mks0601 / A Convolutional Neural Network Cascade For Face Detection
TensorFlow implementation of "A Convolutional Neural Network Cascade for Face Detection", CVPR 2015
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A Convolutional Neural Network Cascade for Face Detection
This repo is re-implementation of the paper in TensorFlow.
Start
Preparing data
-
Download AFLW dataset (positive) and COCO dataset (negative) for training. Any other dataset can be used instead of COCO datset for negative one.
-
Download FDDB dataset for testing.
-
Run
data_parse.pyin thedataGenfolder before training and testing network.
Training classification net
12-net: python train_12net.py
24-net: python train_24net.py
48-net: python train_48net.py
Training calibration net
12-calib net: python train_calib.py 12
24-calib net: python train_calib.py 24
48-calib net: python train_calib.py 48
Hard negative mining(save hard neg db to disk in neg_train/neg_hard/)
hard neg db to train 24-net: python hard_neg_mining.py 24
hard neg db to train 48-net: python hard_neg_mining.py 48
Test
python test.py
Implementation
Implemented with TensorFlow and yields similar result with paper
training set: AFLW dataset(positive), COCO dataset(negative)
test set: FDDB dataset
Result(gren: GT, blue: detected face)
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