added author and url links

README.md

@@ -7,6 +7,8 @@ tags:
 
 # Compact Convolutional Transformers
 
+Based on the _Compact Convolutional Transformers_ example on [keras.io](https://keras.io/examples/vision/cct/) created by [Sayak Paul](https://twitter.com/RisingSayak).
+
 ## Model description
 
 As discussed in the [Vision Transformers (ViT)](https://arxiv.org/abs/2010.11929) paper, a Transformer-based architecture for vision typically requires a larger dataset than usual, as well as a longer pre-training schedule. ImageNet-1k (which has about a million images) is considered to fall under the medium-sized data regime with respect to ViTs. This is primarily because, unlike CNNs, ViTs (or a typical Transformer-based architecture) do not have well-informed inductive biases (such as convolutions for processing images). This begs the question: can't we combine the benefits of convolution and the benefits of Transformers in a single network architecture? These benefits include parameter-efficiency, and self-attention to process long-range and global dependencies (interactions between different regions in an image).
@@ -20,7 +22,7 @@ In [Escaping the Big Data Paradigm with Compact Transformers](https://arxiv.org/
 
 ## Training and evaluation data
 
-The model is trained using the CIFAR-10 dataset. 10% of the data is used for validation.
+The model is trained using the [CIFAR-10 dataset](https://www.cs.toronto.edu/~kriz/cifar.html).
 
 ## Training procedure
 
@@ -39,4 +41,8 @@ The following hyperparameters were used during training:
 
 ![Model Image](./model.png)
 
-</details>
+</details>
+
+<center>
+Model reproduced by <a href="https://github.com/EdAbati" target="_blank">Edoardo Abati</a>
+</center>