Learn to Build Computer Vision Apps for iOS | CoreML, AVFoundation, SwiftUI
This video breaks down the core principles for developing computer vision iOS apps, covering the iOS Vision stack and the challenges of image processing. We explore the importance of the entire pipeline, from image representation to object detection, for shipping a real-world CV app. This conceptual overview provides a strong foundation in pattern recognition and clean architecture for a robust user experience.
I’m walking through the core principles for building real time computer vision iOS apps in Swift, and why a computer vision app is a pipeline, not just a model. I cover the full flow from AVFoundation capture, to pre processing, inference with Core ML wrapped in Vision, and post processing and rendering in the UI. I call out hard parts like coordinate transforms, correct geometry, and drawing bounding boxes. I also emphasize performance, frame throttling, running inference off the main thread, and clean architecture with separate camera, inference, post processing, view model, and overlay renderer. I did not request any action from viewers.
Time Stamps:
00:00 Real time vision pipeline
01:59 Pipeline beats single model
07:23 How images are represented
10:12 Running live model inference
12:20 Mapping results to the screen
15:37 Speed for smooth real time
18:25 Clean architecture for apps
21:23 Shipping like a product
23:14 On device versus cloud