Region Based Convolutional Neural Networks

Region Based Convolutional Neural Networks (R-CNN) are a family of machine learning models for computer vision and specifically object detection.
History.
The original goal of R-CNN was to take an input image and produce a set of bounding boxes as output, where each bounding box contains an object and also the category (e.g. car or pedestrian) of the object. More recently, R-CNN has been extended to perform other computer vision tasks. The following covers some of the versions of R-CNN that have been developed.
Applications.
Region Based Convolutional Neural Networks have been used for tracking objects from a drone-mounted camera, locating text in an image, and enabling object detection in Google Lens. Mask R-CNN serves as one of seven tasks in the MLPerf Training Benchmark, which is a competition to speed up the training of neural networks.