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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.
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.
Simple Swift projects to get started with iOS app development
A PyTorch implementation of ANTNet
Sample App to classify Simpsons using Keras
Demo app for gender classification of facial images using GenderNet, Vision and CoreML.