Evaluate ViT-Base in a single GPU (`${IMAGENET_DIR}` is a directory containing `{train, val}` sets of ImageNet):
```
python main_finetune.py --eval --resume mae_finetuned_vit_base.pth --model vit_base_patch16 --batch_size 16 --data_path ${IMAGENET_DIR}
```
This should give:
```
* Acc@1 83.664 Acc@5 96.530 loss 0.731
```
Evaluate ViT-Large:
```
python main_finetune.py --eval --resume mae_finetuned_vit_large.pth --model vit_large_patch16 --batch_size 16 --data_path ${IMAGENET_DIR}
```
This should give:
```
* Acc@1 85.952 Acc@5 97.570 loss 0.646
```
Evaluate ViT-Huge:
```
python main_finetune.py --eval --resume mae_finetuned_vit_huge.pth --model vit_huge_patch14 --batch_size 16 --data_path ${IMAGENET_DIR}
```
This should give:
```
* Acc@1 86.928 Acc@5 98.088 loss 0.584
```
### Fine-tuning
Get our pre-trained checkpoints from [here](https://github.com/fairinternal/mae/#pre-trained-checkpoints).
To fine-tune with **multi-node distributed training**, run the following on 4 nodes with 8 GPUs each:
```
python submitit_finetune.py \
--job_dir ${JOB_DIR} \
--nodes 4 \
--batch_size 32 \
--model vit_base_patch16 \
--finetune ${PRETRAIN_CHKPT} \
--epochs 100 \
--blr 5e-4 --layer_decay 0.65 \
--weight_decay 0.05 --drop_path 0.1 --reprob 0.25 --mixup 0.8 --cutmix 1.0 \
--dist_eval --data_path ${IMAGENET_DIR}
```
- Install submitit (`pip install submitit`) first.
- Here the effective batch size is 32 (`batch_size` per gpu) * 4 (`nodes`) * 8 (gpus per node) = 1024.
- `blr` is the base learning rate. The actual `lr` is computed by the [linear scaling rule](https://arxiv.org/abs/1706.02677): `lr` = `blr` * effective batch size / 256.
- We have run 4 trials with different random seeds. The resutls are 83.63, 83.66, 83.52, 83.46 (mean 83.57 and std 0.08).
- Training time is ~7h11m in 32 V100 GPUs.
Script for ViT-Large:
```
python submitit_finetune.py \
--job_dir ${JOB_DIR} \
--nodes 4 --use_volta32 \
--batch_size 32 \
--model vit_large_patch16 \
--finetune ${PRETRAIN_CHKPT} \
--epochs 50 \
--blr 1e-3 --layer_decay 0.75 \
--weight_decay 0.05 --drop_path 0.2 --reprob 0.25 --mixup 0.8 --cutmix 1.0 \
--dist_eval --data_path ${IMAGENET_DIR}
```
- We have run 4 trials with different random seeds. The resutls are 85.95, 85.87, 85.76, 85.88 (mean 85.87 and std 0.07).
- Training time is ~8h52m in 32 V100 GPUs.
Script for ViT-Huge:
```
python submitit_finetune.py \
--job_dir ${JOB_DIR} \
--nodes 8 --use_volta32 \
--batch_size 16 \
--model vit_huge_patch14 \
--finetune ${PRETRAIN_CHKPT} \
--epochs 50 \
--blr 1e-3 --layer_decay 0.75 \
--weight_decay 0.05 --drop_path 0.3 --reprob 0.25 --mixup 0.8 --cutmix 1.0 \
--dist_eval --data_path ${IMAGENET_DIR}
```
- Training time is ~13h9m in 64 V100 GPUs.
To fine-tune our pre-trained ViT-Base with **single-node training**, run the following on 1 node with 8 GPUs:
```
OMP_NUM_THREADS=1 python -m torch.distributed.launch --nproc_per_node=8 main_finetune.py \
--accum_iter 4 \
--batch_size 32 \
--model vit_base_patch16 \
--finetune ${PRETRAIN_CHKPT} \
--epochs 100 \
--blr 5e-4 --layer_decay 0.65 \
--weight_decay 0.05 --drop_path 0.1 --mixup 0.8 --cutmix 1.0 --reprob 0.25 \
--dist_eval --data_path ${IMAGENET_DIR}
```
- Here the effective batch size is 32 (`batch_size` per gpu) * 4 (`accum_iter`) * 8 (gpus) = 1024. `--accum_iter 4` simulates 4 nodes.
#### Notes
- The [pre-trained models we provide](https://github.com/fairinternal/mae/#pre-trained-checkpoints) are trained with *normalized* pixels `--norm_pix_loss` (1600 epochs, Table 3 in paper). The fine-tuning hyper-parameters are slightly different from the default baseline using *unnormalized* pixels.
- The original MAE implementation was in TensorFlow+TPU with no explicit mixed precision. This re-implementation is in PyTorch+GPU with automatic mixed precision (`torch.cuda.amp`). We have observed different numerical behavior between the two platforms. In this repo, we use `--global_pool` for fine-tuning; using `--cls_token` performs similarly, but there is a chance of producing NaN when fine-tuning ViT-Huge in GPUs. We did not observe this issue in TPUs. Turning off amp could solve this issue, but is slower.
- Here we use RandErase following DeiT: `--reprob 0.25`. Its effect is smaller than random variance.
### Linear Probing
Run the following on 4 nodes with 8 GPUs each:
```
python submitit_linprobe.py \
--job_dir ${JOB_DIR} \
--nodes 4 \
--batch_size 512 \
--model vit_base_patch16 --cls_token \
--finetune ${PRETRAIN_CHKPT} \
--epochs 90 \
--blr 0.1 \
--weight_decay 0.0 \
--dist_eval --data_path ${IMAGENET_DIR}
```
- Here the effective batch size is 512 (`batch_size` per gpu) * 4 (`nodes`) * 8 (gpus per node) = 16384.
- `blr` is the base learning rate. The actual `lr` is computed by the [linear scaling rule](https://arxiv.org/abs/1706.02677): `lr` = `blr` * effective batch size / 256.
- Training time is ~2h20m for 90 epochs in 32 V100 GPUs.
- To run single-node training, follow the instruction in fine-tuning.
To train ViT-Large or ViT-Huge, set `--model vit_large_patch16` or `--model vit_huge_patch14`. It is sufficient to train 50 epochs `--epochs 50`.
This PT/GPU code produces *better* results for ViT-L/H (see the table below). This is likely caused by the system difference between TF and PT.