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Vision Transformers

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Mayank Pratap Singh

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Description

Vizuara AI Labs newsletter article by Mayank Pratap Singh on understanding and fine-tuning Vision Transformers for image classification. The URL is one post with theory sections plus a hands-on transfer-learning walkthrough.

It compares ViT with CNNs, then covers patch embeddings, positional encodings, encoder-only classification, benefits and drawbacks, and real-world uses. The practical half includes dataset setup, pretrained loading, freezing the backbone, training the head, inference checks, and confusion-matrix evaluation.

Aimed at ML practitioners moving from CNNs into transformer-based vision. Comfort with Python training loops and pretrained models is expected.

Free to read on the Vizuara newsletter site, with subscribe prompts around the article. Follow sections from conceptual intro through the fine-tuning steps.

Useful when you need both the architectural story of ViT and a concrete fine-tuning checklist in one place.