pva-faster-rcnn: A Python repository from oylz

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

Clone the Faster R-CNN repository

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

We'll call the directory that you cloned Faster R-CNN into FRCN_ROOT. Build the Cython modules
```
cd $FRCN_ROOT/lib
make
```

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

Download PVANet detection model for VOC2007

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

Download PVANet detection model for VOC2012 (published model)
```
cd $FRCN_ROOT
./models/pvanet/download_voc_best.sh
```
(Optional) Download all available models (including pre-trained and compressed models)
```
cd $FRCN_ROOT
./models/pvanet/download_all_models.sh
```
(Optional) Download ILSVRC2012 (ImageNet) classification model
```
cd $FRCN_ROOT
./models/pvanet/download_imagenet_model.sh
```
(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

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

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

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

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.

oylz/pva-faster-rcnn