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DiffICM
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#### access DINOv2
export CUDA_VISIBLE_DEVICES=6;

python fast_vis_settings_all.py \
    --batch-size 64 --warmup-epochs 5 --epochs 300 \
    --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 1.0 --lambda_patch 1.0 \
    --finetune "/data0/qiyp/Proteus-pytorch/pretrain/log/DINOv2_training/checkpoint0160.pth" \
    --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;