1_feature_extractor/1_training_IB.sh

#### access DINOv2
export CUDA_VISIBLE_DEVICES=0,1,2;

python -m torch.distributed.launch --nproc_per_node=3 --use_env 1_main_training_IB.py \
    --batch-size 48 --warmup-epochs 5 --epochs 200 \
    --data-set IMNET --data-path '/data1/datasets/imagenet_fold' \
    --teacher-model vit_large --target_model vit_base --model models_proteus_dinov2 \
    --patch_size 14 --mask_probability 0.5 --mask_ratio 0.5 --mask_first_n \
    --lambda_token 1.0 --lambda_fea 1.05 --lambda_patch 1.05 \
    --resume "/data0/qiyp/Proteus-pytorch/pretrain/log/DINOv2_training/checkpoint0160.pth" \
    --log_dir '/data0/qiyp/Proteus-pytorch/pretrain/log/DINOv2_training/log/' \
    --output_dir log/DINOv2_training;



#### access SynCLR

# python -m torch.distributed.launch --nproc_per_node=4 --use_env main.py \
#     --batch-size 128 --warmup-epochs 5 --epochs 300 \
#     --data-set IMNET --data-path imagenet_path \
#     --teacher-model vit_large --target_model vit_base --model models_proteus_synclr \
#     --teacher-path pretrained_synclr_path \
#     --patch_size 14 --mask_probability 0.5 --mask_ratio 0.5 --mask_first_n \
#     --lambda_token 1.0 --lambda_fea 1.0 --lambda_patch 1.0 \
#     --output_dir log/SynCLR_training;



#### access CLIP

# python -m torch.distributed.launch --nproc_per_node=4 --use_env main.py \
#     --batch-size 128 --warmup-epochs 5 --epochs 300 \
#     --data-set IMNET --data-path imagenet_path \
#     --teacher-model vit_large --target_model vit_base --model models_proteus_clip \
#     --teacher-path pretrained_clip_path \
#     --patch_size 14 --mask_probability 0.5 --mask_ratio 0.5 --mask_first_n \
#     --lambda_token 1.0 --lambda_fea 0.0 --lambda_patch 0.0 \
#     --output_dir log/CLIP_training;