There are 15 repositories under augmentation topic.
Fast image augmentation library and an easy-to-use wrapper around other libraries. Documentation: https://albumentations.ai/docs/ Paper about the library: https://www.mdpi.com/2078-2489/11/2/125
fabric is an open-source framework for augmenting humans using AI. It provides a modular framework for solving specific problems using a crowdsourced set of AI prompts that can be used anywhere.
Keras model to generate HTML code from hand-drawn website mockups. Implements an image captioning architecture to drawn source images.
Data augmentation for NLP
A Python library for audio data augmentation. Inspired by albumentations. Useful for machine learning.
Official Implementation of 'Fast AutoAugment' in PyTorch.
PyTorch extensions for fast R&D prototyping and Kaggle farming
Official Pytorch implementation of CutMix regularizer
Fast audio data augmentation in PyTorch. Inspired by audiomentations. Useful for deep learning.
Unofficial PyTorch Reimplementation of RandAugment.
Long-Term Evolution Project of Reinforcement Learning
Code for ICCV2019 paper "InstaBoost: Boosting Instance Segmentation Via Probability Map Guided Copy-Pasting"
TextAugment: Text Augmentation Library
automatic color-grading
Includes: Learning data augmentation strategies for object detection | GridMask data augmentation | Augmentation for small object detection in Numpy. Use RetinaNet with ResNet-18 to test these methods on VOC and KITTI.
AutoML for image augmentation. AutoAlbument uses the Faster AutoAugment algorithm to find optimal augmentation policies. Documentation - https://albumentations.ai/docs/autoalbument/
Publicly available event datasets and transforms.
🌲 Great Ruby logging made easy.
Augmenty is an augmentation library based on spaCy for augmenting texts.
A simpler way of reading and augmenting image segmentation data into TensorFlow
This is the official implementation of our neural-network-based fast diffuse room impulse response generator (FAST-RIR) for generating room impulse responses (RIRs) for a given acoustic environment.
Test Time Augmentation (TTA) wrapper for computer vision tasks: segmentation, classification, super-resolution, ... etc.
Pitch-shift audio clips quickly with PyTorch (CUDA supported)! Additional utilities for searching efficient transformations are included.