- Implemented only for ImageNetLT
- `normal_teachers` is the `Standard model` from the paper
- `aug_teachers` is the `Data Augmentation model` from the paper
data_root
for your dataset in main.py
.wandb.init
in main.py
accordingly.multi_runs.sh
python main.py --experiment=0.1 --seed=1 --gpu="0,1" --train --log_offline
python main.py --experiment=0.2 --seed=1 --gpu="0,1" --train --log_offline
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"
(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.If something is not clear or you found a bug, raise an issue!!