ip-augmentation Goto Github PK
Type: Organization
Type: Organization
Updating!
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
Augmentations usage examples for albumentations library
Official Implementation of AlignMixup - CVPR 2022
Advanced data augmentation with Generative Adversarial Networks for vehicle detection
A data augmentations library for audio, image, text, and video.
[NeurIPS'21] "AugMax: Adversarial Composition of Random Augmentations for Robust Training" by Haotao Wang, Chaowei Xiao, Jean Kossaifi, Zhiding Yu, Animashree Anandkumar, and Zhangyang Wang.
[CVPR 2021] Code for "Augmentation Strategies for Learning with Noisy Labels".
Image augmentation library in Python for machine learning.
AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty
This library augments road images to introduce various real world scenarios that pose challenges for training neural networks of Autonomous vehicles. Automold is created to train CNNs in specific weather and road conditions.
This is a list of awesome methods about data augmentation.
An integration of several popular automatic augmentation methods, including OHL (Online Hyper-Parameter Learning for Auto-Augmentation Strategy) and AWS (Improving Auto Augment via Augmentation Wise Weight Sharing) by Sensetime Research.
Cut and paste augmentation for object detection and instance segmentation
Official PyTorch implementation of the “Context Decoupling Augmentation for Weakly Supervised Semantic Segmentation” (ICCV 2021)
Official PyTorch implementation of "Co-Mixup: Saliency Guided Joint Mixup with Supermodular Diversity" (ICLR'21 Oral)
[MICCAI 2021 Oral] Cooperative Training and Latent Space Data Augmentation for Robust Medical Image Segmentation
Copy-paste augmentation for segmentation and detection tasks
Unofficial implementation of Copy-Paste method:Simple Copy-Paste is a Strong Data Augmentation Method for Instance Segmentation
Official Pytorch implementation of Cut-Thumbnail
Rethinking Data Augmentation for Image Super-resolution (CVPR 2020)
PyTorch implementation of our CVPR2021 (oral) paper "Prototype Augmentation and Self-Supervision for Incremental Learning"
Self-supervised Augmentation Consistency for Adapting Semantic Segmentation (CVPR 2021)
“Style Transfer as Data Augmentation: A Case Study on Named Entity Recognition” (EMNLP 2022)
The official code for DADA: Differentiable Automatic Data Augmentation (ECCV20)
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.
Official Implementation of 'Fast AutoAugment' in PyTorch.
Fourier Domain Adaptation for Semantic Segmentation
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.