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PVANet-FACE: PVANET for face detection

Introduction

Training a face detection model using PVANet.

face detection 1 face detection 2

The dataset used for training is WIDERFACE

This repository contains source files of face detection using the PVANet. It is developed based on the awesome pva-faster-rcnn repository.

Requirement

  1. Nivida CUDA 8.0
  2. Nvidia CUDNN 6
  3. Python 2

Installation

  1. Clone this repository

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

  2. We'll call the directory that you cloned as 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

Training the face detection model

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

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

  2. Download WIDERFace for training.

    I use python-widerface and cute-format to pack all the images of WIDERFace into the custom-defined imdb, where the format of imdb is different from VOC format.

    please look tools/convert_wider_to_imdb.py for detail.

    to run tools/convert_wider_to_imdb.py, update path to WIDERFace

    for example,

    # arg1: path to split (where the label file is)
# arg2: path to images
# arg3: path to label file name
 wider_train = WIDER('/opt/WiderFace/wider_face_split',
               '/opt/WiderFace/WIDER_train/images',
               'wider_face_train.mat')

 cw = CuteWriter('wider-imdb')

 run(wider_train, cw)

    this will generate a db named wider-imdb, and put wider-imdb into data/widerface/

  3. Training PVANet

    cd $FRCN_ROOT
tools/train_net.py --gpu 0 --solver models/pvanet/example_train/solver.prototxt --weights models/pvanet/pretrained/pva9.1_pretrained_no_fc6.caffemodel --iters 100000 --cfg models/pvanet/cfgs/train.yml --imdb wider

How to run the demo

  1. Download pretrained model

  2. Run the tools/demo.py

    cd $FRCN_ROOT
./tools/demo.py --net output/faster_rcnn_pvanet/wider/pvanet_frcnn_iter_100000.caffemodel --def models/pvanet/pva9.1/faster_rcnn_train_test_21cls.pt --cfg models/pvanet/cfgs/submit_1019.yml --gpu 0

Compression

If you want to compress your model, please look at tools/gen_merged_model.py. As compared to sanghoon's implementation (https://github.com/sanghoon/pva-faster-rcnn/blob/master/tools/gen_merged_model.py), I add the function to remove redundant power layers.