Computer Vision For iOS Developers Course

Discover how computer vision lets computers see and interpret digital images, contrasting it with image processing while examining object classification, detection, and segmentation.

Explore five image annotation tools to create datasets and label images for training computer vision models, using bounding boxes, polygons, and semantic segmentation with export options.

Explore tools and environments to train mobile computer vision models, including cloud and local training, data preparation, annotations, and model types like object detectors and image classifiers.

MakeML overview and initial setup for beginners, highlighting data labeling, training neural networks, and model training in a few clicks. Create data sets and micro projects to start training now.

Learn how to adjust training hyperparameters—batch size, iterations, and learning rate—to improve object detection and semantic segmentation models, reduce loss, and optimize model quality.

Review the dribble dataset trained model and its loss, replace the model in the iOS app, and run it on a device to observe soccer ball detection.

Explore a PepsiCo case of using computer vision to sort potatoes by weight for iOS developers, detailing three datasets for detection and segmentation and presenting training results.