+ Bare Metal Performance. The YuNet predictor is implemented with NatML, which takes advantage of hardware machine learning accelerators, like CoreML on iOS and macOS, NNAPI on Android, and DirectML on Windows. View the model performance statistics on NatML Hub.
+ Extremely Easy to Use. The YuNet predictor accepts an image and returns an array of rectangles, each corresponding to a detected face. See more on NatML Hub.
+ Cross Platform. The YuNet predictor supports Android, iOS, macOS, and Windows alike, allowing you to develop in the Editor and deploy to device in one seamless workflow.
+ Augmented Reality. This predictor is particularly suited for augmented reality applications, where it can recognize faces in the camera view.
+ Lightweight Package. This package contains the predictor scripts, whereas the ML model will be downloaded at runtime from NatML Hub and cached onto the device, reducing the app size significantly.
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