Hall of Faces: a face detection model zoo

A collection of face detection models pre-trained on the Widerface dataset.

In the table below you can see each model detailed information including:

• meta architecture name
• model speed
• detector performance measured on the FDDB benchmark
• a download link to a tar.gz file containing the model and configuration files
• a link for a live demo running on a Google Colaboratory notebook

Architecture	Speed (ms)	mAP@0.5	Cfg/Weights	Demo
R-FCN resnet101	92	94.73	link	colab
Faster R-CNN inception resnet v2 atrous	620	94.39	link	colab
SSD mobilenet v1	30	91.20	link	colab
YOLOv2	15	89.59	link	colab
TinyYolo	5	85.5	link	colab

Face detectors performance evaluation on the FDDB dataset

Discrete ROC

Continuous ROC

Training details

Morghulis was used to download and convert it to either Darknet or Tensorflow Object Detection API format.

Tensorflow Object Detection API

The remaining models were trained with Tensorflow Object Detection API on Google Cloud ML Engine.

Darknet

There are 2 models trained with Darknet: one based on YOLOv2 and other on Tiny YOLO. Both used convolutional weights that are pre-trained on Imagenet: darknet19_448.conv.23.