Region Based Convolutional Neural Networks: Difference between revisions

Revision as of 00:48, 6 September 2024

Revision as of 00:50, 6 September 2024

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{{Short description|Machine learning model family}}

[[File:R-cnn.svg|thumb|272x272px|R-CNN architecture]]

'''Region-based Convolutional Neural Networks (R-CNN)''' are a family of machine learning models for [[computer vision]], and specifically [[object detection]] and localization.<ref>{{Cite book |last=Zhang |first=Aston |title=Dive into deep learning |last2=Lipton |first2=Zachary |last3=Li |first3=Mu |last4=Smola |first4=Alexander J. |date=2024 |publisher=Cambridge University Press |isbn=978-1-009-38943-3 |___location=Cambridge New York Port Melbourne New Delhi Singapore |chapter=14.8. Region-based CNNs (R-CNNs) |chapter-url=https://d2l.ai/chapter_computer-vision/rcnn.html}}</ref> The original goal of R-CNN was to take an input image and produce a set of [[Minimum bounding box|bounding boxes]] as output, where each bounding box contains an object and also the category (e.g. car or pedestrian) of the object.