# R-FCN-3000 at 30fps: Decoupling Detection and Classification

Source: [R-FCN-3000 at 30fps: Decoupling Detection and Classification](<https://www.sota2.com/research/paper/68fa63d91e209131cea01518>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T20:51:10.308Z

Authors: Bharat Singh, Hengduo Li, Abhishek Sharma, Larry S. Davis

Published: 2017-12-05T18:30:19.000Z

## Abstract

We present R-FCN-3000, a large-scale real-time object detector in which objectness detection and classification are decoupled. To obtain the detection score for an RoI, we multiply the objectness score with the fine-grained classification score. Our approach is a modification of the R-FCN architecture in which position-sensitive filters are shared across different object classes for performing localization. For fine-grained classification, these position-sensitive filters are not needed. R-FCN-3000 obtains an mAP of 34.9% on the ImageNet detection dataset and outperforms YOLO-9000 by 18% while processing 30 images per second. We also show that the objectness learned by R-FCN-3000 generalizes to novel classes and the performance increases with the number of training object classes - supporting the hypothesis that it is possible to learn a universal objectness detector. Code will be made available.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1712.01802>)

[PDF](<https://arxiv.org/pdf/1712.01802.pdf>)