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Tensorflow Keras implementation of : Learning to tokenize in Vision Transformers

The full credit goes to: Aritra Roy Gosthipaty, Sayak Paul

Short description:

ViT and other Transformer based architectures break down images into patches. As we increase the resolution of the images, the number of patches increases as well. To tackle this, Ryoo et al. introduced a new module called TokenLearner which can help reduce the number of patches used. The full paper can be found here

Model and Dataset used

The Dataset used here is CIFAR-10. The model used here is a mini ViT model with the TokenLearner module.

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

Hyperparameters Value
name AdamW
learning_rate 0.0010000000474974513
decay 0.0
beta_1 0.8999999761581421
beta_2 0.9990000128746033
epsilon 1e-07
amsgrad False
weight_decay 9.999999747378752e-05
exclude_from_weight_decay None
training_precision float32

Training Metrics

After 20 Epocs, the test accuracy of the model is 55.9% and the Top 5 test accuracy is 95.06%

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