vision-transformers-cifar10

Let's train vision transformers for cifar 10!

This is an unofficial and elementary implementation of An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

I use pytorch for implementation.

Updates

• Added ConvMixer implementation. Really simple! (2021/10)

• Added wandb train log to reproduce results. (2022/3)

• Added CaiT and ViT-small. (2022/3)

• Added SwinTransformers. (2022/3)

• Added MLP mixer. (2022/6)

• Changed default training settings for ViT.

Usage example

python train_cifar10.py # vit-patchsize-4

python train_cifar10.py --size 48 # vit-patchsize-4-imsize-48

python train_cifar10.py --patch 2 # vit-patchsize-2

python train_cifar10.py --net vit_small --n_epochs 400 # vit-small

python train_cifar10.py --net vit_timm # train with pretrained vit

python train_cifar10.py --net convmixer --n_epochs 400 # train with convmixer

python train_cifar10.py --net mlpmixer --n_epochs 500 --aug --lr 1e-3

python train_cifar10.py --net cait --n_epochs 200 # train with cait

python train_cifar10.py --net swin --n_epochs 400 # train with SwinTransformers

python train_cifar10.py --net res18 # resnet18+randaug

Results..

	Accuracy	Train Log
ViT patch=2	80%
ViT patch=4 Epoch@200	80%	Log
ViT patch=4 Epoch@500	88%	Log
ViT patch=8	30%
ViT small	80%
MLP mixer	88%
CaiT	80%
Swin-t	90%
ViT small (timm transfer)	97.5%
ViT base (timm transfer)	98.5%
ConvMixerTiny(no pretrain)	96.3%	Log
resnet18	93%
resnet18+randaug	95%	Log

Used in..

• Vision Transformer Pruning arxiv github