AugLy

AugLy es una biblioteca de aumento de datos multimodal y aumentador de conjuntos de datos de machine learning. Proporciona un sistema para generar variaciones sintéticas de datos de entrenamiento a través de conjuntos de datos de audio, imagen, texto y video para aumentar la diversidad de muestras y mejorar la robustez del modelo.

La biblioteca funciona como un simulador de ruido multimedia, diseñado específicamente para imitar capturas de usuarios del mundo real superponiendo plantillas de redes sociales y artefactos de internet sobre los medios. Incluye un rastreador de procedencia de datos para registrar las transformaciones específicas y los niveles de intensidad aplicados a cada pieza de datos aumentados.

La herramienta cubre una amplia gama de capacidades de expansión de conjuntos de datos, incluyendo transformaciones lingüísticas para texto, transformaciones temporales y visuales para video, y transformaciones sónicas para audio.

Features

Dataset Augmentors - A tool for generating synthetic variations of training data to improve the robustness and generalization of AI models.
Multimedia Dataset Augmentation - Generates synthetic variations of training data across audio, image, text, and video datasets to increase sample diversity.
Training Data Augmentation - Applies sonic transformations to audio datasets to increase variety and robustness for machine learning models.
Image Augmentation - Applies visual transformations to image datasets to increase sample diversity and improve model robustness.
Audio Dataset Preprocessing - Applies transformations to audio files to create more varied training samples for sound recognition or processing models.
Image Dataset Expansion Frameworks - Applies visual transformations to image datasets to increase sample variety and help models generalize better.
Machine Learning Data Augmentation - Increases the diversity of audio, image, text, and video training sets to improve the robustness of machine learning models.
Multimodal Data Augmentation - Apply transformations to diverse datasets using logic-based rules to increase data variety.
Text Data Augmentation - Applies linguistic transformations to text datasets to increase sample diversity and improve model robustness.
Unified Multimodal APIs - Offers a unified API interface to apply transformations across diverse modalities including text, audio, and video.
Video Data Augmentation - Applies temporal and visual transformations to video datasets to expand the diversity of training samples.
Video Data Augmentations - Provides temporal and visual transformations for video clips to expand the diversity of machine learning training samples.
Social Media Noise Simulations - Simulates real-world user captures by overlaying social media templates and internet artifacts onto images and videos.
Augmentation Provenance Tracking - Includes a data provenance tracker to record the specific transformations and intensity levels applied to each augmented data point.
Internet Artifact Simulation - Overlays text or social elements onto media to mimic real-world user behavior and internet artifacts for robustness testing.
Social Media Capture Simulation - Overlays images or videos onto feed templates to mimic how users capture content in real-world social media environments.
Social Media Noise Simulation - Adds overlays and templates to media to mimic real-world social media screenshots for testing model robustness against noise.
UI Template Overlays - Combines media samples with predefined social media layout templates to simulate realistic user interface captures.
Data Provenance Recorders - Records the specific transformations and intensity levels applied to augmented data to maintain a clear history of provenance.
Visión artificial - Multimodal augmentation library.
Deep Learning and Computer Vision - Data augmentation library for multiple media types.
Data Wrangling - Multi-modal data augmentation library for audio, image, and text.
Image Augmentation - Augmentation library for audio, image, text, and video data.

Historial de estrellas

facebookresearchAugLy

Name: facebookresearch/augly
Author: facebookresearch

View on GitHub

5,086 estrellas312 forksPython5 vistasai.facebook.com/blog/augly-a-new-data-augmentation-library-to-help-build-more-robust-ai-models

AugLy

Features

Dataset Augmentors - A tool for generating synthetic variations of training data to improve the robustness and generalization of AI models.
Multimedia Dataset Augmentation - Generates synthetic variations of training data across audio, image, text, and video datasets to increase sample diversity.
Training Data Augmentation - Applies sonic transformations to audio datasets to increase variety and robustness for machine learning models.
Image Augmentation - Applies visual transformations to image datasets to increase sample diversity and improve model robustness.
Audio Dataset Preprocessing - Applies transformations to audio files to create more varied training samples for sound recognition or processing models.
Image Dataset Expansion Frameworks - Applies visual transformations to image datasets to increase sample variety and help models generalize better.
Machine Learning Data Augmentation - Increases the diversity of audio, image, text, and video training sets to improve the robustness of machine learning models.
Multimodal Data Augmentation - Apply transformations to diverse datasets using logic-based rules to increase data variety.
Text Data Augmentation - Applies linguistic transformations to text datasets to increase sample diversity and improve model robustness.
Unified Multimodal APIs - Offers a unified API interface to apply transformations across diverse modalities including text, audio, and video.
Video Data Augmentation - Applies temporal and visual transformations to video datasets to expand the diversity of training samples.
Video Data Augmentations - Provides temporal and visual transformations for video clips to expand the diversity of machine learning training samples.
Social Media Noise Simulations - Simulates real-world user captures by overlaying social media templates and internet artifacts onto images and videos.
Augmentation Provenance Tracking - Includes a data provenance tracker to record the specific transformations and intensity levels applied to each augmented data point.
Internet Artifact Simulation - Overlays text or social elements onto media to mimic real-world user behavior and internet artifacts for robustness testing.
Social Media Capture Simulation - Overlays images or videos onto feed templates to mimic how users capture content in real-world social media environments.
Social Media Noise Simulation - Adds overlays and templates to media to mimic real-world social media screenshots for testing model robustness against noise.
UI Template Overlays - Combines media samples with predefined social media layout templates to simulate realistic user interface captures.
Data Provenance Recorders - Records the specific transformations and intensity levels applied to augmented data to maintain a clear history of provenance.
Visión artificial - Multimodal augmentation library.
Deep Learning and Computer Vision - Data augmentation library for multiple media types.
Data Wrangling - Multi-modal data augmentation library for audio, image, and text.
Image Augmentation - Augmentation library for audio, image, text, and video data.

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Augmentor is a Python image augmentation library and framework designed to expand machine learning datasets. It functions as a preprocessing tool that generates synthetic image variations to increase data diversity and as a training data streamer that feeds augmented images and labels directly into neural network loops without requiring intermediate disk storage. The framework maintains spatial alignment between images and their corresponding masks, which is required for semantic segmentation training. It supports various geometric and pixel-level transformations, including elastic distortion
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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
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Ver las 30 alternativas a AugLy

Preguntas frecuentes

¿Qué hace facebookresearch/augly?

¿Cuáles son las características principales de facebookresearch/augly?

Las características principales de facebookresearch/augly son: Dataset Augmentors, Multimedia Dataset Augmentation, Training Data Augmentation, Image Augmentation, Audio Dataset Preprocessing, Image Dataset Expansion Frameworks, Machine Learning Data Augmentation, Multimodal Data Augmentation.

¿Qué alternativas de código abierto existen para facebookresearch/augly?

Las alternativas de código abierto para facebookresearch/augly incluyen: 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… mdbloice/augmentor — Augmentor is a Python image augmentation library and framework designed to expand machine learning datasets. It… albu/albumentations — Albumentations is an image augmentation library and computer vision preprocessing tool designed to expand datasets for… makcedward/nlpaug — nlpaug is a data augmentation library designed to generate synthetic text, audio, and spectrogram data to improve the… kornia/kornia — Kornia is a differentiable computer vision library and cross-framework tensor vision toolset. It implements vision…

AugLy

Features

Historial de estrellas

AugLy

Features

Alternativas open-source a AugLy

aleju/imgaug

albumentations-team/albumentations

mdbloice/Augmentor

albu/albumentations

Preguntas frecuentes

Historial de estrellas

Alternativas open-source a AugLy

aleju/imgaug

albumentations-team/albumentations

mdbloice/Augmentor

albu/albumentations

Preguntas frecuentes