| # GPU selection (e.g., 0, 1, 2, 3 or 0,1 for multiple GPUs) | |
| gpu=1 | |
| # Training parameters | |
| # Mode options: qq, kk, vv, csa, qq_vfm_distill, kk_vfm_distill, vv_vfm_distill, csa_vfm_distill, all_vfm_distill, maskclip, vanilla, sanity_check | |
| mode=csa_vfm_distill | |
| det_image_size=560 | |
| loss_context_weight=0.25 | |
| loss_content_weight=1.0 | |
| data_root=/opt/tiger/xiaomoguhzz/standard_coco | |
| pretrain_ckpt=checkpoints/TinyCLIP-ViT-39M-16-Text-19M-YFCC15M.pt | |
| vfm_type=dinov2-B # {sam-B, sam-L, dinov2-B, dinov2-L, dino-B-8, dino-B-16} | |
| model_name=TinyCLIP-ViT-39M-16-Text-19M | |
| embed_path=metadata/coco_panoptic_clip_hand_craft_TinyCLIP-ViT-39M-16-Text-19M.npy | |
| # Extract mode name for exp_name (remove _vfm_distill suffix if present) | |
| mode_name=${mode%_vfm_distill} | |
| exp_name=TinyCLIP_39M_dinov2B_${mode_name}_${det_image_size}_${loss_context_weight}_${loss_content_weight} | |
| # Single GPU for debugging | |
| CUDA_VISIBLE_DEVICES=${gpu} python -m training.main --batch-size=2 --lr=1e-5 --wd=0.1 --epochs=6 --workers=4 \ | |
| --model ${model_name} --pretrained ${pretrain_ckpt} --warmup 1000 --zeroshot-frequency 1 --dataset-type grid_distill \ | |
| --test-type coco_panoptic --train-data ${data_root}/annotations/instances_train2017.json \ | |
| --val-data ${data_root}/annotations/panoptic_val2017.json \ | |
| --embed-path ${embed_path} --train-image-root ${data_root}/train2017 \ | |
| --val-image-root ${data_root}/val2017 --cache-dir ${pretrain_ckpt} --log-every-n-steps 100 \ | |
| --lock-image --save-frequency 1 --lock-image-unlocked-groups 12 \ | |
| --name ${exp_name} --downsample-factor 16 --det-image-size ${det_image_size} --val-segm-root ${data_root}/annotations/panoptic_val2017 \ | |
| --alpha 0.7 --mode ${mode} --use_vfm ${vfm_type} --loss_context_weight ${loss_context_weight} --loss_content_weight ${loss_content_weight} --version declip | |