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.