Vision Transformers Explained + Fine-Tuning in Python

Explore the groundbreaking Vision Transformer (ViT) model in this comprehensive tutorial video. Dive into the intuition behind ViT's functionality, understanding how it bridges the gap between vision and language processing in machine learning. Learn about attention mechanisms, image patch embeddings, and the key components that make ViT effective. Follow along with a hands-on Python implementation using the Hugging Face transformers library for image classification tasks. Gain insights into setting up the environment, initializing the ViT Feature Extractor, configuring the Hugging Face Trainer, and evaluating model performance. Perfect for those interested in cutting-edge developments in computer vision and natural language processing.

Intro
In this video
What are transformers and attention?
Attention explained simply
Attention used in CNNs
Transformers and attention
What vision transformer ViT does differently
Images to patch embeddings
1. Building image patches
2. Linear projection
3. Learnable class embedding
4. Adding positional embeddings
ViT implementation in python with Hugging Face
Packages, dataset, and Colab GPU
Initialize Hugging Face ViT Feature Extractor
Hugging Face Trainer setup
Training and CUDA device error
Evaluation and classification predictions with ViT
Final thoughts