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 <img src="./images/vit.gif" width="500px"></img>
 
+## Table of Contents
+
+- [Vision Transformer - Pytorch](#vision-transformer---pytorch)
+- [Install](#install)
+- [Usage](#usage)
+- [Parameters](#parameters)
+- [Distillation](#distillation)
+- [Deep ViT](#deep-vit)
+- [CaiT](#cait)
+- [Token-to-Token ViT](#token-to-token-vit)
+- [CCT](#cct)
+- [Cross ViT](#cross-vit)
+- [PiT](#pit)
+- [LeViT](#levit)
+- [CvT](#cvt)
+- [Twins SVT](#twins-svt)
+- [RegionViT](#regionvit)
+- [NesT](#nest)
+- [Masked Autoencoder](#masked-autoencoder)
+- [Masked Patch Prediction](#masked-patch-prediction)
+- [Dino](#dino)
+- [Accessing Attention](#accessing-attention)
+- [Research Ideas](#research-ideas)
+  * [Efficient Attention](#efficient-attention)
+  * [Combining with other Transformer improvements](#combining-with-other-transformer-improvements)
+- [FAQ](#faq)
+- [Resources](#resources)
+- [Citations](#citations)
+
 ## Vision Transformer - Pytorch
 
 Implementation of <a href="https://openreview.net/pdf?id=YicbFdNTTy">Vision Transformer</a>, a simple way to achieve SOTA in vision classification with only a single transformer encoder, in Pytorch. Significance is further explained in <a href="https://www.youtube.com/watch?v=TrdevFK_am4">Yannic Kilcher's</a> video. There's really not much to code here, but may as well lay it out for everyone so we expedite the attention revolution.