Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Fast R-CNN
27.365
Zitationen
1
Autoren
2015
Jahr
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.
Ähnliche Arbeiten
Deep Residual Learning for Image Recognition
2016 · 216.319 Zit.
U-Net: Convolutional Networks for Biomedical Image Segmentation
2015 · 86.058 Zit.
ImageNet classification with deep convolutional neural networks
2017 · 75.548 Zit.
Very Deep Convolutional Networks for Large-Scale Image Recognition
2014 · 75.405 Zit.
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
2016 · 52.734 Zit.