| data_root=/opt/tiger/xiaomoguhzz/standard_coco | |
| pretrain_ckpt=/opt/tiger/xiaomoguhzz/EVA02_CLIP_B_psz16_s8B.pt | |
| exp_name=evab_dinov2B_csa_560_0.05_2.0 | |
| vfm_type=dinov2-B # {sam-B, sam-L, dinov2-B, dinov2-L, dino-B-8, dino-B-16} | |
| dataset_type=grid_distill # {proposals_distill,grid_distill,dift_grid_distill} | |
| # 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 EVA02-CLIP-B-16 --pretrained eva --warmup 1000 --zeroshot-frequency 1 --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_EVACLIP_ViTB16.npy --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 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 1.0 |