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 by Shaoqing Ren, Kaiming He, Ross Girshick, Jian Sun.

Requirements: software

1. Requirements for Tensorflow (see: Tensorflow)

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

# Make sure to clone with --recursive
git clone --recursive https://github.com/smallcorgi/Faster-RCNN_TF.git

2. Build the Cython modules

   cd $FRCN_ROOT/lib
   make

Demo

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_path

The demo performs detection using a VGG16 network trained for detection on PASCAL VOC 2007.

Training Results

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

Download pre-trained ImageNet models

If you want to train the model by yourself, and you can download the pre-trained ImageNet models here.