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.
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Added ConvMixer implementation. Really simple! (2021/10)
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Added wandb train log to reproduce results. (2022/3)
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Added CaiT and ViT-small. (2022/3)
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Added SwinTransformers. (2022/3)
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Added MLP mixer. (2022/6)
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Changed default training settings for ViT.
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 --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
| 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 |