Faster-RCNN in Tensorflow
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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) - Tested r.011

  2. Python packages needed to run this repo: 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
  1. Build the Cython modules
    cd Faster-RCNN_TF/lib

Quickstart Demo and validate everything is installed correctly

After successfully completing basic installation, you'll be ready to run the demo.

You are going to need to download a pretrained model that was trained on PASCAL VOC 2007 dataset. Download from either of these locations and put into the Faster-RCNN_TF directory [Google Drive] [Dropbox]

Get pretrained model

mv VGGnet_fast_rcnn_iter_70000.ckpt?dl=0 VGGnet_fast_rcnn_iter_70000.ckpt

To run the demo

cd Faster-RCNN_TF/tools

python --model /home/ubuntu/Faster-RCNN_TF/VGGnet_fast_rcnn_iter_70000.ckpt --gpu 0

To run the demo IPython Notebook:

The will load the above pretrained Faster-RCNN_TF network that includes (VGG_ImageNet base and PASCAL 2007 training) and perform detection on each image in the list. is located in the tools folder from the project root. The gpu flag will target the first GPU on your box. You can add images into the data folder and run the against any of your custom photos once you refactor the image list.

Training Model from scratch for fun

  1. Download the training, validation, test data and VOCdevkit

  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]

    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/ GPU_ID VGG16 pascal_voc
    ./experiments/scripts/ $DEVICE $DEVICE_ID VGG16 pascal_voc

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

A tensorflow implementation of SubCNN (working progress)