Image Classification Tutorial 📸

Image classification is a fundamental task in computer vision where the goal is to assign a label to an image based on its content. This tutorial will guide you through the basics, tools, and applications of image classification.

Key Concepts

• What is Image Classification?
  It involves identifying objects, scenes, or patterns within images using machine learning models. For example, a model might classify an image as "dog" or "cat".

  

• How Does It Work?
  Models process pixel data through layers to extract features, then use these features to predict the most likely class.

  

Popular Methods

• Convolutional Neural Networks (CNNs)
  The backbone of modern image classification. CNNs use filters to detect spatial hierarchies in images.

  

• Transfer Learning
  Leverage pre-trained models (e.g., ResNet, VGG) to improve performance with less data.

  

Tools & Frameworks

• TensorFlow
  A popular open-source library for building image classification models.

  

• PyTorch
  Known for its flexibility and dynamic computation graphs, ideal for research.

  

Applications

• Healthcare
  Diagnosing diseases from medical imaging (e.g., X-rays, MRIs).

  

• Autonomous Vehicles
  Recognizing traffic signs, pedestrians, and obstacles in real-time.

  

For deeper insights, explore our Deep Learning Fundamentals tutorial. Happy coding! 🚀