Transformers for Computer Vision Applications

This course covers transformer networks in computer vision, from basics to advanced applications, focusing on attention mechanisms and Python libraries.

Advanced

36 Lessons

Certificate of Completion

This course covers transformer networks in computer vision, from basics to advanced applications, focusing on attention mechanisms and Python libraries.

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This course includes

3 Projects

24 Playgrounds

8 Quizzes

This course includes

3 Projects

24 Playgrounds

8 Quizzes

Course Overview

This is a comprehensive course on transformer networks 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, multihead 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

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

What You'll Learn

An understanding of transformers and attention mechanisms

Course Content

Introduction

This course offers a comprehensive exploration of transformers in computer vision.

Introduction to the Course

Overview of Transformer Networks

This chapter focuses on the transformers, attention mechanisms, self, multihead, encoder-decoder attention, and unsupervised/self-supervised pretraining

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

This chapter focuses on transformers in computer vision, covering encoder-decoder patterns and attention types and comparing self-attention with convolution.

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

This chapter focuses on image classification with vision transformers (ViT, DeiT) and shifter window (Swin) transformers.

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

In this chapter, you’ll cover methods used for object detection, focusing on DEtection TRansformers (DETR).

Object Detection Methods Review DEtection TRansformers (DETR)Quiz: Transformers in Object Detection

Transformers in Semantic Segmentation

3 Lessons

This chapter focuses on image segmentation techniques using ConvNets and transformers, highlighting their differences and applications.

Spatio-Temporal Transformers

2 Lessons

In this chapter, you’ll cover spatio-temporal transformers for video action recognition using Python, focusing on video classification tasks.

Object Detection with Vision Transformers

Project

Wrap Up

1 Lesson

This chapter summarizes key takeaways, covering attention mechanisms, transformers in NLP and computer vision, and extending to video analysis.

Course Author

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Emma Bostian 🐞

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