![]() | This article may be too technical for most readers to understand.(August 2020) |
Region-based Convolutional Neural Networks (R-CNN) are a family of machine learning models for computer vision and specifically object detection.
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.
Region-based convolutional neural networks have been used for tracking objects from a drone-mounted camera, [5] locating text in an image, [6] and enabling object detection in Google Lens. [7] 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. [8]
![]() | This article may be too technical for most readers to understand.(August 2020) |
Region-based Convolutional Neural Networks (R-CNN) are a family of machine learning models for computer vision and specifically object detection.
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.
Region-based convolutional neural networks have been used for tracking objects from a drone-mounted camera, [5] locating text in an image, [6] and enabling object detection in Google Lens. [7] 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. [8]