Top AI Repos — open-source AI, indexed and scored
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
R-CNN: Regions with Convolutional Neural Network Features
| Date | Stars |
|---|---|
| 2026-07-31 | 2414 |
| 2026-08-04 | 2415 |
| 2026-08-05 | 2415 |
| 2026-08-06 | 2416 |
Today
+1 stars today
This week
— stars this week
This month
— stars this month
Momentum
0.0
growth rate 0.00%/day
### This code base is no longer maintained and exists as a historical artifact to supplement our CVPR and PAMI papers on Region-based Convolutional Neural Netwoks. For more recent work that's faster and more accurrate, please see [Fast and Faster R-CNN](https://github.com/rbgirshick/py-faster-rcnn).
## R-CNN: *Region-based Convolutional Neural Networks*
Created by Ross Girshick, Jeff Donahue, Trevor Darrell and Jitendra Malik at UC Berkeley EECS.
Acknowledgements: a huge thanks to Yangqing Jia for creating Caffe and the BVLC team, with a special shoutout to Evan Shelhamer, for maintaining Caffe and helping to merge the R-CNN fine-tuning code into Caffe.
### Introduction
R-CNN is a state-of-the-art visual object detection system that combines bottom-up region proposals with rich features computed by a convolutional neural network. At the time of its release, R-CNN improved the previous best detection performance on PASCAL VOC 2012 by 30% relative, going from 40.9% to 53.3% mean average precision. Unlike the previous best results, R-CNN achieves this performance without using contextual rescoring or an ensemble of feature types.
R-CNN was initially described in an [arXiv tech report](http://arxiv.org/abs/1311.2524) and will appear in a forthcoming CVPR 2014 paper.
### Citing R-CNN
If you find R-CNN useful in your research, please consider citing:
@inproceedings{girshick14CVPR,
Author = {Girshick, Ross and Donahue, Jeff and Darrell, Trevor and Malik, Jitendra},
Title = {Rich feature hierarchies for accurate object detection and semantic segmentation},
Booktitle = {Computer Vision and Pattern Recognition},
Year = {2014}
}
### License
R-CNN is released under the Simplified BSD License (refer to the
LICENSE file for details).
### PASCAL VOC detection results
Method | VOC 2007 mAP | VOC 2010 mAP | VOC 2012 mAP
-------------- |:------------:|:------------:|:------------:
R-CNN | 54.2% | 50.2% | 49.6%
R-CNN bbox reg | 58.5% | 53.7% | 53.3%
* VOC 2007 per-class results are available in our [CVPR14 paper](http://www.rossgirshick.info/#girshick2014rcnn)
* VOC 2010 per-class results are available on the [VOC 2010 leaderboard](http://host.robots.ox.ac.uk:8080/leaderboard/displaylb_dt.php?challengeid=6&compid=4)
* VOC 2012 per-class results are available on the [VOC 2012 leaderboard](http://host.robots.ox.ac.uk:8080/leaderboard/displaylb_dt.php?challengeid=11&compid=4)
* These models are available in the model package (see below)
### ImageNet 200-class detection results
Method | ILSVRC2013 test mAP
---------------|:-------------------:
R-CNN bbox reg | 31.4%
* For more details see the updated [R-CNN tech report](http://arxiv.org/abs/1311.2524v3) (Sections 2.5 and 4, in particular)
* This model is available in the model package (see below)
* The code that was used for training is in the `ilsvrc` branch (still needs some cleanup before merging into `master`)
### Installing R-CNN
0. **Prerequisites**
0. MATLAB (tested with 2012b on 64-bit Linux)
0. Caffe's [prerequisites](http://caffe.berkeleyvision.org/installation.html#prequequisites)
0. **Install Caffe** (this is the most complicated part)
0. R-CNN has been checked for compatability against Caffe release v0.999. *It has not been updated to work with the current Caffe master.*
0. Download [Caffe v0.999](https://github.com/BVLC/caffe/archive/v0.999.tar.gz)
0. Follow the [Caffe installation instructions](http://caffe.berkeleyvision.org/installation.html)
0. Let's call the place where you installed caffe `$CAFFE_ROOT` (you can run `export CAFFE_ROOT=$(pwd)`)
0. **Important:** Make sure to compile the Caffe MATLAB wrapper, which is not built by default: `make matcaffe`
1. **Important:** Make sure to run `cd $CAFFE_ROOT/data/ilsvrc12 && ./get_ilsvrc_aux.sh` to download the ImageNet image mean
0. **Install R-CNN**
0. Get the R-CNN source code by cloning the repository:Excerpt of 12,187 characters
Read on GitHub
Ross Girshick · UC Berkeley
49
Jeff Donahue
1
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:5516ac1747084b09, llm:Repository description and README: 'R-CNN: Region-based Convolutional Neural Networks', object detection system, references to CVPR paper, PASCAL VOC detection results, implementation in Matlab/Caffe.
matched fp:5516ac1747084b09, llm:Repository description and README: 'R-CNN: Region-based Convolutional Neural Networks', object detection system, references to CVPR paper, PASCAL VOC detection results, implementation in Matlab/Caffe.