readme: ---
  library_name: keras
  ---
  
  ## Model description
  **This model is implementation of the distillation recipe proposed in DeiT.**   
  Visit Keras example on [Distilling Vision Transformers](https://keras.io/examples/vision/deit/).   
     
  Full credits to: [Sayak Paul](https://twitter.com/RisingSayak)   
     
  In the original Vision Transformers (ViT) paper (Dosovitskiy et al.), the authors concluded that to perform on par with Convolutional Neural Networks (CNNs), ViTs need to be pre-trained on larger datasets. The larger the better. This is mainly due to the lack of inductive biases in the ViT architecture -- unlike CNNs, they don't have layers that exploit locality.  
     
  Many groups have proposed different ways to deal with the problem of data-intensiveness of ViT training. One such way was shown in the Data-efficient image Transformers, (DeiT) paper (Touvron et al.). The authors introduced a distillation technique that is specific to transformer-based vision models. DeiT is among the first works to show that it's possible to train ViTs well without using larger datasets.
  
  ## Intended uses & limitations
  
  The model is trained for demonstrative purposes and does not guarantee the best results in production.   
  For better results, follow & optimize the [Keras example](https://keras.io/examples/vision/deit/) as per your need.
  
  ## Training and evaluation data
  
  The model is trained and evaluated on [TF Flowers dataset](https://www.tensorflow.org/datasets/catalog/tf_flowers)
  
  ## Training procedure
  
  Training procedure is followed exactly as from the [keras example](https://keras.io/examples/vision/deit/).   
  The batch size is however decreased to 16 from the original 256 for accomodating the model in a single V100 GPU memory.
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  
  | name | learning_rate | decay | beta_1 | beta_2 | epsilon | amsgrad | weight_decay | exclude_from_weight_decay | training_precision |
  |----|-------------|-----|------|------|-------|-------|------------|-------------------------|------------------|
  |AdamW|6.25000029685907e-05|0.0|0.8999999761581421|0.9990000128746033|1e-07|False|9.999999747378752e-05|None|float32|
  
   ## Model Plot
  
  <details>
  <summary>View Model Plot</summary>
  
  ![Model Image](./model.png)
  
  </details>