A Visual Guide to Vision Transformers
This is a visual guide to Vision Transformers (ViTs), a class of deep learning models that have achieved state-of-the-art performance on image classification tasks. Vision Transformers apply the transformer architecture, originally designed for natural language processing (NLP), to image data. This guide will walk you through the key components of Vision Transformers in a scroll story format, using visualizations and simple explanations to help you understand how these models work and how the flow of the data through the model looks like.
Nice! A small piece of feedback: I would have the dimensions mentioned in the text also annotated on the diagram. It wasn't exactly clear how the input data was flattened for example.
That kind of scroll is OK-ish for a background parallax effect, or maybe some pretty fade-in/out effects while elements scroll into view (without changing their relative position in the page).
When it interferes with the main functionality of the page, namely reading the content, they break accessibility, distract over understanding the difficult topic, make the content brittle against changes in the platform (different browsers or future standard updates), and as others pointed out make it difficult or impossible to use alternative presentations.
With most comments commenting on the presentation and not on the content, I think it makes clear that it detracts from the experience more than helps.
To be honest, I actually really like the visual delivery here. It's especially helpful for understanding what's going on with computer vision problems. Please make more!
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Entirely plausible this is intended for someone more "mathmatical" than myself but appreciate the work regardless.
When it interferes with the main functionality of the page, namely reading the content, they break accessibility, distract over understanding the difficult topic, make the content brittle against changes in the platform (different browsers or future standard updates), and as others pointed out make it difficult or impossible to use alternative presentations.
With most comments commenting on the presentation and not on the content, I think it makes clear that it detracts from the experience more than helps.
[1] https://drive.google.com/file/d/12uHo9QIfS-jBpVTs3lmQ3BEpxhD...
It helps to think of kqv as a form of look up.