Passio Mobile AI Data vs Vision Detector

Mobile AI Data is the companion app of the Mobile AI platform. Collect data for your AI projects and test the recognition results of your AI models in a fully-integrated mobile app. Passio's Mobile-AI self service platform makes it easy for companies to add vertical-specific computer vision into their apps. Mobile AI allows users to train their own image classification models and package them into an SDK for on device image classification.

Vision Detector is an AI image analysis tool that supports CoreML machine learning models, multi-modal Large Language Models, and on-device Apple Intelligence. Choose the mode that best fits your use case. You can choose one of the following image input sources: - Live video by built-in camera - Still image by the built-in camera - Photo Library - Files (iCloud, Google Drive, Dropbox, etc) For live video inputs, continuous inference is performed on the camera feed. However, the frame rate and other parameters depend on the device. # Notification Center Vision Detector can call Notification Center for predefined conditions. Broadcasting notifications send notification to your all Apple devices signed in with the same Apple Account. ## CoreML Mode Vision Detector locally executes CoreML models using Apple’s Vision framework. Before using this mode, prepare a machine learning model in the CoreML format (.mlmodel or .mlpackage) using Create ML. Import ML models via iClpoud or AirDrop. Vision Detector supports the following types of CoreML models: - Image classification - Object detection - Style transfer ## Vision LLM Mode Vision Detector can use multi-modal large language model (LLMs). Define what to do with prompt as following samples: - If the image is screen shot, answer the name software - Call notification when you see a cat - Count number of persons in the image, and notify when it changes ## Apple Intelligence mode Available with iOS 27 on selected devices (iPhone 17 Pro, iPhone Air, etc). The images are completely processed on your device locally with your prompt.