Care sunt principalele funcționalități ale facebookresearch/augly?

Principalele funcționalități ale facebookresearch/augly sunt: Dataset Augmentors, Multimedia Dataset Augmentation, Training Data Augmentation, Image Augmentation, Audio Dataset Preprocessing, Image Dataset Expansion Frameworks, Machine Learning Data Augmentation, Multimodal Data Augmentation.

Care sunt câteva alternative open-source pentru facebookresearch/augly?

Alternativele open-source pentru facebookresearch/augly 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… 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

AugLy este o bibliotecă de augmentare a datelor multimodale și un augmentator de seturi de date pentru machine learning. Oferă un sistem pentru generarea de variații sintetice ale datelor de antrenament pe seturi de date audio, imagine, text și video pentru a crește diversitatea eșantioanelor și a îmbunătăți robustețea modelului.

Biblioteca funcționează ca un simulator de zgomot multimedia, conceput special pentru a imita capturile reale ale utilizatorilor prin suprapunerea șabloanelor de social media și a artefactelor de internet peste media. Include un tracker de proveniență a datelor pentru a înregistra transformările specifice și nivelurile de intensitate aplicate fiecărei piese de date augmentate.

Instrumentul acoperă o gamă largă de capabilități de expansiune a seturilor de date, inclusiv transformări lingvistice pentru text, transformări temporale și vizuale pentru video și transformări sonice pentru 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.
Computer Vision - 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.

Istoric stele

facebookresearchAugLy

Name: facebookresearch/augly
Author: facebookresearch

View on GitHub

5,086 stele312 fork-uriPython9 vizualizăriai.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.
Computer Vision - 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.

Alternative open-source pentru AugLy

Proiecte open-source similare, clasificate după numărul de funcționalități comune cu AugLy.

aleju/imgaug
aleju/imgaug
14,742Vezi pe GitHub
imgaug is a Python library for machine learning data augmentation and computer vision dataset expansion. It provides tools to increase the volume and variety of training sets by applying random geometric, color, and noise transformations to images. The library ensures spatial consistency by synchronizing transformations across images and their associated annotations, such as bounding boxes, keypoints, and segmentation maps. It uses a compositional pipeline pattern to chain multiple augmentations into sequences and employs deterministic seed management to reproduce specific data samples. The
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albumentations-team/albumentations
albumentations-team/albumentations
15,308Vezi pe GitHub
Albumentations is a computer vision image augmentation library designed to increase training data diversity for deep learning models. It provides a toolset for applying geometric and color transformations to images and annotations, including a specialized collection of 3D operations for volumetric data used in medical and scientific imaging. The library functions as an image mask and bounding box transformer, automatically updating masks, bounding boxes, and keypoints when images undergo geometric changes. This ensures that spatial alterations remain synchronized across images and their assoc
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mdbloice/augmentor
mdbloice/Augmentor
5,137Vezi pe GitHub
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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albu/albumentations
albu/albumentations
15,308Vezi pe GitHub
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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Vezi toate cele 30 alternative pentru AugLy

Întrebări frecvente

Ce face facebookresearch/augly?

AugLy

Features

Istoric stele

AugLy

Features

Alternative open-source pentru AugLy

aleju/imgaug

albumentations-team/albumentations

mdbloice/Augmentor

albu/albumentations

Întrebări frecvente

Istoric stele

Alternative open-source pentru AugLy

aleju/imgaug

albumentations-team/albumentations

mdbloice/Augmentor

albu/albumentations

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