Awesome
Long-Tailed Recognition by Mutual Information Maximization between Latent Features and Ground-Truth Labels
Python code for ICML 2023 paper entitled "Long-Tailed Recognition by Mutual Information Maximization between Latent Features and Ground-Truth Labels"
Test environment:
- Ubuntu 18.04
- CUDA 11.3, cuDNN 8.3.2
Requirements:
- Python 3.8
- Pytorch 1.12.1
- tqdm
- scikit-learn
- tensorboard
- matplotlib
This code is based on:
- https://github.com/facebookresearch/moco
- https://github.com/dvlab-research/Parametric-Contrastive-Learning/tree/main/PaCo/LT
Experiment procedure:
- (Set dataset directory) Change --data argument of sh/ImageNetLT_train_teacher.sh and sh/ImageNetLT_train_student.sh
- (Train teacher) Run bash ./sh/ImageNetLT_train_teacher.sh
- (Train student) Run bash ./sh/ImageNetLT_train_student.sh
Long-tailed recognition accuracy:
| Dataset | Backbone | Epochs | Top-1 Acc(%) |
|---|---|---|---|
| ImageNet-LT | ResNeXt-50 | 90 | 58.3 |
| ImageNet-LT | ResNeXt-50 | 400 | 58.8 |
| iNaturalist 2018 | ResNet-50 | 100 | 73.1 |
| iNaturalist 2018 | ResNet-50 | 400 | 74.5 |
| CIFAR-100-LT (imb. 100) | ResNet-32 | 200 | 53.0 |
| CIFAR-100-LT (imb. 50) | ResNet-32 | 200 | 57.6 |
| CIFAR-100-LT (imb. 10) | ResNet-32 | 200 | 65.7 |
| CIFAR-100-LT (imb. 100) | ResNet-32 | 400 | 54.0 |
| CIFAR-100-LT (imb. 50) | ResNet-32 | 400 | 58.1 |
| CIFAR-100-LT (imb. 10) | ResNet-32 | 400 | 67.0 |