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 by Shaoqing Ren, Kaiming He, Ross Girshick, Jian Sun.
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Requirements for Tensorflow (see: Tensorflow)
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Python packages you might not have:
cython,python-opencv,easydict
- For training the end-to-end version of Faster R-CNN with VGG16, 3G of GPU memory is sufficient (using CUDNN)
- Clone the Faster R-CNN repository
# Make sure to clone with --recursive
git clone --recursive https://github.com/smallcorgi/Faster-RCNN_TF.git- Build the Cython modules
cd $FRCN_ROOT/lib sh make.sh
After successfully completing basic installation, you'll be ready to run the demo.
To run the demo
cd $FRCN_ROOT
./tools/demo.py --model model_pathThe demo performs detection using a VGG16 network trained for detection on PASCAL VOC 2007.
Pre-trained ImageNet models can be downloaded here.
Download model training on PASCAL VOC 2007 here.
| 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 |