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Object detection with Vision Transformers
Object detection is a core task in computer vision, powering technologies from self-driving cars to real-time video surveillance. It involves detecting and localizing objects within an image, and recent advances in deep learning have made this task more accurate and efficient. One of the latest innovations driving object detection is the Vision Transformer (ViT), a model that has changed the landscape of image processing with its ability to capture global context better than traditional methods.
In this blog, we’ll explore Object Detection in detail, introduce the power of Vision Transformers, and then walk through a hands-on project where we’ll use ViTs for object detection. To make it more engaging, we’ll create an interactive interface that allows users to upload images and see real-time object detection results.
What You’ll Learn
- What Object Detection is and why it’s important.
- How Vision Transformers (ViTs) differ from traditional neural networks.
- Step-by-step implementation of object detection using ViTs with PyTorch.
- Build an interactive tool for object detection using
ipywidgets
.
Table of Contents
- Introduction to Object Detection
- What are Vision Transformers?
- Transformer Architecture Explained
- Setting Up the Project