openmixup and Awesome-Mixup
The toolbox for visual representation learning and the survey of mixup augmentations are complements, as the former provides an implementation framework for various mixup techniques while the latter offers a comprehensive overview and analysis of those techniques and their extensions.
About openmixup
Westlake-AI/openmixup
CAIRI Supervised, Semi- and Self-Supervised Visual Representation Learning Toolbox and Benchmark
This is a toolbox for machine learning engineers and researchers who develop computer vision systems. It helps you efficiently experiment with different visual representation learning techniques, specifically supervised, semi-supervised, and self-supervised methods that often involve 'mixup' data augmentation. You can input image datasets and configuration files, and it outputs trained models capable of image classification or acting as powerful pre-trained models for tasks like object detection or segmentation.
About Awesome-Mixup
Westlake-AI/Awesome-Mixup
[Survey] Awesome List of Mixup Augmentation and Beyond (https://arxiv.org/abs/2409.05202)
This is a curated collection of Mixup augmentation techniques used in machine learning for improving model generalization and preventing overfitting. It provides an overview of various data-centric methods, including their implementations in PyTorch, for tasks like image classification, object detection, and natural language processing. Machine learning researchers, data scientists, and AI practitioners looking to enhance their model training strategies would find this resource valuable.
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