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.

Download model training on PASCAL VOC 2007 [Google Drive] [Dropbox]

To run the demo

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

    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

    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

      $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

    cd $FRCN_ROOT/data
    ln -s $VOCdevkit VOCdevkit2007

5. Download pre-trained ImageNet models

   Download the pre-trained ImageNet models [Google Drive] [Dropbox]

   ```Shell

   mv VGG_imagenet.npy $FRCN_ROOT/data/pretrain_model/VGG_imagenet.npy
   ```

6. Run script to train and test model

    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

References

Faster R-CNN caffe version

A tensorflow implementation of SubCNN (working progress)