mdbloice/augmentor की मुख्य विशेषताएं क्या हैं?

mdbloice/augmentor की मुख्य विशेषताएं हैं: 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।

mdbloice/augmentor के कुछ ओपन-सोर्स विकल्प क्या हैं?

mdbloice/augmentor के ओपन-सोर्स विकल्पों में शामिल हैं: 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

Augmentor एक Python इमेज ऑगमेंटेशन लाइब्रेरी और फ्रेमवर्क है जिसे मशीन लर्निंग डेटासेट का विस्तार करने के लिए डिज़ाइन किया गया है। यह एक प्रीप्रोसेसिंग टूल के रूप में कार्य करता है जो डेटा विविधता बढ़ाने के लिए सिंथेटिक इमेज वेरिएशन जनरेट करता है और एक ट्रेनिंग डेटा स्ट्रीमर के रूप में जो इंटरमीडिएट डिस्क स्टोरेज की आवश्यकता के बिना ऑगमेंटेड इमेज और लेबल को सीधे न्यूरल नेटवर्क लूप में फ़ीड करता है।

यह फ्रेमवर्क इमेज और उनके संबंधित मास्क के बीच स्थानिक संरेखण (spatial alignment) बनाए रखता है, जो सिमेंटिक सेगमेंटेशन ट्रेनिंग के लिए आवश्यक है। यह इलास्टिक डिस्टॉर्शन, टिल्ट और स्क्यू के माध्यम से पर्सपेक्टिव शिफ्ट, रोटेशन, शियरिंग और रैंडम रीजन इरेज़िंग सहित विभिन्न ज्यामितीय और पिक्सेल-स्तरीय परिवर्तनों का समर्थन करता है।

इस सिस्टम में डेटा असंतुलन को संबोधित करने के लिए प्रति-वर्ग प्रसंस्करण रणनीतियों के लिए क्षमताएं शामिल हैं और ऑगमेंटेड डेटासेट के समानांतर निर्माण में तेज़ी लाने के लिए मल्टी-थ्रेडिंग का उपयोग करता है। यह प्रीप्रोसेसिंग चरण के दौरान रॉ इमेज फ़ाइलों को साफ़ और मानकीकृत करने के लिए यूटिलिटीज़ भी प्रदान करता है।

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.
कंप्यूटर विज़न - Image augmentation library.
Data Wrangling - User-friendly tool for image augmentation in classification tasks.
डेवलपर टूल्स - Python library for image augmentation in machine learning pipelines.

स्टार हिस्ट्री

mdbloiceAugmentor

Name: mdbloice/augmentor
Author: mdbloice

View on GitHub

5,137 स्टार्स873 फोर्क्सPythonMIT8 व्यूज़augmentor.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.
कंप्यूटर विज़न - Image augmentation library.
Data Wrangling - User-friendly tool for image augmentation in classification tasks.
डेवलपर टूल्स - Python library for image augmentation in machine learning pipelines.

Augmentor के ओपन-सोर्स विकल्प

समान ओपन-सोर्स प्रोजेक्ट्स, जो Augmentor के साथ साझा की गई सुविधाओं के आधार पर रैंक किए गए हैं।

aleju/imgaug
aleju/imgaug
14,742GitHub पर देखें
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
Python
GitHub पर देखें14,742
albumentations-team/albumentations
albumentations-team/albumentations
15,308GitHub पर देखें
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
Python
GitHub पर देखें15,308
albu/albumentations
albu/albumentations
15,308GitHub पर देखें
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
Python
GitHub पर देखें15,308
facebookresearch/augly
facebookresearch/AugLy
5,086GitHub पर देखें
AugLy is a multimodal data augmentation library and machine learning dataset augmentor. It provides a system for generating synthetic variations of training data across audio, image, text, and video datasets to increase sample diversity and improve model robustness. The library functions as a multimedia noise simulator, specifically designed to mimic real-world user captures by overlaying social media templates and internet artifacts onto media. It includes a data provenance tracker to record the specific transformations and intensity levels applied to each piece of augmented data. The tool
Python
GitHub पर देखें5,086

Augmentor के सभी 30 विकल्प देखें

अक्सर पूछे जाने वाले प्रश्न

mdbloice/augmentor क्या करता है?

Augmentor

Features

स्टार हिस्ट्री

Augmentor

Features

Augmentor के ओपन-सोर्स विकल्प

aleju/imgaug

albumentations-team/albumentations

albu/albumentations

facebookresearch/AugLy

अक्सर पूछे जाने वाले प्रश्न

स्टार हिस्ट्री

Augmentor के ओपन-सोर्स विकल्प

aleju/imgaug

albumentations-team/albumentations

albu/albumentations

facebookresearch/AugLy

अक्सर पूछे जाने वाले प्रश्न