Welcome to the image classification tutorial! 🌟 This guide will walk you through the fundamentals of training models to recognize objects in images using machine learning and deep learning techniques. Let's dive in!

What is Image Classification?

Image classification is a task where a model assigns a label to an entire image. For example, identifying whether an image contains a dog, cat, or bird.

image_classification

Key Steps in Image Classification

  1. Data Collection
    Gather a labeled dataset (e.g., CIFAR-10, ImageNet) or create your own.

    data_collection

  2. Model Architecture
    Use architectures like Convolutional_Neural_Network (CNN) or ResNet for feature extraction.

    convolutional_neural_network

  3. Training & Optimization
    Train the model with loss functions (e.g., cross-entropy) and optimize using techniques like stochastic_gradient_descent (SGD).

    stochastic_gradient_descent

  4. Evaluation
    Test the model's accuracy using metrics like precision, recall, and F1-score.

    evaluation_metrics

Practical Applications

  • Medical Imaging: Detecting tumors in X-rays.
  • Retail: Identifying products in inventory photos.
  • Autonomous Vehicles: Recognizing traffic signs.

For an in-depth look at image preprocessing techniques, check out our image_processing tutorial! 📚

Tools & Libraries

  • TensorFlow
  • PyTorch
  • Keras
  • OpenCV

Let me know if you'd like to explore image_segmentation next! 🎯