Welcome to the exciting world of integrating machine learning (ML) into iOS applications! This comprehensive guide will walk you through the use of Core ML and ML Kit (TensorFlow Lite) to incorporate robust ML models into your iOS applications. We aim to make the process user-friendly and enlightening, ensuring that you can create amazing demos and projects efficiently.
Contents
- Machine Learning Framework for iOS
- Flow of Model When Using Core ML
- Flow of Model When Using Create ML
- Baseline Projects
- Application Projects
- Performance
- Measure Module
- Implements
- See Also
Machine Learning Framework for iOS
Several important frameworks for integrating ML into iOS exist, such as:
Flow of Model When Using Core ML
The process of using machine learning frameworks is akin to preparing a delicious dish. Imagine you’re a chef: you start with a model (your recipe) that was created in TensorFlow (your raw ingredients). Before you can serve this dish, you need to convert it into a compatible format for each mobile ML framework, just like preparing ingredients for different cooking methods.
Once your model is ready, running inference is like cooking. You need to handle preprocessing (getting your ingredients ready) and postprocessing (plating your dish) manually. This adds a layer of individuality to your serving, much like the chef putting their unique spin on a dish.
Flow of Model When Using Create ML
Using Create ML has a similar workflow, where you prepare your data and define the model through an intuitive interface.
Baseline Projects
Current Projects (DONE)
- Using built-in model with Core ML
- Using built-in on-device model with ML Kit
- Using a custom model for Vision with Core ML and ML Kit
- Object Detection with Core ML
Upcoming Projects (TODO)
- Object Detection with ML Kit
- Using built-in cloud model on ML Kit – Landmark recognition
- Custom model for NLP with Core ML and ML Kit
- Custom model for Audio with Core ML and ML Kit (Audio recognition, Speech recognition)
Application Projects
One of the standout projects is the dont-be-turtle-ios, which focuses on word recognition through Core ML and ML Kit, allowing character detection and word recognition in real-time!
Performance
Performance is crucial for a seamless user experience. Understanding the Execution Time and its components such as Inference Time and Postprocessing Time can help you optimize your applications:
| Image Models | Inference Time (ms) | Execution Time (ms) | FPS |
|---|---|---|---|
| ImageClassification-CoreML | 40 | 40 | 23 |
| MobileNet-MLKit | 120 | 130 | 6 |
Measure Module
This module helps in measuring latency time for inference and execution and provides the FPS information, crucial for monitoring performance.
Implements
Ensure you have proper unit testing and bunch testing in place for each model to guarantee high-quality outputs.
See Also
Troubleshooting
If you encounter issues while setting up or using machine learning frameworks, here are some troubleshooting ideas:
- Check model compatibility with Core ML or ML Kit.
- Verify data preprocessing methods used before inference.
- Use logging to track down any inference errors.
For more insights, updates, or to collaborate on AI development projects, stay connected with [fxis.ai](https://fxis.ai).
Our Vision
At [fxis.ai](https://fxis.ai), we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.
Now, you’re equipped to dive into the incredible world of machine learning on iOS. Happy coding!
