RealTime AI Camera
A free iPhone app that identifies 601 different objects in real time, fully offline. YOLOv8 with the complete Open Images V7 class set. No network, no account, no ads.
🍴 0 forks
📱 iPhone
🔒 100% offline
💝 Free forever
What the app does
🎯 Object Detection
YOLOv8 with all 601 object classes from Open Images V7. Every standard iPhone detection app caps at the 80-class COCO set. This one recognizes 7.5× more categories — musical instruments by type, kitchen appliances, rare animals, scientific instruments, the works.
📝 On-Device OCR
English optical character recognition using Apple’s Vision framework. Point the camera at a sign, label, menu, or document — it reads the text without sending a frame anywhere.
🌎 Offline Translation
Spanish → English translation using a rule-based engine + dictionary. No cloud translation service, no Google Translate API call. Works in airplane mode.
📏 LiDAR Distance
On Pro iPhone models, per-object depth measurement using the built-in LiDAR scanner. Every detected object gets a distance overlay so the app can tell you how far away everything actually is.
📸 App Screenshots
Performance
Average 10 FPS across supported iPhone models. Optimizations include CoreML with Metal acceleration, Neural Engine utilization on A12+ chips, smart thermal and battery management, and adaptive frame-rate based on device capability.
Compatibility
Privacy-first by design
🔒 Works Offline
Put the phone in airplane mode. Every feature — detection, OCR, translation, LiDAR depth — still works. The ML runs on-device end to end.
🚫 No Tracking
No analytics SDKs. No user identifiers. No usage telemetry. The app doesn’t know who you are and neither do we.
🖥️ No Servers
There is no backend. The app never makes an outbound network request to any server, ours or anyone else’s.
💝 Free, No Ads
Not a trial. Not a freemium tier. No ads, no in-app purchases, no subscriptions. Free as in actually free.
The build story
Getting 601-class YOLO to run on an iPhone at 10 FPS wasn’t a weekend project. The hard parts were the PyTorch → CoreML conversion (some ops don’t translate cleanly and silently produce garbage), hallucination tuning across the extra 521 classes, responsive layout across every iPhone form factor from SE to 17 Pro Max, and the memory-bandwidth bottleneck in the camera pipeline — zero-copy plumbing from AVCaptureSession straight through to the model was what actually got the frame rate up.
Full write-up on the engineering: This Is What a Robot Can See Now.
Try it now
Free on the App Store. Source on GitHub. Model weights on HuggingFace.