xueyouluo/examples

cleanlab Examples

Not sure where to start? Try checking out how to find ImageNet Label Errors.

A brief description of the files and folders:

• imagenet, 'cifar10', 'mnist' - code to find label errors in these datasets and reproduce the results in the confident learning paper. You will also need to git clone confidentlearning-reproduce.
  • imagenet_train_crossval.py - a powerful script to train cross-validated predictions on ImageNet, combine cv folds, train with on masked input (train without label errors), etc.
  • cifar10_train_crossval.py - same as above, but for CIFAR.
• classifier_comparison.ipynb - tutorial showing cleanlab performance across 10 classifiers and 4 dataset distributions.
• iris_simple_example.ipynb - tutorial showing how to use cleanlab on the simple IRIS dataset.
• model_selection_demo.ipynb - tutorial showing model selection on the cleanlab's parameter settings.
• simplifying_confident_learning_tutorial.ipynb - tutorial implementing cleanlab as raw numpy code.
• visualizing_confident_learning.ipynb - tutorial to demonstrate the noise matrix estimation performed by cleanlab.

License

Copyright (c) 2017-2021 Cleanlab Inc.

All files listed above and contained in this folder (https://github.com/cleanlab/examples) are part of cleanlab.

cleanlab is free software: you can redistribute it and/or modify it under the terms of the GNU Affero General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

cleanlab is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Affero General Public License for more details.

You should have received a copy of the GNU Affero General Public License in LICENSE.