This visual guide introduces Vision Transformers (ViTs), which excel in image classification using the transformer architecture from NLP. Steps like patch creation, embedding, attention score calculation, multi-head attention, and residual connections are explained. ViTs are supervised models trained on image-label datasets, predicting class probabilities through a neural network, minimizing loss with backpropagation. The guide aims to demystify ViTs, offering clarity on their functionality and training process. For further understanding, a Colab Notebook and external resources are provided. While controversial, ViTs’ success in image classification is unexpected, showcasing the potential of transformer architectures beyond NLP.
https://blog.mdturp.ch/posts/2024-04-05-visual_guide_to_vision_transformer.html