Master Computer Vision with Transformers: A Deep Dive into Cutting-Edge AI

What you will learn:

Transformer networks and their architecture
State-of-the-art Transformer architectures for computer vision tasks
Practical application of ViT, DETR, Swin Transformer, and other models
Advanced attention mechanisms in deep learning
Inductive bias and model assumptions in deep learning
Applying Transformers to NLP and machine translation
Various attention types in computer vision (spatial, channel, temporal)
Image classification, object detection, and segmentation with Transformers
Video processing with spatio-temporal Transformers
Utilizing pre-trained models with the Hugging Face library

Description

Revolutionize your understanding of Computer Vision with our comprehensive course. Explore the transformative power of Transformer networks, moving beyond their NLP dominance to master their application in image classification, object detection, segmentation, and video processing. We'll demystify attention mechanisms, delve into state-of-the-art architectures like Vision Transformers (ViT), Detection Transformers (DETR), and Swin Transformers, and equip you with practical skills using the Hugging Face library. Uncover the underlying principles, from inductive biases to advanced attention techniques (spatial, channel, temporal), and build a strong foundation in this rapidly evolving field. This course is your gateway to becoming a proficient computer vision engineer.

We begin with a foundational understanding of transformer networks, exploring their origins in NLP and the core concepts of self-attention mechanisms. You'll learn how these powerful models generalize to the 2D spatial domain of images, allowing us to understand convolutional operations through a new lens. We will discuss the nuances of different attention types (spatial, channel, and temporal) and their impact on model performance.

The course then dives deep into specific computer vision applications. Learn the intricacies of Vision Transformer (ViT), Shifted Window Transformer (SWIN), Detection Transformer (DETR), and Segmentation Transformer (SETR), along with their practical implementation using the Hugging Face library. We’ll also cover advanced topics like spatio-temporal transformers for video processing and multi-task learning setups. By the end, you'll be confident in applying these cutting-edge techniques to real-world problems.

Curriculum

Introduction

This introductory section sets the stage for the course, providing a concise overview of the topics covered and the course structure. The 'Introduction' lecture lays the groundwork for the journey ahead.

Overview of Transformer Networks

This module forms the foundation for understanding Transformers. Lectures cover the rise of Transformers, inductive bias in deep learning models, the fundamental concept of attention mechanisms, and their application in NLP. You'll also learn about self-attention mechanisms, multi-head attention, encoder-decoder attention, the strengths and weaknesses of transformer architectures, and the importance of unsupervised pre-training techniques, touching upon LLMs such as BERT and GPT.

Transformers in Computer Vision

This section bridges the gap between NLP and CV. Lectures cover the encoder-decoder pattern, convolutional encoders, comparing self-attention and convolution, different attention types (spatial, channel, temporal), the generalization of self-attention equations, local vs. global attention, and finally, a discussion of the pros and cons of attention in computer vision.

Transformers in Image Classification

This module focuses on image classification using transformers. Lectures cover the Vision Transformer (ViT) and Shifted Window Transformers (SWIN), detailing their architectures and capabilities in classifying images effectively.

Transformers in Object Detection

This module introduces object detection with transformers. It begins with a review of object detection using convolutional neural networks (YOLO), then moves into the core of Detection Transformers (DETR) and culminates with a comparative analysis of DETR and YOLOv5 highlighting their distinct strengths and applications.

Transformers in Semantic Segmentation

Explore semantic image segmentation using transformers. The module begins with a review of conventional CNN-based segmentation methods, then demonstrates how transformers can be effectively applied to this task.

Spatio-Temporal Transformers

This advanced module explores the application of transformers to video processing and introduces spatio-temporal transformers, demonstrating their use in moving object detection and multi-task learning setups.

Huggingface Vision Transformers

This practical module guides you through the use of Hugging Face's vision transformer library. Lectures cover an overview of Hugging Face pipelines, practical application of vision transformers, and a demonstration using the Gradio interface.

Conclusion

The final section summarizes the key learnings and provides concluding remarks on the course content.

Material

This section provides access to supplementary course materials, including slides.

Deal Source: real.discount