R CNN - AshokBhat/ml GitHub Wiki

About

R-CNN (Region-based Convolutional Neural Networks)
State-of-the-art for object detection

Core idea

First, using selective search, identify a manageable number of bounding-box object region candidates (“region of interest” or “RoI”).
Then extracts CNN features from each region independently for classification.

Illustration

Paper

Title: Rich feature hierarchies for accurate object detection and semantic segmentation
Date: 2013-14
Link: https://arxiv.org/abs/1311.2524

Abstract

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