Skip to content

Latest commit

 

History

History
30 lines (28 loc) · 1.98 KB

FINETUNE.md

File metadata and controls

30 lines (28 loc) · 1.98 KB
Raw

Fine-tuning UM-MAE

A typical command To fine-tune Swin-T (recommended default) with single-node distributed training, run the following on 1 node with 8 GPUs each:

python -m torch.distributed.launch --nproc_per_node=8 main_finetune.py \
        --input_size 256 \
        --batch_size 64 \
        --accum_iter 2 \
        --model swin_tiny_256 \
        --finetune ./work_dirs/pretrain_mae_swin_tiny_256_mask_vmr025_200e//checkpoint-199.pth \
        --epochs 100 \
        --blr 5e-4 --layer_decay 0.85 \
        --weight_decay 0.05 --drop_path 0.1 --reprob 0.25 --mixup 0.8 --cutmix 1.0 \
        --dist_eval --data_path /path/to/ImageNet/ \
        --log_dir ./work_dirs/finetune_mae_swin_tiny_256_mask_vmr025_200e_100e \
        --output_dir ./work_dirs/finetune_mae_swin_tiny_256_mask_vmr025_200e_100e

Please modify your data_path /path/to/ImageNet/ and possibly the dataloader.

More detailed training script follows:

Models Pre-train Method Sampling Strategy Secondary Mask Ratio Encoder Ratio Finetune Epochs Finetune Command
ViT-B MAE RS -- 25% 100 make finetune_mae_vit_base_patch16_dec512d2b_200e_100e
ViT-B MAE UM 25% 25% 100 make finetune_mae_vit_base_patch16_dec512d2b_mask_vmr025_200e_100e
PVT-S SimMIM RS -- 100% 100 make finetune_simmim_pvt_small_256_200e_100e
PVT-S UM-MAE (ours) UM 25% 25% 100 make finetune_mae_pvt_small_256_mask_vmr025_200e_100e
Swin-T SimMIM RS -- 100% 100 make finetune_simmim_swin_tiny_256_200e_100e
Swin-T UM-MAE (ours) UM 25% 25% 100 make finetune_mae_swin_tiny_256_mask_vmr025_200e_100e
Swin-L SimMIM RS -- 100% 100 see official
Swin-L UM-MAE (ours) UM 25% 25% 100 make finetune_mae_swin_large_256_mask_vmr025_200e_100e