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TFFRCNN

This is an experimental Tensor Flow implementation of Faster RCNN (TFFRCNN), mainly based on the work of smallcorgi and rbgirshick. I have re-organized the libraries under lib path, making each of python modules independent to each other, so you can understand, re-write the code easily.

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.

What's New

  • Resnet networks support
  • KITTI object detection dataset support
  • Position Sensitive ROI Pooling (psroi_pooling), not testing yet
  • Hard Example Mining
  • Data Augment
  • PVANet
  • Tensorflow 1.0
  • R-FCN
  • Multi-layer Architecture (HyperNet)
  • more hacks...

Acknowledgments:

  1. py-faster-rcnn

  2. Faster-RCNN_TF

  3. ROI pooling

Requirements: software

  1. Requirements for Tensorflow (see: Tensorflow)

  2. Python packages you might not have: cython, python-opencv, easydict (recommend to install: Anaconda)

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
git clone https://github.com/CharlesShang/TFFRCNN.git
  1. Build the Cython modules 
    cd TFFRCNN/lib
make # compile cython and roi_pooling_op, you may need to modify make.sh for your platform

Demo

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

To run the demo

cd $TFFRCNN
python ./faster_rcnn/demo.py --model model_path

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

Download list

  1. VGG16 trained on ImageNet

  2. VGG16 - TFFRCNN (0.689 mAP on VOC07).

  3. VGG16 - TFFRCNN (0.748 mAP on VOC07)

  4. Resnet50 trained on ImageNet

  5. Resnet50 - TFFRCNN (0.712 mAP on VOC07)

  6. PVANet trained on ImageNet, converted from caffemodel

Training on Pascal VOC 2007

  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 $TFFRCNN/data
ln -s $VOCdevkit VOCdevkit2007

  5. Download pre-trained model VGG16 and put it in the path ./data/pretrain_model/VGG_imagenet.npy

  6. Run training scripts

    cd $TFFRCNN
python ./faster_rcnn/train_net.py --gpu 0 --weights ./data/pretrain_model/VGG_imagenet.npy --imdb voc_2007_trainval --iters 70000 --cfg  ./experiments/cfgs/faster_rcnn_end2end.yml --network VGGnet_train --set EXP_DIR exp_dir

  7. Run a profiling

    cd $TFFRCNN
# install a visualization tool
sudo apt-get install graphviz  
./experiments/profiling/run_profiling.sh 
# generate an image ./experiments/profiling/profile.png

Training on KITTI detection dataset

  1. Download the KITTI detection dataset

    http://www.cvlibs.net/datasets/kitti/eval_object.php

  2. Extract all of these tar into ./TFFRCNN/data/ and the directory structure looks like this:

    KITTI
    |-- training
            |-- image_2
                |-- [000000-007480].png
            |-- label_2
                |-- [000000-007480].txt
    |-- testing
            |-- image_2
                |-- [000000-007517].png
            |-- label_2
                |-- [000000-007517].txt

  3. Convert KITTI into Pascal VOC format

    cd $TFFRCNN
./experiments/scripts/kitti2pascalvoc.py \
--kitti $TFFRCNN/data/KITTI --out $TFFRCNN/data/KITTIVOC

  4. The output directory looks like this:

    KITTIVOC
    |-- Annotations
            |-- [000000-007480].xml
    |-- ImageSets
            |-- Main
                |-- [train|val|trainval].txt
    |-- JPEGImages
            |-- [000000-007480].jpg

  5. Training on KITTIVOC is just like on Pascal VOC 2007

    python ./faster_rcnn/train_net.py \
--gpu 0 \
--weights ./data/pretrain_model/VGG_imagenet.npy \
--imdb kittivoc_train \
--iters 160000 \
--cfg ./experiments/cfgs/faster_rcnn_kitti.yml \
--network VGGnet_train