Face Detector
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README.md
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ssd_inception_v2_fddb.config.base

README.md

TF Face Detector

Face Detector using Tensorflow Object Detection API

Web DEMO: https://tf-face-detector.herokuapp.com/

Prerequisite

  • Python >= 3.x
    • TensorFlow >= 1.2
    • Pillow >= 4.2.1 (for visualizing results)
    • cv2 >= 3.3 (for generating dataset)

Setup

git submodule update --init
pip3 install -r requirements.txt

FDDB dataset

http://vis-www.cs.umass.edu/fddb/

To download data and generate tfrecord dataset (needed cv2):

python data/fddb.py

Training

perl -pe "s|PATH_TO_BE_CONFIGURED|${PWD}/data|g" ./ssd_inception_v2_fddb.config.base > ssd_inception_v2_fddb.config
(cd models && protoc object_detection/protos/*.proto --python_out=.)
export PYTHONPATH=$PYTHONPATH:`pwd`:`pwd`/slim
python object_detection/train.py \
    --train_dir=../train \
    --pipeline_config_path=../ssd_inception_v2_fddb.config

or

./run_local.sh

Export graph

export PYTHONPATH=${PYTHONPATH}:$(pwd)/models:$(pwd)/models/slim
export CHECKPOINT_NUMBER=<target checkpoint number>
export EXPORT_DIRECTORY=<path to output graph>
python models/object_detection/export_inference_graph.py \
    --input_type=encoded_image_string_tensor \
    --pipeline_config_path=ssd_inception_v2_fddb.config \
    --trained_checkpoint_prefix=train/model.ckpt-${CHECKPOINT_NUMBER} \
    --output_directory=${EXPORT_DIRECTORY}