This is the source code for our CVPR (2022) paper: Long-tailed Visual Recognition via Gaussian Clouded Logit Adjustment based on Pytorch. This version is a demo of how to use GCL loss. The version that supports more datasets is in the works and is coming soon.
First stage
$ python cifar_train_backbone.py --arch resnet32 /
--dataset cifar10 --data_path './dataset/data_img' /
--loss_type 'GCL' --imb_factor 0.01 /
--batch_size 64 --learning_rate 0.1 Second stage
$ python cifar_train_classifier.py --arch resnet32 /
--dataset cifar10 --data_path './dataset/data_img' /
--loss_type 'GCL' --imb_factor 0.01 /
--train_rule 'BalancedRS'/
--batch_size 64 --learning_rate 0.1 - Support Cifar10/100-LT dataset
- Support imageNet-LT
- Support iNaturalist2018
- Support Places365-LT
- More loss functions
- Separate configuration files for easier execution
- Some other minor performance improvements
@inproceedings{Li2022Long,
author = {Mengke Li, Yiu{-}ming Cheung, Yang Lu},
title = {Long-tailed Visual Recognition via Gaussian Clouded Logit Adjustment},
pages = {},
booktitle = {CVPR},
year = {2022},
}
Awesome-of-Long-Tailed-Recognition
Long-Tailed-Classification-Leaderboard
If you have any questions, please send the email to Mengke Li at: [email protected].
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