Deploying Custom Vision Transformers (ViT) on iOS with CoreML
This article demonstrates how to convert a custom-trained Vision Transformer (ViT) model into a high-performance CoreML model, enabling fast, offline skin lesion classification on an iPhone.
Why it matters
This demonstrates how state-of-the-art AI models can be brought to the edge, empowering users with fast, private access to intelligent services.
Key Points
- 1Bridging the gap between state-of-the-art AI research and real-world mobile applications
- 2Leveraging Vision Framework, CoreML, and SwiftUI for privacy-focused, low-latency inference
- 3Transforming a PyTorch ViT model into a CoreML package for deployment on iOS devices
Details
The article outlines a workflow for taking a pre-trained ViT model in PyTorch, converting it to the CoreML format using coremltools, and then integrating it into a native iOS app built with SwiftUI and the Vision Framework. This allows for millisecond-latency, offline skin lesion classification directly on an iPhone, without relying on sluggish cloud APIs. The key steps involve tracing the PyTorch model with a dummy input, converting it to CoreML, and then deploying the .mlpackage model within the iOS app. By leveraging the iPhone's Neural Engine, the ViT model can run efficiently on-device, enabling a 'Privacy First' approach to mobile AI applications.
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