face-detection 0.1.3

A simple and lightweight package for state of the art face detection with GPU support.

State of the Art Face Detection in Pytorch with DSFD and RetinaFace

This repository includes:

• A High-Performance Pytorch Implementation of the paper "DSFD: Dual Shot Face Detector" (CVPR 2019). adapted from the original source code.
• Lightweight single-shot face detection from the paper RetinaFace: Single-stage Dense Face Localisation in the Wild adapted from https://github.com/biubug6/Pytorch_Retinaface.

NOTE This implementation can only be used for inference of a selection of models and all training scripts are removed. If you want to finetune any models, we recommend you to use the original source code.

Install

You can install this repository with pip (requires python>=3.6);

pip install face_detection

You can also install with the setup.py

python3 setup.py install

Getting started

Run

python3 test.py

This will look for images in the images/ folder, and save the results in the same folder with an ending _out.jpg

Simple API

To perform detection you can simple use the following lines:

import cv2
import face_detection
print(face_detection.available_detectors)
detector = face_detection.build_detector(
  "DSFDDetector", confidence_threshold=.5, nms_iou_threshold=.3)
# BGR to RGB
im = cv2.imread("path_to_im.jpg")[:, :, ::-1]

detections = detector.detect(im)

This will return a tensor with shape [N, 5], where N is number of faces and the five elements are [xmin, ymin, xmax, ymax, detection_confidence]

Batched inference

import numpy as np
import face_detection
print(face_detection.available_detectors)
detector = face_detection.build_detector(
  "DSFDDetector", confidence_threshold=.5, nms_iou_threshold=.3)
# [batch size, height, width, 3]
images_dummy = np.zeros((2, 512, 512, 3))

detections = detector.batched_detect(im)

Improvements

Difference from DSFD

For the original source code, see here.

• Removal of all unnecessary files for training / loading VGG models.
• Improve the inference time by about 30x (from ~6s to 0.2) with rough estimates using time (Measured on a V100-32GB GPU).

The main improvements in inference time comes from:

• Replacing non-maximum-suppression to a highly optimized torchvision version
• Refactoring init_priorsin the SSD model to cache previous prior sizes (no need to generate this per forward pass).
• Refactoring the forward pass in Detect in utils.py to perform confidence thresholding before non-maximum suppression
• Minor changes in the forward pass to use pytorch 1.0 features

Difference from RetinaFace

For the original source code, see here.

We've done the following improvements:

• Remove gradient computation for inference (torch.no_grad).
• Replacing non-maximum-suppression to a highly optimized torchvision version

Inference time

This is very roughly estimated on a 1024x687 image. The reported time is the average over 100 runs. (With no cudnn benchmarking and no fp16 computation).

Model	Device	MS
DSFDDetector	CPU (MacOS Mid '14 15-Inch, Intel 2.2GHz i7)	17,496
DSFDDetector	GPU (1x NVIDIA V100-32GB)	100
RetinaNetResNet50	CPU (MacOS Mid '14 15-Inch, Intel 2.2GHz i7)	3428
RetinaNetResNet50	GPU (1x NVIDIA V100-32GB)
RetinaNetMobileNetV1	CPU (MacOS Mid '14 15-Inch, Intel 2.2GHz i7)	281
RetinaNetMobileNetV1	GPU (1x NVIDIA V100-32GB)

Citation

If you find this code useful, remember to cite the original authors:

@inproceedings{li2018dsfd,
  title={DSFD: Dual Shot Face Detector},
  author={Li, Jian and Wang, Yabiao and Wang, Changan and Tai, Ying and Qian, Jianjun and Yang, Jian and Wang, Chengjie and Li, Jilin and Huang, Feiyue},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  year={2019}
}

@inproceedings{deng2019retinaface,
  title={RetinaFace: Single-stage Dense Face Localisation in the Wild},
  author={Deng, Jiankang and Guo, Jia and Yuxiang, Zhou and Jinke Yu and Irene Kotsia and Zafeiriou, Stefanos},
  booktitle={arxiv},
  year={2019}