Comprehensive Guide to Transformers for Computer Vision Engineers

Comprehensive Lecture on Transformers for Computer Vision

Table of Contents

  1. Introduction
  2. Transformer Theory
  3. Vision Transformers (ViT) and Applications
  4. Setup Instructions
  5. Hands-on Practice
  6. References

Introduction

This comprehensive lecture is designed for data scientists specializing in Computer Vision who want to learn about Transformer architectures. The content covers both theoretical foundations and practical applications, with a focus on Vision Transformers (ViT) and related models.

Vision Transformer Architecture
Figure 1: Vision Transformer (ViT) architecture showing how an image is processed through patch embedding, positional encoding, and transformer layers.

The lecture is structured to provide:

  • Theoretical understanding of transformer architecture and attention mechanisms
  • Overview of transformer applications in Computer Vision
  • Detailed setup instructions for different hardware environments (including options for laptops without GPUs)
  • Hands-on practice exercises with step-by-step solutions
  • References to further resources and research papers

All hands-on exercises are compatible with Google Colab, making them accessible even without dedicated GPU hardware.

Transformer Theory

Please refer to the transformer_theory.md file for a comprehensive explanation of transformer architecture, attention mechanisms, and their adaptation for computer vision tasks.

Key topics covered:

  • Self-attention mechanism
  • Scaled dot-product attention
  • Multi-head attention
  • Transformer encoder-decoder architecture
  • Positional encoding
  • Vision Transformer (ViT) architecture
  • Advanced transformer variants for computer vision

Vision Transformers (ViT) and Applications

Please refer to the transformer_applications_cv.md file for an in-depth exploration of Vision Transformers and their applications in computer vision.

Key topics covered:

  • Image classification with ViT
  • Object detection with transformer-based models
  • Semantic, instance, and panoptic segmentation
  • Medical imaging applications
  • Video understanding
  • 3D vision and point clouds
  • Multi-modal vision-language tasks
  • Latest advancements in Vision Transformers
  • Industry applications and real-world impact

Setup Instructions

Please refer to the setup_instructions.md file for detailed instructions on setting up your environment for working with Vision Transformers.

Key topics covered:

  • Google Colab setup (recommended for users without a GPU)
  • Local setup with CPU
  • Local setup with GPU
  • Memory optimization tips
  • Troubleshooting common issues

Hands-on Practice

Please refer to the hands_on_practice.md file for step-by-step exercises and solutions for working with Vision Transformers.

Exercises included:

  1. Image Classification with Pre-trained ViT
  2. Fine-tuning ViT on a Custom Dataset
  3. Attention Visualization in Vision Transformers
  4. Transfer Learning with ViT for Custom Image Classification
  5. Efficient Inference with Vision Transformers

Each exercise includes detailed code examples, explanations, and solution analysis.

References

For a complete list of references, please see the reference sections in the individual files:

Key resources include:

  1. Vaswani, A., et al. (2017). Attention is all you need. In Advances in neural information processing systems.
  2. Dosovitskiy, A., et al. (2021). An image is worth 16x16 words: Transformers for image recognition at scale. ICLR 2021.
  3. Liu, Z., et al. (2021). Swin transformer: Hierarchical vision transformer using shifted windows. ICCV 2021.
  4. Hugging Face Transformers documentation: https://huggingface.co/docs/transformers/
  5. Alammar, J. (2018). The Illustrated Transformer. https://jalammar.github.io/illustrated-transformer/