Adaptive Split-Fusion Transformer
Update:
27/04/2022
- The paper is posted on arXiv and the code is released.
1. Requirements
timm==0.3.4
torch==1.8.0
torchvision
pyyaml
Data Preparation
ImageNet with following folder structure:
│imagenet/
├──train/
│ ├── n01440764
│ │ ├── n01440764_10026.JPEG
│ │ ├── n01440764_10027.JPEG
│ │ ├── ......
│ ├── ......
├──val/
│ ├── n01440764
│ │ ├── ILSVRC2012_val_00000293.JPEG
│ │ ├── ILSVRC2012_val_00002138.JPEG
│ │ ├── ......
│ ├── ......
2. Pretrained Models
The pretrained models will be released soon.
3. Training
Train ASF-former-S with 8 GPUs:
CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 ./distributed_train.sh 8 path/to/data --model ASF_former_S -b 64 --lr 5e-4 --weight-decay .05 --amp --img-size 224
Train ASF-former-B with 8 GPUs:
CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 ./distributed_train.sh 8 path/to/data --model ASF_former_B -b 64 --lr 5e-4 --weight-decay .065 --amp --img-size 224
4. Evaluation
Evaluate the ASF-former-S model:
CUDA_VISIBLE_DEVICES=0 python main.py path/to/data --model ASF_former_S -b 100 --eval_checkpoint path/to/checkpoint
Evaluate the ASF-former-B model:
CUDA_VISIBLE_DEVICES=0 python main.py path/to/data --model ASF_former_B -b 100 --eval_checkpoint path/to/checkpoint
5. Transfer Learning
Transfer ASF-former-S to CIFAR-10:
CUDA_VISIBLE_DEVICES=0,1 python transfer_learning.py --lr 0.025 --b 64 --num-classes 10 --img-size 224 --transfer-learning True --transfer-model path/to/model
Transfer ASF-former-B to CIFAR-10:
CUDA_VISIBLE_DEVICES=0,1 python transfer_learning.py --lr 0.025 --b 64 --num-classes 10 --img-size 224 --transfer-learning True --transfer-model path/to/model --model ASF_former_B
Transfer ASF-former-S to CIFAR-100:
CUDA_VISIBLE_DEVICES=0,1 python transfer_learning.py --lr 0.05 --b 64 --num-classes 100 --img-size 224 --transfer-learning True --transfer-model path/to/model --dataset cifar100
Transfer ASF-former-B to CIFAR-100:
CUDA_VISIBLE_DEVICES=0,1 python transfer_learning.py --lr 0.05 --b 64 --num-classes 100 --img-size 224 --transfer-learning True --transfer-model path/to/model --dataset cifar100 --model ASF_former_B
Citation
@article{Su2022AdaptiveST,
title={Adaptive Split-Fusion Transformer},
author={Zixuan Su and Hao Zhang and Jingjing Chen and Lei Pang and Chong-Wah Ngo and Yu-Gang Jiang},
journal={ArXiv},
year={2022},
volume={abs/2204.12196}
}
Our codes are based on T2T-ViT.
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