Pytorch Implementation of Class-Balanced Distillation for Long-Tailed Visual Recognition by Ahmet Iscen, André Araujo, Boqing Gong, Cordelia Schmid

Note:

- Implemented only for ImageNetLT
- `normal_teachers` is the `Standard model` from the paper
- `aug_teachers` is the `Data Augmentation model` from the paper

Things to do before you run :

• Change the data_root for your dataset in main.py.
• If you are using wandb logging (Weights & Biases), make sure to change the wandb.init in main.py accordingly.

How to use?

• Easy to use : Check this script - multi_runs.sh
• Train the normal teachers :

python main.py --experiment=0.1 --seed=1 --gpu="0,1" --train --log_offline

• Train the augmentation teachers :

python main.py --experiment=0.2 --seed=1 --gpu="0,1" --train --log_offline

• Train the Class Balanced Distilled Student :

python main.py --experiment=0.3 --alpha=0.4 --beta=100 --seed=$seeds --gpu="0,1" --train --log_offline --normal_teacher="10,20" --aug_teacher="20,30"

Arguments :

(General)

• --seed: Seed of your current run
• --gpu: GPUs to be used
• --experiment: Experiment number (Check libs/utils/experiment_maker.py for more details)
• --wandb_logger: Does wandb Logging
• --log_offline: Does offline Logging
• --resume: Resumes the training if the run crashes

(Specific to Distillation and Student's training)

• --alpha: Weightage between Classifier loss and distillation loss
• --beta: weightage for the Cosine Similarity between teachers and student
• --normal_teachers: What all seed of norma teachers do you want to use? If you want to use only augmentation teachers, just don't use this argument. It is None by default.
• --aug_teachers: What all seed of augmented teachers do you want to use? If you want to use only normal teachers, just don't use this argument. It is None by default.

Raise an issue :

If something is not clear or you found a bug, raise an issue!!