Largest list of models for Core ML (for iOS 11+)

Collection of models for Core ML

👀 iOS11 demo application for age and gender classification of facial images.

Discover, download, compile & launch different image processing & style transfer CoreML models on iOS.

A CoreML model which classifies images of food

😃 iOS11 demo application for sentiment polarity analysis.

Predict handwritten digits with CoreML

A Big Awesome List CoreML Models.

🌅 iOS11 demo application for visual sentiment prediction.

🏷 iOS11 demo application for predicting gender from first names.

The better way to deal with MNIST model and Core ML in iOS

Build your iOS 11+ apps with the ready-to-use Core ML models below

This project shows how to use CoreML and Vision with a pre-trained deep learning SSD (Single Shot MultiBox Detector) model. There are many variations of SSD. The one we’re going to use is MobileNetV2 as the backbone this model also has separable convolutions for the SSD layers, also known as SSDLite. This app can find the locations of several different types of objects in the image. The detections are described by bounding boxes, and for each bounding box, the model also predicts a class.

Create an AR Project with ARKit. Explored Tracking, Plane Detection, Hit Testing, Light Estimation, SCNNode Rendering etc.

MobileNet in CoreML with Vision implemented for iPhone iOS in Swift

iOS AR application that helps engineers identify hardware with object recognition. Grand Prize Winner and Best Neural Network @ HackWescam 2018.

an end-to-end tutorial for OCR recognition using CNN

a CoreML version of FastDepth

Core ML and Vision object classifier with a lightweight trained model. The model is trained and tested with Create ML straight from Xcode Playgrounds with the dataset I provided.

A simple classification of mnist handwritten digits

Most developing countries have a serious shortage of qualified medical personnel. Particularly of qualified pathologists, which leads to long delays in the testing and the diagnosis of diseases. This in its own leads to needless suffering and unnecessary deaths. A review of literature on the field of ML in medical science shows that ML can partially contribute to correcting this problem. These have been specially designed to allow ML models to work with mobile and portable devices such as the iPhones, iPads and the android equivalents. The aim of this dissertation is to show the potential and possibility to develop the next stage of software that can be used in the medical field. The use of ML will reduce the need for other resources such as external databases and other heavy tools. The applications proposed in this project could allow medical professionals such as “Doctors without borders” to test and diagnose patients in field hospitals without having to carry a lot of equipment or have to send blood samples over long distances with related delays and the risk of contamination. The applications could also be used in developed countries where medical facilities are under pressure. The applications could provide an indication of a disease/illness, saving time and resources.

PictionAIry, my WWDC18 scholarship entry!

Augmented Reality Tetris made with ARKit and SceneKit

Take your food's photo and let the code guess it

Confoo 2018 - Add Machine Learning to your iOS 
App with Core ML

Get Ready for Core ML 2

Confoo 2018 - Add Machine Learning to your iOS 
App with Core ML

Test Example of CoreML