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Update README.md

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remove the dummy arguments in the example scripts
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Saining Xie 2021-09-30 10:41:37 -07:00 committed by GitHub
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@ -91,7 +91,7 @@ CIFAR-10/CIFAR-100 datasets will be downloaded automatically.
#### CIFAR-10
```
python -u -m torch.distributed.launch --nproc_per_node=8 --use_env main.py \
--batch-size 128 --output_dir [your output dir path] --epochs 100 --lr 3e-4 --weight-decay 0.1 --eval-freq 10 \
--batch-size 128 --output_dir [your output dir path] --epochs 100 --lr 3e-4 --weight-decay 0.1 \
--no-pin-mem --warmup-epochs 3 --data-set cifar10 --data-path [cifar-10 data path] --no-repeated-aug \
--resume [your pretrain checkpoint file] \
--reprob 0.0 --drop-path 0.1 --mixup 0.8 --cutmix 1
@ -100,7 +100,7 @@ python -u -m torch.distributed.launch --nproc_per_node=8 --use_env main.py \
#### CIFAR-100
```
python -u -m torch.distributed.launch --nproc_per_node=8 --use_env main.py \
--batch-size 128 --output_dir [your output dir path] --epochs 100 --lr 3e-4 --weight-decay 0.1 --eval-freq 10 \
--batch-size 128 --output_dir [your output dir path] --epochs 100 --lr 3e-4 --weight-decay 0.1 \
--no-pin-mem --warmup-epochs 3 --data-set cifar100 --data-path [cifar-100 data path] --no-repeated-aug \
--resume [your pretrain checkpoint file] \
--reprob 0.0 --drop-path 0.1 --mixup 0.5 --cutmix 1
@ -109,7 +109,7 @@ python -u -m torch.distributed.launch --nproc_per_node=8 --use_env main.py \
#### Flowers
```
python -u -m torch.distributed.launch --nproc_per_node=8 --use_env main.py \
--batch-size 128 --output_dir [your output dir path] --epochs 100 --lr 3e-4 --weight-decay 0.3 --eval-freq 10 \
--batch-size 128 --output_dir [your output dir path] --epochs 100 --lr 3e-4 --weight-decay 0.3 \
--no-pin-mem --warmup-epochs 3 --data-set flowers --data-path [oxford-flowers data path] --no-repeated-aug \
--resume [your pretrain checkpoint file] \
--reprob 0.25 --drop-path 0.1 --mixup 0 --cutmix 0
@ -118,7 +118,7 @@ python -u -m torch.distributed.launch --nproc_per_node=8 --use_env main.py \
#### Pets
```
python -u -m torch.distributed.launch --nproc_per_node=8 --use_env main.py \
--batch-size 128 --output_dir [your output dir path] --epochs 100 --lr 3e-4 --weight-decay 0.1 --eval-freq 10 \
--batch-size 128 --output_dir [your output dir path] --epochs 100 --lr 3e-4 --weight-decay 0.1 \
--no-pin-mem --warmup-epochs 3 --data-set pets --data-path [oxford-pets data path] --no-repeated-aug \
--resume [your pretrain checkpoint file] \
--reprob 0 --drop-path 0 --mixup 0.8 --cutmix 0