Official cleanlab repo is at https://github.com/cleanlab/cleanlab
cgnorthcutt/cleanlab 的主要功能包括：Robust Learning Frameworks, Data Wrangling。
cgnorthcutt/cleanlab 的开源替代品包括： aleju/imgaug — imgaug is a Python library for machine learning data augmentation and computer vision dataset expansion. It provides… alfredxiangwu/lightcnn — A Light CNN for Deep Face Representation with Noisy Labels, TIFS 2018. alibaba-edu/ranking-based-instance-selection — Ranking-based-Instance-Selection. antoninbrthn/csidn — Code for the article "Confidence Scores Make Instance-dependent Label-noise Learning Possible", ICML'21. anuragkr90/webly-labeled-sounds — Github repo for webly labeled learning of sound events. albu/albumentations — Albumentations is an image augmentation library and computer vision preprocessing tool designed to expand datasets for…
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
A Light CNN for Deep Face Representation with Noisy Labels, TIFS 2018
Ranking-based-Instance-Selection
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