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Faster-RCNN in Tensorflow
| Date | Stars |
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| 2026-07-24 | 2342 |
| 2026-07-25 | 2342 |
| 2026-07-28 | 2342 |
| 2026-07-30 | 2342 |
| 2026-08-06 | 2342 |
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# Faster-RCNN_TF
This is an experimental Tensorflow implementation of Faster RCNN - a convnet for object detection with a region proposal network.
For details about R-CNN please refer to the paper [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](http://arxiv.org/pdf/1506.01497v3.pdf) by Shaoqing Ren, Kaiming He, Ross Girshick, Jian Sun.
### Requirements: software
1. Requirements for Tensorflow (see: [Tensorflow](https://www.tensorflow.org/))
2. Python packages you might not have: `cython`, `python-opencv`, `easydict`
### Requirements: hardware
1. For training the end-to-end version of Faster R-CNN with VGG16, 3G of GPU memory is sufficient (using CUDNN)
### Installation (sufficient for the demo)
1. Clone the Faster R-CNN repository
```Shell
# Make sure to clone with --recursive
git clone --recursive https://github.com/smallcorgi/Faster-RCNN_TF.git
```
2. Build the Cython modules
```Shell
cd $FRCN_ROOT/lib
make
```
### Demo
*After successfully completing [basic installation](#installation-sufficient-for-the-demo)*, you'll be ready to run the demo.
Download model training on PASCAL VOC 2007 [[Google Drive]](https://drive.google.com/file/d/0ByuDEGFYmWsbZ0EzeUlHcGFIVWM/view?usp=sharing&resourcekey=0-wl5NWArb595zL-BX6ctTgQ) [[Dropbox]](https://www.dropbox.com/s/cfz3blmtmwj6bdh/VGGnet_fast_rcnn_iter_70000.ckpt?dl=0)
To run the demo
```Shell
cd $FRCN_ROOT
python ./tools/demo.py --model model_path
```
The demo performs detection using a VGG16 network trained for detection on PASCAL VOC 2007.
### Training Model
1. Download the training, validation, test data and VOCdevkit
```Shell
wget http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtrainval_06-Nov-2007.tar
wget http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtest_06-Nov-2007.tar
wget http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCdevkit_08-Jun-2007.tar
```
2. Extract all of these tars into one directory named `VOCdevkit`
```Shell
tar xvf VOCtrainval_06-Nov-2007.tar
tar xvf VOCtest_06-Nov-2007.tar
tar xvf VOCdevkit_08-Jun-2007.tar
```
3. It should have this basic structure
```Shell
$VOCdevkit/ # development kit
$VOCdevkit/VOCcode/ # VOC utility code
$VOCdevkit/VOC2007 # image sets, annotations, etc.
# ... and several other directories ...
```
4. Create symlinks for the PASCAL VOC dataset
```Shell
cd $FRCN_ROOT/data
ln -s $VOCdevkit VOCdevkit2007
```
5. Download pre-trained ImageNet models
Download the pre-trained ImageNet models [[Google Drive]](https://drive.google.com/file/d/0ByuDEGFYmWsbNVF5eExySUtMZmM/view?usp=sharing&resourcekey=0-bxxPp_3P3tc-6Ca7sPrLsw) [[Dropbox]](https://www.dropbox.com/s/po2kzdhdgl4ix55/VGG_imagenet.npy?dl=0)
```Shell
mv VGG_imagenet.npy $FRCN_ROOT/data/pretrain_model/VGG_imagenet.npy
```
6. Run script to train and test model
```Shell
cd $FRCN_ROOT
./experiments/scripts/faster_rcnn_end2end.sh $DEVICE $DEVICE_ID VGG16 pascal_voc
```
DEVICE is either cpu/gpu
### The result of testing on PASCAL VOC 2007
| Classes | AP |
|-------------|--------|
| aeroplane | 0.698 |
| bicycle | 0.788 |
| bird | 0.657 |
| boat | 0.565 |
| bottle | 0.478 |
| bus | 0.762 |
| car | 0.797 |
| cat | 0.793 |
| chair | 0.479 |
| cow | 0.724 |
| diningtable | 0.648 |
| dog | 0.803 |
| horse | 0.797 |
| motorbike | 0.732 |
| person | 0.770 |
| pottedplant | 0.384 |
| sheep | 0.664 |
| sofa | 0.650 |
| train | 0.766 |
| tvmonitor | 0.666 |
| mAP | 0.681 |
###References
[Faster R-CNN caffe version](https://github.com/rbgirshick/py-faster-rcnn)
[A tensorflow implementation of SubCNN (working progress)](https://github.com/yuxng/SubCNN_TF)
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