Transformers in Computer Vision - English version

🧠 Dive into Transformers in Computer Vision with Coursat.ai & Dr. Ahmad ElSallab 🚀

Course Title: Transformers in Computer Vision - English Version

Unlock the Power of Vision Transformers!

Course Introduction:

Transformers: A Brief History and Impact 📚

• Since their breakthrough in NLP in 2017, Transformer Networks have revolutionized the field of machine learning.
• These models are now the backbone for almost all NLP tasks, and they're expanding their horizons into Computer Vision (CV).

The Advent of Vision Transformers:

• Although slightly late to the CV party compared to CNNs, Vision Transformers (ViTs) and other related models have been making significant strides since 2020.
• They're rapidly transforming how we approach tasks like image classification, object detection, and segmentation.

Core Concepts & Architecture:

Attention Mechanisms: The Heart of Transformers 🧩

• We'll start by exploring the attention mechanism that lies at the core of transformer networks.
• Understanding this will help us appreciate the power and flexibility of transformers in both NLP and CV tasks.

Pros and Cons: A Balanced View 🏫

• By examining the advantages and limitations of transformer architectures, we'll gain insight into when and how to effectively apply them.
• We'll touch upon Large Scale Language Models (LLMs) like BERT and GPT to understand their impact on NLP.

Transformers in the 2D World of Images 🖼️

Extending Attention Beyond Text:

• We'll extend the attention mechanism into the spatial domain of images, exploring how transformers handle visual data differently from text.
• The encoder-decoder architecture will be demystified, highlighting its relevance in CV tasks.

Attention Types and Their Roles:

• From channel to spatial attention, we'll dissect local vs. global attention and other key concepts that make transformers versatile function approximators.

Transformers for Computer Vision Tasks:

From Classification to Segmentation:

• In the next three modules, we'll delve into the specific transformer models designed for CV challenges:
  • Vision Transformer (ViT): The Google-developed model that's setting new standards in image classification.
  • Shifter Window Transformer (SWIN): A model from Microsoft that addresses the limitations of ViTs.
  • Detection Transformer (DETR): A Facebook AI Research innovation that revolutionizes object detection tasks.
  • Segmentation Transformer (SETR): Tackling image segmentation with impressive results.
• We'll explore these and more, understanding their unique approaches to CV problems.

Transformers in Video Processing:

• We'll also cover Spatio-Temporal Transformers and their applications, particularly in detecting moving objects within video sequences.

Multi-Task Learning Setup:

• We'll introduce the concept of multi-task learning and how transformer models can be adapted to handle multiple tasks simultaneously.

Practical Applications with Huggingface Library:

Applying Pre-Trained Models in Real-World Scenarios: 💻

• Finally, we'll show you how to leverage the powerful Huggingface library and its Pipeline interface to implement pre-trained transformer models in your own projects.
• Whether for research or practical applications, you'll be equipped with the knowledge to apply these models effectively.

Join us on this transformative journey into the future of computer vision with Coursat.ai and Dr. Ahmad ElSallab. 🌟

This course is designed for those who wish to stay ahead of the curve in AI and machine learning, especially in the rapidly evolving field of computer vision. Whether you're a data scientist, AI researcher, or a student looking to deepen your understanding of transformers, this course will provide you with a comprehensive and practical toolkit to navigate and excel in the world of CV.

Enroll now and be part of the transformational wave in AI! 🚀🌊