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

cv_updated.tar.gz

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

Lack of a direct word-to-word correspondence between input and output sentences

Difficulty in training recurrent neural networks

What’s the main challenge associated with unaligned data in neural machine translation (NMT)?

What’s the main challenge associated with unaligned data in neural machine translation (NMT)?

To eliminate attention scores below a certain threshold

To produce a weight distribution across all input states

To ensure that the decoder generates the correct output token

In the context of attention mechanisms, what’s the purpose of normalizing attention scores using softmax?

In the context of attention mechanisms, what’s the purpose of normalizing attention scores using softmax?

It measures the similarity between query and values.

In the context of self-attention, what’s the role of the query vector?

In the context of self-attention, what’s the role of the query vector?

By calculating the dot product between the query and keys matrices

By generating various representations for each word

By forming matrices $Q$ (Query) and $K$ (Key) and multiplying them

How can we visualize the self-attention operation in matrix form?

How can we visualize the self-attention operation in matrix form?

Test your understanding of concepts and challenges in neural machine translation and attention mechanisms with this quiz.

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 and Multihead Attention