# 08 - Vision Transformers: Architecture and Training

Complete guide to Vision Transformers: Architecture and Training: architecture, practical implementation and best practices for developers and technical teams.

## What you'll learn

- ViT architecture: patch embedding, positional encoding, visual self-attention
- Complete from-scratch implementation with PyTorch
- Differences between ViT-B/16, ViT-L/32, DeiT, Swin Transformer
- Fine-tuning a pre-trained ViT on a custom dataset
- Data augmentation techniques for ViT (MixUp, CutMix, RandAugment)

*This article is part of the **Deep Learning Avanzato** series on federicocalo.dev.*

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## Read the full article

The complete article (18 min read) with code examples, diagrams, and practical exercises is available here:

**➡️ [08 - Vision Transformers: Architecture and Training](https://federicocalo.dev/en/blog/vision-transformers-architecture-training)**

`https://federicocalo.dev/en/blog/vision-transformers-architecture-training`

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*By [Federico Calò](https://federicocalo.dev) — Software Developer & Technical Writer*