Build intelligence into your apps using machine learning models from the research community designed for Core ML
Models can be used with Core ML, Create ML, Xcode, and are available in a number of sizes and architecture formats. Refer to the model’s associated Xcode project for guidance on how to best use the model in your app.
Images
Images
FCRN-DepthPredictionDepth Estimation
Predict the depth from a single image.
MNISTDrawing Classification
Classify a single handwritten digit (supports digits 0-9).
UpdatableDrawingClassifierDrawing Classification
Drawing classifier that learns to recognize new drawings based on a K-Nearest Neighbors model (KNN).
MobileNetV2Image Classification
The MobileNetv2 architecture trained to classify the dominant object in a camera frame or image.
Resnet50Image Classification
A Residual Neural Network that will classify the dominant object in a camera frame or image.
SqueezeNetImage Classification
A small Deep Neural Network architecture that classifies the dominant object in a camera frame or image.
DeeplabV3Image Segmentation
Segment the pixels of a camera frame or image into a predefined set of classes.
YOLOv3Object Detection
Locate and classify 80 different types of objects present in a camera frame or image.
YOLOv3-TinyObject Detection
Locate and classify 80 different types of objects present in a camera frame or image.
Text
Text
BERT-SQuADQuestion Answering
Find answers to questions about paragraphs of text.
FCRN-DepthPrediction
FCRN.mlmodelStoring model weights using full precision (32 bit) floating point numbers. 254.7MB
Finding Answers to Questions in a Text DocumentLocate relevant passages in a document by asking the Bidirectional Encoder Representations from Transformers (BERT) model a question.