Swin Transformer: Hierarchical Vision Transformer using Shifted Windows

Learn about the *Swin Transformer* — a cutting-edge deep learning model that combines the strengths of *Transformers* and *Convolutional Neural Networks (CNNs)* for computer vision tasks.

In this video, we explain how Swin Transformers work, why they are powerful, and how they efficiently process large-scale images using **shifted windows**.

📌 *What You’ll Learn:*
✅ Introduction to *Swin Transformer architecture*
✅ How it bridges the gap between *Transformers and CNNs*
✅ The concept of *hierarchical vision Transformers*
✅ How *shifted windows* allow efficient processing of large images
✅ Applications in *image classification, object detection, and segmentation*

💡 *Who This Video is For:*
AI and computer vision enthusiasts
Students learning deep learning and modern vision architectures
Developers exploring Transformer-based models for computer vision
Anyone curious about state-of-the-art models like *Swin Transformer*

💬 *For Queries or Feedback:*
Comment below or email me at *aarohisingla1987@gmail.com*

🔔 Don’t forget to *like, share, and subscribe* for more tutorials on **AI, computer vision, and Transformers**!

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