Upload code/scripts/openai_declip+/dist_DeCLIP+_openai_vitb16_coco.sh with huggingface_hub

code/scripts/openai_declip+/dist_DeCLIP+_openai_vitb16_coco.sh

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+data_root=/opt/tiger/xiaomoguhzz/standard_coco
+exp_name=OPENAI-B_DINOv2-B_csa_560_declip+_0.25_1.0_0.3
+vfm_type=dinov2-B # {sam-B, sam-L, dinov2-B, dinov2-L, dino-B-8, dino-B-16}
+dataset_type=proposals_distill # {proposals_distill,grid_distill,dift_grid_distill,dift_proposals_distill}
+version=declip2 # {declip,declip2,declip+}
+# always keep total batchsize=16 , otherwise, Linear scaling the learning rate
+CUDA_VISIBLE_DEVICES=0,1,2,3 torchrun --nproc_per_node 4 --master_port 29500 -m training.main --batch-size=4 --lr=1e-5 --wd=0.1 --epochs=6 --workers=4 \
+--model ViT-B-16 --pretrained openai --warmup 1000 --zeroshot-frequency 6 --dataset-type ${dataset_type} \
+--test-type coco_panoptic --train-data ${data_root}/annotations/instances_train2017.json \
+--val-data ${data_root}/annotations/panoptic_val2017.json \
+--embed-path metadata/coco_panoptic_clip_hand_craft_ViTB16.npy --train-image-root ${data_root}/train2017 \
+--val-image-root ${data_root}/val2017  --log-every-n-steps 100 \
+--lock-image --save-frequency 1 --lock-image-unlocked-groups 12 \
+--name ${exp_name} --downsample-factor 16 --det-image-size 560 --val-segm-root ${data_root}/annotations/panoptic_val2017 \
+--alpha 0.7 --mode csa_vfm_distill  --use_vfm ${vfm_type} --loss_context_weight 0.05 --loss_content_weight 2.0 --loss_region_weight 1.0 --skip-first-eval --repa_layer_idx -1 --sd-refine-weight 1.0 --cache-self-attn sd_self_attn_cache/sd_self_attn_coco.h5 --version ${version} --resume logs/OPENAI-B_DINOv2-B_csa_560_declip+_0.25_1.0_0.3/checkpoints/epoch_6.pt --eval