Transformers for Computer Vision

Explore how Transformer models, originally designed for natural language processing, are revolutionizing computer vision tasks through this comprehensive 3 hour 55 minute video playlist. Master the fundamentals of Transformers and self-attention mechanisms while learning to implement Vision Transformer (ViT) architecture for image classification and object detection. Discover how to combine Transformers with CNNs in hybrid models and apply advanced architectures like Swin Transformers that use hierarchical structures with shifted windows. Learn practical implementation techniques using PyTorch and TensorFlow, including transfer learning approaches for Vision Transformers and custom object detection using Detection Transformer (DETR). Gain hands-on experience with real-world projects covering image classification, object detection, and segmentation tasks while understanding training strategies and best practices for computer vision applications. Perfect for AI and machine learning enthusiasts, students, developers, and researchers seeking to understand modern vision models and apply Transformer technology to computer vision challenges.

Syllabus

Transformers for beginners | What are they and how do they work
Vision Transformers explained
Vision Transformer explained in detail | ViTs
Image Classification Using Vision Transformer | ViTs
Image Classification Using Vision Transformer | ViTs on Google Colab
Vision Transformer for Image Classification Using transfer learning
Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
Image Classification Using Swin Transformer
Object detection Using Detection Transformer (Detr) on custom dataset