Image Models

Vision Transformer

Introduced by Dosovitskiy et al. in An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

The Vision Transformer, or ViT, is a model for image classification that employs a Transformer-like architecture over patches of the image. An image is split into fixed-size patches, each of them are then linearly embedded, position embeddings are added, and the resulting sequence of vectors is fed to a standard Transformer encoder. In order to perform classification, the standard approach of adding an extra learnable “classification token” to the sequence is used.

Source: An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Papers


Paper Code Results Date Stars

Tasks


Task Papers Share
Semantic Segmentation 82 9.03%
Image Classification 61 6.72%
Object Detection 34 3.74%
Self-Supervised Learning 29 3.19%
Decoder 25 2.75%
Image Segmentation 23 2.53%
Language Modelling 19 2.09%
Classification 17 1.87%
Retrieval 14 1.54%

Categories