Core ML Models

Build intelligence into your apps using machine learning models from the research community designed for Core ML.

Models are in Core ML format and can be integrated into Xcode projects. You can select different versions of models to optimize for sizes and architectures.

Images

Images

FCRN-DepthPrediction Depth Estimation

Predict the depth from a single image.
MNIST Drawing Classification

Classify a single handwritten digit (supports digits 0-9).
UpdatableDrawingClassifier Drawing Classification

Drawing classifier that learns to recognize new drawings based on a K-Nearest Neighbors model (KNN).
MobileNetV2 Image Classification

The MobileNetv2 architecture trained to classify the dominant object in a camera frame or image.
Resnet50 Image Classification

A Residual Neural Network that will classify the dominant object in a camera frame or image.
SqueezeNet Image Classification

A small Deep Neural Network architecture that classifies the dominant object in a camera frame or image.
DeeplabV3 Image Segmentation

Segment the pixels of a camera frame or image into a predefined set of classes.
YOLOv3 Object Detection

Locate and classify 80 different types of objects present in a camera frame or image.
YOLOv3-Tiny Object Detection

Locate and classify 80 different types of objects present in a camera frame or image.
PoseNet Pose Estimation

Estimates up to 17 joint positions for each person in an image.
Text

Text

BERT-SQuAD Question Answering

Find answers to questions about paragraphs of text.