This guy is so fast, I was creating a similar tutorial after learning from Cassva comp on Kaggle. As always an excellent tutorial on VIT and Probabilistic Bayesian Neural Networks!!
New code walkthrough on http://keras.io : the Vision Transformer model. Perform image classification *without convolutions* by applying a Transformer to vector encodings of image patches. Created by Khalid Salama.https://keras.io/examples/vision/image_classification_with_vision_transformer/ …
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Now that we are talking about Transformers, can we have a Performer Layer?
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C'est un réel bonheur d'être un rookie dans le monde du Machine Learning, et de voir tous ces nouveaux exemples de codes qui sont postés sur http://Keras.io . Merci infiniment pour ce que vous avez créée. PS: J'attend la v2 de votre livre pour l'acheter
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Why:"Unlike in the ViT paper, which prepends a learnable embedding to the sequence of encoded patches to serve as the image representation, all the outputs of the final Transformer block are reshaped with Flatten and used as the image representation input to the classifier head"?
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