Fast R-CNN

April 30, 2015 · Entered Twilight · 🏛 ICCV 2015

"Last commit was 8.0 years ago (≥5 year threshold)"

Evidence collected by the PWNC Scanner

Repo contents: .gitignore, .gitmodules, LICENSE, README.md, caffe-fast-rcnn, data, experiments, lib, matlab, models, output, todo.txt, tools

Authors Ross Girshick arXiv ID 1504.08083 Category cs.CV: Computer Vision Citations 27.7K Venue ICCV 2015 Repository https://github.com/rbgirshick/fast-rcnn ⭐ 3453 Last Checked 1 month ago

Abstract

This paper proposes a Fast Region-based Convolutional Network method (Fast R-CNN) for object detection. Fast R-CNN builds on previous work to efficiently classify object proposals using deep convolutional networks. Compared to previous work, Fast R-CNN employs several innovations to improve training and testing speed while also increasing detection accuracy. Fast R-CNN trains the very deep VGG16 network 9x faster than R-CNN, is 213x faster at test-time, and achieves a higher mAP on PASCAL VOC 2012. Compared to SPPnet, Fast R-CNN trains VGG16 3x faster, tests 10x faster, and is more accurate. Fast R-CNN is implemented in Python and C++ (using Caffe) and is available under the open-source MIT License at https://github.com/rbgirshick/fast-rcnn.