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README.md

Face-MagNet: Magnifying Feature Maps to Detect Small Faces

By Pouya Samangouei*, Mahyar Najibi*, Larry Davis, Rama Chellappa

* contributed equally.

The paper can be found here.

This Python implementation is based on Faster-RCNN.

License

Face-MagNet is released under the Apache 2 License (refer to the LICENSE file for details).

Citing Face-MagNet

@inproceedings{wacv18facemagnet,
    Author = {Pouya Samangouei and Mahyar Najibi and Larry Davis and Rama Chellappa},
    Title = {Face-MagNet: Magnifying Feature Maps to Detect Small Faces},
    Booktitle = {IEEE Winter Conf. on Applications of Computer Vision ({WACV})},
    Year = {2018}
}

Installation

  • Download face-magnet.
cd <project-parent-dir>
git clone --recursive https://github.com/po0ya/face-magnet
FM_ROOT=`pwd`/face-magnet
  • Install python requirements.

    cd $FM_ROOT
    pip install -r requirements.txt
    
  • Make lib:

    cd $FM_ROOT/lib
    make
    
  • Install nccl from https://developer.nvidia.com/nccl for multi-gpu training.

  • Build caffe.

    cd caffe-facemagnet
    # Set build flags e.g., edit caffe-facemagnet/Makefile.config appropriately.
    make -j8
    make pycaffe
    
  • Download the WIDER-Face dataset.

    WIDER_PATH=<path-to-wider-face>
    
  • Perpare the data directory:

    • Download imagenet pretrained weights using scripts from Faster-RCNN:

      ./data/scripts/fetch_imagenet_models.sh
      
    • Create a symlink to WIDER-Face path:

      ln -s $WIDER_PATH data/wider
      
      • (optional) Copy imglist.csv files into WIDER_PATH:
      cp -r data/WIDER_imglists/* $WIDER_PATH
      

      These csv files are generated with matlab/wider_csv.m and is provided in case matlab is not available. If it is, the code will generate these automatically.

  • Create a or link a debug directory for output logs.

    mkdir debug
    

    or

    ln -s <path-to-debug-dir> debug
    

Train a model:

./experiments/scripts/train_min800.sh <model-name> <gpu-ids>
  • <model-name> can be:

    • facemagnet: Face-MagNet model for the 8th row of Table 1 and the final results.
    • sizesplit: SizeSplit model for the 7th row of Table 1.
    • skipface: SkipFace model for the 6th row of Table 1.
    • context: Context model for the 5th row of Table 1.
    • default: For the first row of Table 1.
    • facemagnet_nc, sizesplit_nc, and skipface_nc for the models without context pooling.
  • <gpu-ids> is a string of comma separated GPU ids.

  • To see the training progress tail -f <log-path> where <log-path> is shown after running the above script.

For example, for facemagnet run:

./experiments/scripts/train_min800.sh facemagnet 0,1

To train Face-MagNet on two GPUs.

For single GPU training please use ./tools/train_net.py.

 python ./tools/train_net.py --gpu <gpu-id> \
  --solver models/wider/VGG16/solvers/<model-name>.prototxt \
  --weights data/imagenet_models/VGG16.v2.caffemodel \
  --imdb wider_train \
  --iters 76000 \
  --cfg experiments/cfgs/min800.yml \
  ${EXTRA_ARGS}

Single image detection

To detect faces on a single image with a trained model:

./experiments/scripts/test_single.sh <model-name> <image-path> <args>
  • <model-name> is the same as the training section.
  • <image-path> is the path to the image
  • args can be the following optional arguments:
    • --min_size <int> Overrides cfg.TEST.SCALES so that the minimum dimension of the image is scaled to this value.
    • --max_size <int> Overrides cfg.TEST.MAX_SIZE so that the maximum dimension is not greater than its value.
    • --pyramid Performs the detection three times on scales [0.5, 1, 2] of the image and combines the result.

For example to detect the faces in images of data/demo:

./experiments/scripts/test_single.sh facemagnet data/demo/demo0.jpg --pyramid

Reproducing the results

  • To produce the results of each row of Table 1, use:

    ./experiments/scripts/test_min800.sh <model-name>
    
  • To perform the benchmark test on WIDER-Face:

    ./experiments/scripts/test_wider.sh
    
  • To test the fddb and pascal datasets:

    • Pascal image list can be found in data/pascal_imglist.csv
    • Download FDDB and link it to data/fddb.
    • Prepare Pascal Faces and put it under data/pascal.
    • To get the detections:
      ./experiments/scripts/test_base.sh VGG16 {fddb|pascal} <model-name> 38000 ./experiments/cfgs/min800.yml
      
    • To produce eval plots see tools/marcopede-face_eval.
    • Take a look at and use ./experiments/scripts/plot_fddb.sh for FDDB.

Notes

  • The results are reported using two NVIDIA P6000 GPU with 24GB GPU memory. Big images need a higher memory because of the sizes of the convolutional feature maps, therefore if you're facing memory issues try setting cfg.TEST.MAX_SIZE to a smaller number.
TODOs:
  • Uploading the models.
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