Learn about transformer networks, self-attention, multi-head attention, and spatiotemporal transformers in this course, focusing on their applications in computer vision and deep learning.

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DETR

Transformers

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This is a comprehensive course on vision transformers and their use cases in computer vision. You’ll begin by exploring the rise of transformers and attention mechanisms and their role in deep neural networks. 
You’ll gain insights into self-attention mechanisms, multi-head attention, and the pros and cons of transformers building a strong foundation. Next, you’ll discover how transformers reshape image analysis. Comparing self-attention with convolutional encoders and understanding spatial vs. channel vs. temporal attention, you’ll grasp nuances in applying transformer architectures to visual data. 

The course also explores spatiotemporal transformers, bridging the gap between static images and dynamic data. After completing this course, you’ll have the knowledge and skills to leverage transformer networks across diverse applications in deep learning and artificial intelligence.

Transformers for Computer Vision Applications

What’s a common component in traditional image classification architectures like CNN?

What’s a common component in traditional image classification architectures like CNN?

What challenge arises when replacing the encoder's convolutional block with an attention mechanism?

What challenge arises when replacing the encoder’s convolutional block with an attention mechanism?

What does the vision transformer (ViT) use as tokens for processing images?

What does the vision transformer (ViT) use as tokens for processing images?

Ability to distill knowledge without extensive pretraining

Exclusively local self-attention mechanism



What sets DeiT apart from traditional transformers like ViT?

What sets DeiT apart from traditional transformers like ViT?

What innovative strategies does Swin employ to address the quadratic complexity challenge in image classification?

What innovative strategies does Swin employ to address the quadratic complexity challenge in image classification?

Test your understanding of vision transformers and their innovative architectures.

Introduction

Overview of Transformer Networks

Neural Machine Translation with a Transformer and Keras

Transformers in Computer Vision

Vision Transformer for Image Classification

Transformers in Image Classification

Fine-Tuning Vision Transformers for Image Classification

Transformers in Object Detection

Transformers in Semantic Segmentation

Spatio-Temporal Transformers

Object Detection with Vision Transformers

Wrap Up

Quiz: Transformers in Image Classification