Instructions to use keras-io/learning_to_tokenize_in_ViT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- TF-Keras
How to use keras-io/learning_to_tokenize_in_ViT with TF-Keras:
# Note: 'keras<3.x' or 'tf_keras' must be installed (legacy) # See https://github.com/keras-team/tf-keras for more details. from huggingface_hub import from_pretrained_keras model = from_pretrained_keras("keras-io/learning_to_tokenize_in_ViT") - Notebooks
- Google Colab
- Kaggle
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Browse files
README.md
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## Short description:
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ViT and other Transformer based architectures
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## Model and Dataset used
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## Short description:
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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](https://openreview.net/forum?id=z-l1kpDXs88)
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## Model and Dataset used
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