- Vision Transformers (ViTs) use the transformer architecture, originally designed for natural language processing, to process image data. An image is divided into fixed-size patches, which are flattened and linearly embedded into vectors.
- These vectors, along with positional encodings, are passed through multiple transformer layers. Each layer uses self-attention mechanisms to capture relationships between patches and feed-forward networks to process the features. The output of the final layer is passed to a classification head for prediction.
- ViTs excel at capturing global context in images due to their ability to focus on relevant regions across the entire input.
These are the datasets used in this Vision-Transformer Model for binary image classification: