  Object detection performance, as measured on the canonical PASCAL VOC
dataset, has plateaued in the last few years. The best-performing methods are
complex ensemble systems that typically combine multiple low-level image
features with high-level context. In this paper, we propose a simple and
scalable detection algorithm that improves mean average precision (mAP) by more
than 30% relative to the previous best result on VOC 2012---achieving a mAP of
53.3%. Our approach combines two key insights: (1) one can apply high-capacity
convolutional neural networks (CNNs) to bottom-up region proposals in order to
localize and segment objects and (2) when labeled training data is scarce,
supervised pre-training for an auxiliary task, followed by domain-specific
fine-tuning, yields a significant performance boost. Since we combine region
proposals with CNNs, we call our method R-CNN: Regions with CNN features. We
also compare R-CNN to OverFeat, a recently proposed sliding-window detector
based on a similar CNN architecture. We find that R-CNN outperforms OverFeat by
a large margin on the 200-class ILSVRC2013 detection dataset. Source code for
the complete system is available at http://www.cs.berkeley.edu/~rbg/rcnn.
