PVANet: Lightweight Deep Neural Networks for Real-time Object Detection

by Sanghoon Hong, Byungseok Roh, Kye-hyeon Kim, Yeongjae Cheon, Minje Park (Intel Imaging and Camera Technology) Presented in EMDNN2016, a NIPS2016 workshop (arXiv link)

Introduction

This repository is a fork from py-faster-rcnn and demonstrates the performance of PVANet.

You can refer to py-faster-rcnn README.md and faster-rcnn README.md for more information.

Desclaimer

Please note that this repository doesn't contain our in-house codes used in the published article.

• This version of py-faster-rcnn is slower than our in-house runtime code (e.g. image pre-processing code written in Python)
• PVANet was trained by our in-house deep learning library, not by this implementation.
• There might be a tiny difference in VOC2012 test results, because some hidden parameters in py-faster-rcnn may be set differently with ours.

Citing PVANet

If you want to cite this work in your publication:

@article{hong2016pvanet,
  title={{PVANet}: Lightweight Deep Neural Networks for Real-time Object Detection},
  author={Hong, Sanghoon and Roh, Byungseok and Kim, Kye-Hyeon and Cheon, Yeongjae and Park, Minje},
  journal={arXiv preprint arXiv:1611.08588},
  year={2016}
}

Installation

0. Install Dependency

      sudo pip install Cython

1. Clone the Faster R-CNN repository

   # Make sure to clone with --recursive
   git clone --recursive https://github.com/sanghoon/pva-faster-rcnn.git

2. We'll call the directory that you cloned Faster R-CNN into FRCN_ROOT. Build the Cython modules

   cd $FRCN_ROOT/lib
   make

3. Build Caffe and pycaffe

   cd $FRCN_ROOT/caffe-fast-rcnn
   # Now follow the Caffe installation instructions here:
   #   http://caffe.berkeleyvision.org/installation.html
   # For your Makefile.config:
   #   Uncomment `WITH_PYTHON_LAYER := 1`
   
   cp Makefile.config.example Makefile.config
   make -j8 && make pycaffe

4. Download PVANet detection model for VOC2007

   cd $FRCN_ROOT
   ./models/pvanet/download_voc2007.sh

5. Download PVANet detection model for VOC2012 (published model)

   cd $FRCN_ROOT
   ./models/pvanet/download_voc_best.sh

6. (Optional) Download all available models (including pre-trained and compressed models)

   cd $FRCN_ROOT
   ./models/pvanet/download_all_models.sh

7. (Optional) Download ILSVRC2012 (ImageNet) classification model

   cd $FRCN_ROOT
   ./models/pvanet/download_imagenet_model.sh

8. (Optional) If the scripts don't work, please download the models from ...

   Model	Google Drive
   PVANet for VOC2007	link
   PVANet for VOC2012	link
   PVANet for VOC2012 (compressed)	link
   PVANet for ILSVRC2012 (ImageNet)	link
   PVANet pre-trained	link

How to run the demo

1. Download PVANet detection models

2. Run the demo script

   cd $FRCN_ROOT
   ./tools/test_pvanet.py

How to test on the VOC dataset

1. Download PASCAL VOC 2007 and 2012 -- Follow the instructions in py-faster-rcnn README.md

2. PVANet on PASCAL VOC 2007

   cd $FRCN_ROOT
   ./tools/test_net.py --net models/pvanet/pva9.1/PVA9.1_ImgNet_COCO_VOC0712.caffemodel --def models/pvanet/pva9.1/faster_rcnn_train_test_21cls.pt --cfg models/pvanet/cfgs/submit_1019.yml --gpu 0

3. PVANet (compressed)

   cd $FRCN_ROOT
   ./tools/test_net.py --net models/pvanet/pva9.1/PVA9.1_ImgNet_COCO_VOC0712plus_compressed.caffemodel --def models/pvanet/pva9.1/faster_rcnn_train_test_ft_rcnn_only_plus_comp.pt --cfg models/pvanet/cfgs/submit_1019.yml --gpu 0

Expected results

Mean Average Precision on VOC detection tasks

Model	VOC2007 mAP (%)	VOC2012 mAP (%)
PVANet+ (VOC2007)	84.9	N/A
PVANet+ (VOC2012)	89.8	84.2
PVANet+ (VOC2012 + compressed)	87.8	83.7

• The training set for the VOC2012 model includes the VOC2007 test set. Therefore the accuracies on VOC2007 of the model are not meaningful; They're shown here just for reference

Validation error on ILSVRC2012

Input size	Top-1 error (%)	Top-5 error (%)
192x192	30.00	N/A
224x224	27.66	8.84

• We re-trained a 224x224 model from the '192x192' model as a base model.