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DeiT

Abstract

Recently, neural networks purely based on attention were shown to address image understanding tasks such as image classification. However, these visual transformers are pre-trained with hundreds of millions of images using an expensive infrastructure, thereby limiting their adoption. In this work, we produce a competitive convolution-free transformer by training on Imagenet only. We train them on a single computer in less than 3 days. Our reference vision transformer (86M parameters) achieves top-1 accuracy of 83.1% (single-crop evaluation) on ImageNet with no external data. More importantly, we introduce a teacher-student strategy specific to transformers. It relies on a distillation token ensuring that the student learns from the teacher through attention. We show the interest of this token-based distillation, especially when using a convnet as a teacher. This leads us to report results competitive with convnets for both Imagenet (where we obtain up to 85.2% accuracy) and when transferring to other tasks. We share our code and models.

How to use it?

from mmpretrain import inference_model

predict = inference_model('deit-tiny_4xb256_in1k', 'demo/bird.JPEG')
print(predict['pred_class'])
print(predict['pred_score'])

Models and results

Image Classification on ImageNet-1k

Model

Pretrain

Params (M)

Flops (G)

Top-1 (%)

Top-5 (%)

Config

Download

deit-tiny_4xb256_in1k

From scratch

5.72

1.26

74.50

92.24

config

model | log

deit-tiny-distilled_3rdparty_in1k*

From scratch

5.91

1.27

74.51

91.90

config

model

deit-small_4xb256_in1k

From scratch

22.05

4.61

80.69

95.06

config

model | log

deit-small-distilled_3rdparty_in1k*

From scratch

22.44

4.63

81.17

95.40

config

model

deit-base_16xb64_in1k

From scratch

86.57

17.58

81.76

95.81

config

model | log

deit-base_3rdparty_in1k*

From scratch

86.57

17.58

81.79

95.59

config

model

deit-base-distilled_3rdparty_in1k*

From scratch

87.34

17.67

83.33

96.49

config

model

deit-base_224px-pre_3rdparty_in1k-384px*

224px

86.86

55.54

83.04

96.31

config

model

deit-base-distilled_224px-pre_3rdparty_in1k-384px*

224px

87.63

55.65

85.55

97.35

config

model

Models with * are converted from the official repo. The config files of these models are only for inference. We haven’t reproduce the training results.

Warning

MMPretrain doesn’t support training the distilled version DeiT. And we provide distilled version checkpoints for inference only.

Citation

@InProceedings{pmlr-v139-touvron21a,
  title =     {Training data-efficient image transformers & distillation through attention},
  author =    {Touvron, Hugo and Cord, Matthieu and Douze, Matthijs and Massa, Francisco and Sablayrolles, Alexandre and Jegou, Herve},
  booktitle = {International Conference on Machine Learning},
  pages =     {10347--10357},
  year =      {2021},
  volume =    {139},
  month =     {July}
}
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