Overview
Vision Transformer (ViT) adapts the transformer architecture, originally designed for NLP, to computer vision. It splits images into fixed-size patches, treating them as tokens analogous to words in NLP. ViT models are pretrained, requiring less computational resources compared to convolutional neural networks. The pretrained models can then be fine-tuned for various downstream image classification tasks. The architecture involves embedding these image patches, passing them through transformer encoder layers with multi-head self-attention, and then using a classification head to predict image labels. The ViTConfig class allows customization of the model architecture, controlling parameters such as hidden layer sizes, attention heads, and dropout probabilities. Use cases include image classification, object detection (with modifications), and semantic segmentation. The model can be easily integrated using the Hugging Face Transformers library.
Common tasks