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

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DETR

Transformers

DETR2

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. temporal attention, you’ll grasp nuances in applying transformer architectures to visual data. The course also explores spatio-temporal transformers, bridging the gap between static images and dynamic data.

By the end of the course, you’ll be equipped with the knowledge and skills to leverage transformer networks across diverse applications in deep learning and artificial intelligence.

Transformers for Computer Vision Applications

What’s the primary focus of semantic segmentation?

What’s the primary focus of semantic segmentation?

Increasing spatial dimensions during decoding

Preserving fine-grained details during downsampling

Addressing vanishing gradients in shallow networks

What’s the role of skip connections in U-Net architecture for semantic segmentation?

What’s the role of skip connections in U-Net architecture for semantic segmentation?

Lack of inductive bias for local spatial features

What’s a key challenge associated with using transformer decoders in image segmentation?

What’s a key challenge associated with using transformer decoders in image segmentation?

Lack of inductive bias for capturing local spatial features

What’s a potential limitation of using transformer decoders in semantic segmentation?

What’s a potential limitation of using transformer decoders in semantic segmentation?

Test your understanding of transformer applications in image segmentation, addressing challenges, and understanding models like SETR and Segmenter.

Introduction

Overview of Transformer Networks

Neural Machine Translation with a Transformer and Keras

Transformers in Computer Vision

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 Semantic Segmentation