taokong / Ron
Programming Languages
RON: Reverse Connection with Objectness Prior Networks for Object Detection
RON is a state-of-the-art visual object detection system for efficient object detection framework. The code is modified from py-faster-rcnn. You can use the code to train/evaluate a network for object detection task. For more details, please refer to our CVPR paper.
***There is also a tensorflow re-implementation of RON at RON_Tensorflow, thanks @HiKapok!
Citing RON
If you find RON useful in your research, please consider citing:
@inproceedings{KongtCVPR2017,
Author = {Tao Kong, Fuchun Sun, Anbang Yao, Huaping Liu, Ming Lu, Yurong Chen},
Title = {RON: Reverse Connection with Objectness Prior Networks for Object Detection},
Booktitle = {IEEE Conference on Computer Vision and Pattern Recognition},
Year = {2017}
}
PASCAL VOC detection results
| Method | VOC 2007 mAP | VOC 2012 mAP | Input resolution |
|---|---|---|---|
| Fast R-CNN | 70.0% | 68.4% | 1000*600 |
| Faster R-CNN | 73.2% | 70.4% | 1000*600 |
| SSD300 | 72.1% | 70.3% | 300*300 |
| SSD500 | 75.1% | 73.1% | 500*500 |
| RON320 | 74.2% | 71.7% | 320*320 |
| RON384 | 75.4% | 73.0% | 384*384 |
MS COCO detection results
| Method | Training data | AP(0.50-0.95) | Input resolution |
|---|---|---|---|
| Faster R-CNN | trainval | 21.9% | 1000*600 |
| SSD500 | trainval35k | 24.4% | 500*500 |
| RON320 | trainval | 23.6% | 320*320 |
| RON384 | trainval | 25.4% | 384*384 |
Note: SSD300 and SSD500 are the original SSD model from SSD.
RON Installation
-
Clone the RON repository
git clone https://github.com/taokong/RON.git -
Build Caffe and pycaffe
cd $RON_ROOT/ git clone https://github.com/taokong/caffe-ron.git cd caffe-ron make -j8 && make pycaffe *this version use CUDNN for efficiency, so make sure that "USE_CUDNN := 1" in the Makefile.config file. -
Build the Cython modules
cd $RON_ROOT/lib make -
installation for training and testing models on PASCAL VOC dataset
3.0 The PASCAL VOC dataset has the basic structure:
$VOCdevkit/ # development kit $VOCdevkit/VOCcode/ # VOC utility code $VOCdevkit/VOC2007 # image sets, annotations, etc.3.1 Create symlinks for the PASCAL VOC dataset
cd $RON_ROOT/data ln -s $VOCdevkit VOCdevkit2007 ln -s $VOCdevkit VOCdevkit2012 -
Test with PASCAL VOC dataset
Now we provide two models for testing the pascal voc 2007 test dataset. To use demo you need to download the pretrained RON model, please download the model manually from BaiduYun(Google Drive), and put it under
$data/RON_models.4.0 The original model as introduced in the RON paper:
./test_voc07.sh # The final result of the model should be 74.2% mAP.4.1 A lite model we make some optimization after the original one:
./test_voc07_reduced.sh # The final result of the model should be 74.1% mAP. -
Train with PASCAL VOC dataset
Please download ImageNet-pre-trained VGG models manually from BaiduYun(Google Drive), and put them into $data/ImageNet_models. Then everything is done, you could train your own model.
5.0 The original model as introduced in the RON paper:
./train_voc.sh
5.1 A lite model we make some optimization after the original one:
./train_voc_reduced.sh