelera generarea paralelă a seturilor de date augmentate. Oferă, de asemenea, utilitare pentru curățarea și standardizarea fișierelor imagine brute în etapa de preprocesare.

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.
Data Wrangling - User-friendly tool for image augmentation in classification tasks.
Instrumente pentru dezvoltatori - Python library for image augmentation in machine learning pipelines.

Istoric stele

mdbloiceAugmentor

Name: mdbloice/augmentor
Author: mdbloice

View on GitHub

5,137 stele873 fork-uriPythonMIT7 vizualizăriaugmentor.readthedocs.io/en/stable

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.
Data Wrangling - User-friendly tool for image augmentation in classification tasks.
Instrumente pentru dezvoltatori - Python library for image augmentation in machine learning pipelines.

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Întrebări frecvente

Ce face mdbloice/augmentor?

Care sunt principalele funcționalități ale mdbloice/augmentor?

Principalele funcționalități ale mdbloice/augmentor sunt: Computer Vision Dataset Expansion, Image Augmentation, Transform Sequence Assemblers, Image Data Preprocessing, Image Dataset Expansion Frameworks, ML Training Data Streamers, On-the-Fly Training Transformations, Synchronized Label Augmentations.

Care sunt câteva alternative open-source pentru mdbloice/augmentor?

Alternativele open-source pentru mdbloice/augmentor includ: aleju/imgaug — imgaug is a Python library for machine learning data augmentation and computer vision dataset expansion. It provides… albumentations-team/albumentations — Albumentations is a computer vision image augmentation library designed to increase training data diversity for deep… albu/albumentations — Albumentations is an image augmentation library and computer vision preprocessing tool designed to expand datasets for… facebookresearch/augly — AugLy is a multimodal data augmentation library and machine learning dataset augmentor. It provides a system for… open-mmlab/mmsegmentation — MMSegmentation is an open-source semantic segmentation toolbox built on PyTorch that provides a modular, configurable… open-mmlab/mmcv — mmcv is a foundation library for computer vision based on PyTorch. It provides a comprehensive system for constructing…

Augmentor

Features

Istoric stele

Augmentor

Features

Alternative open-source pentru Augmentor

aleju/imgaug

albumentations-team/albumentations

albu/albumentations

Întrebări frecvente

Istoric stele

Întrebări frecvente

Alternative open-source pentru Augmentor

aleju/imgaug

albumentations-team/albumentations

albu/albumentations

facebookresearch/AugLy