How Swipe Cleaner Processes Photos Entirely On-Device Using Apple's Core ML

Swipe Cleaner, built by the Nomos team, performs all photo analysis locally on the user's device rather than uploading images to remote servers. The app uses Apple's Core ML framework and the iPhone's Neural Engine to run lightweight ML models — kept under 50MB through quantization and architecture optimization. To handle large photo libraries without performance issues, the app processes images in batches of 50 and caches results locally via Core Data. The developers argue that on-device processing offers a structural privacy guarantee, since no photo upload pipeline was ever built into the app. The trade-off is that model improvements require full app updates, unlike cloud-based systems that can refresh models silently.