Reference Codes

20220317: add DeiT based on facebook github, ViT on cifar10 (no pretrained dataset): 78.86 % acc vs DeiT on cifar10 (no pretrained dataset): 80.16 % acc

1. https://github.com/kentaroy47/vision-transformers-cifar10
2. https://github.com/FrancescoSaverioZuppichini/ViT
3. https://github.com/lucidrains/vit-pytorch
4. https://github.com/facebookresearch/deit

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)

Usage

python train_cifar10.py --lr 1e-4 --aug --n_epochs 200 # vit-patchsize-4

python train_cifar10.py --patch 2 --lr 1e-4 --aug --n_epochs 200 # vit-patchsize-2

python train_cifar10.py --net vit_timm --lr 1e-4 # train with pretrained vit

python train_cifar10.py --net convmixer --aug --n_epochs 200 # train with convmixer

python train_cifar10.py --net res18 # resnet18

python train_cifar10.py --net res18 --aug --n_epochs 200 # resnet18+randaug

Results..

	Accuracy	Train Log
ViT patch=2	80%
ViT patch=4	80%	Log
ViT patch=8	30%
ViT small (timm transfer)	97.5%
ViT base (timm transfer)	98.5%
ConvMixerTiny(no pretrain)	96.3%
resnet18	93%
resnet18+randaug	95%	Log

Used in..

• Vision Transformer Pruning arxiv github