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

SwiftUI Stable Diffusion implementation using CoreML and PyTorch

A CoreML model which classifies images of food

😃 iOS11 demo application for sentiment polarity analysis.

Real-time on-device text-to-image and image-to-image Semantic Search with video stream capture using USearch & UForm AI Swift SDKs for Apple devices 🍏

🌅 iOS11 demo application for visual sentiment prediction.

🏷 iOS11 demo application for predicting gender from first names.

This project is a demo on using CoreML framework for sentiment analysis of text. .mlmodel was developed from Scikit-learn Pipeline using coremltools python package. More details here : https://developer.apple.com/documentation/coreml/converting_trained_models_to_core_ml

a CoreML version of FastDepth

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

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 di…

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

Very simple app that uses CoreML model to transform image to anime style image.

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.

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

Real time camera object detection with Machine Learning in swift. Basic introduction to Core ML, Vision and ARKit.

Augmented Reality Tetris made with ARKit and SceneKit

Combining the power of MobileNetV2 with the privacy of on-device learning. Benefit from real-time updates and efficient image processing, all while ensuring your data remains securely on your device. Experience precision, speed, and trust with PixeLearner.

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 parti…