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#### 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; |