Update README.md

README.md

@@ -3,19 +3,29 @@ library_name: keras
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 ## Model description
-
-More information needed
+**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
 
-More information needed
+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
 
-More information needed
+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: