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Transformers for Computer Vision Applications

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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This course includes

36 Lessons

3 Projects

24 Playgrounds

8 Quizzes

Course Overview

What You'll Learn

Course Content

Recommendations

Course Overview

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. ...Show More

This is a comprehensive course on vision transformers and their use cases in computer vision. You’ll begin by exploring the rise...Show More

What You'll Learn

An understanding of transformers and attention mechanisms

Hands-on implementation of computer vision techniques with transformer models

The ability to apply transfer learning for image classification

A strong grasp of object detection and segmentation using transformers

An understanding of transformers and attention mechanisms

Course Content

Introduction

1 Lessons

Get familiar with transformers in computer vision, covering key concepts and architectures.

Introduction to the Course

Overview of Transformer Networks

14 Lessons

Grasp the fundamentals of transformer networks, attention mechanisms, and their impact on deep learning.

Introduction to Transformers

The Rise of Transformers

Inductive Bias in DNNs

Attention: General Deep Learning Idea

Attention in NLP

Is Attention All We Need?

Quiz: Attention and Inductive Bias

Self-Attention Mechanism

Self-Attention Matrix Equations

Multihead Attention

Encoder-Decoder Attention

Transformers Pros and Cons

Unsupervised and Self-Supervised Pretraining

Quiz: Transformers and Multihead Attention

Neural Machine Translation with a Transformer and Keras

Project

Transformers in Computer Vision

9 Lessons

Break apart the application of transformers, attention mechanisms, and the encoder-decoder pattern in computer vision.

Introduction to Transformers in Computer Vision

Encoder-Decoder Design Pattern

Convolutional Encoders

Self-Attention vs. Convolution

Quiz: Encoder-Decoder Architecture and Attention Mechanism in Computer Vision

Spatial vs. Channel vs. Temporal Attention

Local vs. Global Attention

Pros and Cons of Attention in CV

Quiz: Attention in Computer Vision

Transformers in Image Classification

3 Lessons

Grasp the fundamentals of ViT, DeiT, and Swin Transformers in image classification.

Image Classification with Vision Transformer (ViT and DeiT)

Shifter Window (Swin) Transformers

Quiz: Transformers in Image Classification

Fine-Tuning Vision Transformers for Image Classification

Project

Transformers in Object Detection

3 Lessons

Take a closer look at object detection methods, from traditional approaches to DEtection TRansformers (DETR).

Object Detection Methods Review

DEtection TRansformers (DETR)

Quiz: Transformers in Object Detection

Transformers in Semantic Segmentation

3 Lessons

Focus on innovative methods using ConvNets and transformers for semantic image segmentation.

Image Segmentation Using ConvNets

Image Segmentation Using Transformers

Quiz: Transformers in Semantic Segmentation

Spatio-Temporal Transformers

2 Lessons

Build on the versatility of spatio-temporal transformers for advanced video analysis tasks.

Spatio-Temporal Transformers

Quiz: Spatio-Temporal Transformers

Object Detection with Vision Transformers

Project

11.

Wrap Up

1 Lessons

Step through key concepts of transformers in computer vision and their practical applications.

Conclusion

Course Author

Ammar Mohanna

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Frequently Asked Questions

Do vision transformers use position encoding?

ViTs use position encoding to capture spatial relationships in image patches. They process images as sequences and lack CNNs’ inherent spatial awareness.

How to retrieve position encodings?

Access the position encoding layer in the ViT model (e.g., model.positional_encoding in PyTorch) to extract and analyze the spatial information embedded.

What is the vision transformer backbone?

A Vision Transformer (ViT) backbone is the core architecture for feature extraction in image processing tasks. It splits images into patches, embeds them, and processes them using self-attention mechanisms, making it a versatile choice for classification, detection, and segmentation tasks.