With an Image Labeling model, you can identify the contents of an image or each frame of live video. Each prediction returns a set of labels as well as a confidence score for each label. Image Labeling can recognize people, places, and things.
If you need to know what objects are in an image, and where they are, consider using Object Detection instead.
Custom Training Models for Image Labeling¶
You can train a custom model that is compatible with the Image Labeling API by using Quickstart: Use Fritz AI Studio to Train a Custom Model.
Pre-trained Image Labeling¶
The pre-trained Image Labeling model supports 681 labels. View the full label list.
|MobileNet V2 variant||Core ML (iOS), TensorFlow Lite (Android)||~13MB||224x224-pixel image||Label + confidence score (0-100%)||38 FPS on iPhone X, 10 FPS on Pixel 2|
Custom Model Compatibility Checklist¶
If you have a custom model that was trained outside of Fritz AI, follow this checklist to make sure it will be compatible with the Image Labeling API.
- Your model must be in the TensorFlow Lite (.tflite) or Core ML (.mlmodel) formats.
- iOS Only The name of the input layer must be named
imageand the output
- Android Only The input (image) and output layer (confidence) should be defined in the TensorFlow Lite conversion tool.
- The input should have the following dimensions:
1 (batch_size) x 224 (height) x 224 (width) x 3 (num_channels). Height and width are configurable.
- The output should have the following dimensions:
1 x number_of_labels.