The article also provides a step-by-step walkthrough of how a single data point is processed by a Vision Transformer, from the initial focus on a single image, through the creation of patches, flattening of patches, creation of patch embeddings, addition of a classification token, and addition of positional embedding vectors. The process continues with the transformer input, creation of QKV (Query, Key, Value) vectors, calculation of attention scores, aggregation of contextual information, and application of multi-head attention. The process concludes with the identification of the classification token output and prediction of classification probabilities. The guide includes a link to a Colab Notebook for further understanding and invites readers to reach out with any questions or feedback.
Key takeaways:
- Vision Transformers (ViTs) are a class of deep learning models that apply the transformer architecture, originally designed for natural language processing, to image data and have achieved state-of-the-art performance on image classification tasks.
- The process of preparing an image for a Vision Transformer involves dividing the image into patches, flattening these patches into vectors, creating patch embeddings, and adding a classification token and positional embedding vectors.
- The transformer part of the Vision Transformer involves creating Query, Key, and Value vectors, calculating attention scores, aggregating contextual information, and using residual connections and a feed forward network. This process is repeated several times.
- The final step of the Vision Transformer process is to use the classification token output and a fully connected neural network to predict the classification probabilities of the input image. The model is trained using backpropagation and gradient descent to minimize a loss function.