AI Vision Library Documentation

Welcome to the AI Vision Library documentation! This library provides advanced computer vision functionalities specifically tailored for robotic applications, including autonomous drones, robotic arms, and medical robots.

This guide walks you through the installation, configuration, and use of the library, offering best practices and advanced features. Whether you’re a developer integrating AI into robotics or a researcher exploring new applications, this documentation will help you maximize the potential of the AI Vision Library.

Note

This documentation is intended for developers, engineers, and researchers familiar with Python and machine learning concepts.

Contents:

• Introduction
  • Welcome to the AI Vision Library Documentation!
• Detect Objects
  • Function Signature
  • Example Usage
  • Detailed Example: Displaying Detected Objects with Bounding Boxes
  • Advanced Usage: Optimizing Detection Performance
  • Common Errors and Troubleshooting
  • Conclusion
  • What to Do Next:
• Recognize Objects
  • Function Signature
  • Example Usage
  • Detailed Example: Displaying Recognized Objects with Bounding Boxes
  • Advanced Usage: Custom Object Recognition Models
  • Optimization Tips
  • Common Errors and Troubleshooting
  • Conclusion
• Train Model
  • Function Signature
  • Parameters
  • Return Value
  • Example Usage
  • Detailed Example: Fine-Tuning a Pre-Trained Model
  • Advanced Usage: Customizing the Training Process
  • Optimization Tips
  • Common Errors and Troubleshooting
  • Conclusion
• Error Handling and Troubleshooting
  • Common Errors
  • 1. `CUDA not available` or `CUDA out of memory`
  • 2. Invalid image format or Image loading failed
  • 3. Model not found or Failed to load model
  • 4. Unexpected keyword argument or TypeError
  • Debugging Tips
  • Handling Specific Exceptions
  • Reporting Issues
  • Conclusion
• Best Practices
  • 1. Performance Optimization
  • 2. Code Quality and Maintainability
  • 3. Security Considerations
  • 4. Robustness and Error Handling
  • 5. Testing and Validation
• Sample Code and Tutorials
  • Example 1: Real-Time Object Detection with Live Visualization
  • Example 2: Training a Custom Object Recognition Model with Data Augmentation
  • Example 3: Integrating with ROS for Autonomous Drone Navigation
  • Example 4: Video Analytics with Multi-Object Tracking

Changelog

Version 1.0.0

• Initial release of the AI Vision Library.

• Features include object detection, object recognition, and model training.

Version 1.1.0

• Added support for edge devices.

• Improved real-time processing speed.

• Fixed minor bugs in the object recognition module.

Indices and tables

• Index

• Module Index

• Search Page

Further Reading

If you’re interested in exploring more advanced topics, consider the following sections:

• Integration with ROS: Learn how to integrate the AI Vision Library with the Robot Operating System (ROS) for real-time robotics applications.

• Custom Model Training: Dive deeper into training custom models with the AI Vision Library.

• Performance Optimization: Discover tips and techniques to optimize the performance of your AI models.

For related projects and community discussions, visit our [GitHub repository](https://github.com/your-repo) or [community forum](https://community.yourproject.com).

Contributing

We welcome contributions to the AI Vision Library! Whether you’re fixing a bug, adding a feature, or improving the documentation, your help is appreciated.

To contribute: 1. Fork the repository on GitHub. 2. Create a new branch with your changes. 3. Submit a pull request with a detailed description of your changes.

For more information on contributing, please see our [contributing guidelines](https://github.com/your-repo/contributing).

Additional Resources

• [ROS Documentation](https://docs.ros.org) - Comprehensive guide to the Robot Operating System.

• [TensorFlow](https://www.tensorflow.org) - Explore deep learning frameworks used in the AI Vision Library.

• [OpenCV](https://opencv.org) - Learn more about image processing techniques.