Models

What is Vision Transformer?

The Vision Transformer leverages powerful natural language processing embeddings (BERT) and applies them to images.

About the model

Here is an overview of the

Vision Transformer

model:

Date of Release
Model Type Classification
Architecture
Framework Used PyTorch
Annotation Format
Stars on GitHub 6200+

What is the Vision Transformer?

The Vision Transformer leverages powerful natural language processing embeddings (BERT) and applies them to images. When providing images to the model, each image is split into patches that are linearly embedded after which position embeddings are added and this is sequentially fed to the transformer encoder. Finally, to classify the image, a [CLS] token is inserted at the beginning of the image sequence.

Vision Transformer Architecture

Vision Transformer Architecture

Vision Transformer Performance

Applying transformers to image classification tasks achieves state-of-the-art performance on a variety of datasets, rivaling traditional convolutional neural networks.

ViT Performance


Images in Courtesy of Google Research

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Model Performance

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Deploy Vision Transformer to production

Roboflow offers a range of SDKs with which you can deploy your model to production.

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Vision Transformer Annotation Format

Vision Transformer

uses the

uses the

annotation format. If your annotation is in a different format, you can use Roboflow's annotation conversion tools to get your data into the right format.

Convert data between formats

Label data automatically with Vision Transformer

You can automatically label a dataset using

Vision Transformer

with help from Autodistill, an open source package for training computer vision models. You can label a folder of images automatically with only a few lines of code. Below, see our tutorials that demonstrate how to use

Vision Transformer

to train a computer vision model.

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