Image Classification Tools

User guide

  • Installation
    • Install from PyPI
    • Requirements
    • Optional dependencies
    • Install from source
    • Verify installation
    • GPU support
    • Next steps
  • Quick start guide
    • Basic workflow
    • Example: MNIST classification
      • 1. Prepare data
      • 2. Define model
      • 3. Train model
      • 4. Evaluate model
      • 5. Visualize results
    • Working with custom datasets
    • Convolutional neural networks
    • Data augmentation
    • Hyperparameter optimization
    • Next steps

API reference

  • API reference
    • Core modules
      • Data loading
        • Classes
        • Overview
        • Example usage
      • Training
        • train_one_epoch()
        • evaluate()
        • train_model()
        • Functions
        • Overview
        • Example usage
      • Evaluation
        • evaluate_model()
        • Functions
        • Overview
        • Example usage
      • Plotting
        • Overview
        • Example usage
      • Hyperparameter optimization
        • Overview
        • Key components
        • Example usage
        • Multi-GPU optimization
        • Search space format
        • Notes
    • Module overview
    • Complete module index

Project links

  • GitHub Repository
  • PyPI Package
  • Issue Tracker
Image Classification Tools
  • Overview: module code

All modules for which code is available

  • image_classification_tools.pytorch.data
  • image_classification_tools.pytorch.evaluation
  • image_classification_tools.pytorch.training

© Copyright 2026, Dr. George Perdrizet.

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