Vision transformer

Vision_Transformer

Vision Transformer (ViT)

The Vision Transformer (ViT) is a type of artificial neural network architecture that applies transformer models, originally designed for natural language processing (NLP), to computer vision tasks. The ViT model was introduced by researchers at Google Research and has demonstrated state-of-the-art performance on various image recognition benchmarks.

Architecture

The Vision Transformer architecture leverages the self-attention mechanism of transformers to process image data. Unlike traditional convolutional neural networks (CNNs), which use convolutional layers to extract features from images, ViT divides an image into fixed-size patches and treats each patch as a token, similar to words in NLP.

Image Patching

An input image is divided into a grid of non-overlapping patches. Each patch is then flattened into a vector and linearly embedded into a lower-dimensional space. These embedded patches are combined with positional encodings to retain spatial information.

Transformer Encoder

The sequence of embedded patches is fed into a standard transformer encoder, which consists of multiple layers of multi-head self-attention and feed-forward neural networks. The self-attention mechanism allows the model to weigh the importance of different patches relative to each other, enabling it to capture global context.

Classification Head

For image classification tasks, a special classification token is prepended to the sequence of embedded patches. The output corresponding to this token is passed through a feed-forward network to produce the final class predictions.

Advantages

• **Scalability**: ViT models can be scaled up more easily than CNNs by increasing the number of transformer layers or the size of the patches.
• **Performance**: ViT has achieved competitive performance on several image classification benchmarks, such as ImageNet.
• **Transfer Learning**: Pre-trained ViT models can be fine-tuned on specific tasks, similar to pre-trained models in NLP.

Challenges

• **Data Efficiency**: ViT models typically require large amounts of training data to achieve optimal performance.
• **Computational Resources**: Training ViT models can be computationally intensive, requiring significant GPU resources.

Applications

Vision Transformers have been applied to various computer vision tasks, including:

• Image classification
• Object detection
• Semantic segmentation

See Also

• Transformer (machine learning model)
• Convolutional neural network
• ImageNet
• Google Research
• Natural language processing