What are best practices for Vision in Swift?

When working with Vision in Swift, consider the following best practices to ensure optimal performance and accuracy:

• Optimize Image Input: Ensure that the images used for processing are of appropriate size and quality to enhance performance.
• Use Core ML Models: Integrate Core ML models with Vision for advanced tasks such as object detection and facial recognition.
• Asynchronous Processing: Utilize asynchronous methods to prevent blocking the main thread and maintain a smooth user experience.
• Batch Processing: Process images in batches when possible to improve efficiency and reduce overhead.
• Error Handling: Implement robust error handling to handle potential issues during image analysis gracefully.
• Test on Real Devices: Always test Vision applications on real hardware to assess performance and accuracy in real-world scenarios.

By following these practices, developers can leverage the power of Vision to create responsive and intelligent applications.