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Vision Transformer

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What is Vision Transformer?

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

How to Train
Vision Transformer
Vision Transformer
Training Notebook
View on GitHub
Vision Transformer
Video Guide
Vision Transformer

Training on Kaggle
Vision Transformer

Training on SageMaker

About the model

Here is an overview of the

Vision Transformer

model:
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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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Vision Transformer Annotation Format

Vision Transformer

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.
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Convert data between formats

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