import Fritz
let poseModel = FritzVisionHumanPoseModelFast()
let image = FritzVisionImage(buffer: sampleBuffer)
guard let result = try? poseModel.predict(image) else { return }
image.draw(poses: result.poses())
The Swift code sample here illustrates how simple it can be to use Pose Estimation in your app. Use the links below to access additional information about Fritz AI Studio and documentation that will help you get started.
Discover Studio2D Coordinates of predicted keypoints are provided for each skeleton detected.
All predictions / model inferences are made completely on-device.
No internet connection is required to interpret images or video.
No internet dependency means super-fast performance.
Fast: Optimized for speed, best for processing video streams in real-time or on older devices.
Accurate: Optimized for higher accuracy where prediction quality is more important than speed.
Small: Optimized for size, keep your application bundle size low and conserve bandwidth.
Supported mobile platforms:
Runs on live video with a fast frame rate.
Exact FPS performance varies depending on device, but it is possible to run this feature on live video on modern mobile devices.
Uses a MobileNet backbone
~2 MB
1,600 M
353x257-pixel image
2D Coordinates of each keypoint detected
Number of keypoints detected
The confidence associated with each detection
Core ML, TensorFlow, TensorFlow Mobile, TensorFlow Lite, Keras
25 FPS on iPhone X
7 FPS on Pixel 2