Mastering Machine Learning in iOS with SwiftUI: From Basics to Advanced
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- Discussions
- Introduction
- Chapter 1: Introduction to Machine Learning and Its Relationship to AI
- Chapter 2: Machine Learning Techniques
- Chapter 3: Apple’s Machine Learning Ecosystem
- Chapter 4: Create ML and Core ML
- Chapter 5: CreateML and Transfer Learning
- Chapter 6: Getting Started with Create ML
- Chapter 7: VisionFeaturePrint_Screen
- Chapter 8: Create ML Project Interface and Model Training
- Chapter 9: Improving Model Performance
- Chapter 10: Vision Framework with Core ML
- Chapter 11: Using Machine Learning Model with Core ML
- Chapter 13: Using Existing ML Model in Apps
- Chapter 12: Steps to Train Machine Learning Models
- Chapter 14: Object Detection in Create ML - Data Prep
- Chapter 15: Object Detection in Create ML - Model Training
- Chapter 16: Object Detection in Create ML - Using Model
- Chapter 17: Style Transfer Technique
- Chapter 18: Using Style Transfer Technique in SwiftUI App
- Chapter 19: Hand Pose Classification
- Chapter 20: Rock-Paper-Scissor App
- Chapter 21: Action Classification Model using Create ML
- Chapter 22: Using Action Classifier Model in an App
- Chapter 23: Activity Classification using Create ML
- Chapter 24: Using Activity Classification Model in SwiftUI App
- Chapter 25: Collecting Activity Motion Data using iPhone
- Chapter 26: Sound Classification in Create ML
- Chapter 27: Animal Sound Classification in SwiftUI
- Chapter 28: Built-in Sound Classification with Sound Analysis framework
- Chapter 29: Text Classification in Create ML
- Chapter 30: Word Tagging in Create ML
- Chapter 31: Tabular Classification
- Chapter 32: Tabular Regression
- Chapter 33: Recommendations Model using Create ML
- Chapter 34: Understanding Training Data
- Chapter 35: Thank You for Reading!

Chapter 5: CreateML and Transfer Learning

Create ML simplifies the process of building machine learning models by offering pre-configured templates and leveraging Apple’s technologies like Metal for GPU-accelerated training. It allows users to train custom models using their own datasets and outputs models in the Core ML format for seamless integration into Apple apps.

Create ML often relies on transfer learning for certain tasks, particularly in image classification and object detection. Transfer learning enables the framework to use pre-trained models as a starting point, reducing the amount of data and training time required for building a new model.

Training a machine learning model from scratch is a challenging, costly, and time-consuming process. To begin, you require a substantial dataset of images. For instance, MobileNetV2 is a convolutional neural network (CNN) architecture model specifically designed for optimal performance on mobile and embedded devices. It underwent training on approximately 1.2 million images from the ImageNet dataset, enabling it to categorize objects into 1000 distinct classes. This is where Transfer learning comes into play.

What Is Transfer Learning?

Transfer learning is a machine learning technique that involves training a new model using an existing pre-trained model. The concept is to leverage the knowledge gained from the pre-trained model and adapt it to perform a specific task.

The process involves using a Pre-trained Model, which is initially trained on a large, general dataset (such as ImageNetfor images). Subsequently, it undergoes Fine-Tuning to adapt the pre-trained model to a specific task by employing a smaller, task-specific dataset. Transfer learning involves extracting valuable features from the pre-trained model to train a new classifier. It heavily relies on the features it already possesses from the pre-trained model.

Transfer learning enables faster training by leveraging existing knowledge. This approach reduces the need for extensive data, allowing custom models to perform effectively even with limited datasets.

Imagine you’re building a model to classify dog breeds. You’ll need a pre-trained Convolutional Neural Network (CNN) model that has been trained on a vast dataset of general animal images. Subsequently, you’ll fine-tune the model using a small dataset of labeled dog breed photographs to adapt it specifically for your task.

Benefits from Transfer Learning in Create ML

Reduced Training Time: Transfer learning allows Create ML to leverage pre-trained models, significantly reducing the time required to train new models from scratch.
Lower Data Requirements: By using transfer learning, Create ML can achieve high accuracy with smaller labeled datasets, as it builds on existing knowledge.
Improved Performance: Pre-trained models have already learned useful features, which can enhance the performance of new models even with limited data.
Ease of Use: Transfer learning simplifies the model creation process, making it more accessible for non-experts or beginners with minimal coding experience.
Resource Efficiency: It reduces computational resources and costs, as training starts from a pre-trained model rather than random initialization.
Customization: Create ML can fine-tune pre-trained models for specific tasks, allowing developers to create highly specialized models without extensive training.
Faster Deployment: With transfer learning, models can be deployed more quickly, accelerating the development cycle for iOS/macOS apps.

Conclusion

In this chapter, we explored how transfer learning enhances the learning process in Create ML, enabling you to build your own custom models using your own dataset.