ViT (Vision Transformer)

What is the Vision Transformer?

The Vision Transformer (ViT) is a deep learning architecture that applies the Transformer model, originally designed for natural language processing tasks, to computer vision problems. Instead of using convolutional neural networks (CNNs), which have traditionally been the go-to architecture for image classification tasks, the Vision Transformer divides an input image into a fixed number of non-overlapping patches and linearly embeds them as input tokens for the Transformer model. ViT has achieved state-of-the-art performance on a variety of computer vision benchmarks, demonstrating the versatility and effectiveness of the Transformer architecture.

What does the Vision Transformer do?

The Vision Transformer performs the following tasks:

  • Image patching: ViT divides an input image into non-overlapping patches, each of which serves as an input token for the Transformer model.
  • Token embedding: ViT linearly embeds each image patch, creating a sequence of flat vectors that can be processed by the Transformer.
  • Transformer processing: ViT applies the standard Transformer architecture, including self-attention and feed-forward layers, to the sequence of patch embeddings.
  • Classification: ViT uses a classification head attached to the output of the Transformer model to predict the class labels for the input image.

Some benefits of using the Vision Transformer

The Vision Transformer offers several benefits for computer vision tasks:

  • Scalability: ViT can be easily scaled up by increasing the number of layers, hidden units, or self-attention heads in the Transformer architecture, enabling it to handle larger and more complex datasets.

  • Transfer learning: ViT has demonstrated strong transfer learning capabilities, allowing pretrained models to be fine-tuned on smaller datasets with fewer labeled examples.

  • State-of-the-art performance: ViT has achieved state-of-the-art performance on various computer vision benchmarks, surpassing traditional CNN-based approaches.

More resources to learn more about the Vision Transformer

To learn more about the Vision Transformer and its applications, you can explore the following resources: