RetinaFace Face Detection

author: David Wang

Description

This operator detects faces in the images by using RetinaFace Detector. It will return the bounding box positions and the confidence scores of detected faces. This repository is an adaptation from biubug6/Pytorch_Retinaface.

Code Example

Load an image from path './turing.png' and use the pre-trained RetinaFace model to generate face bounding boxes and confidence scores.

Write a pipeline with explicit inputs/outputs name specifications:

from towhee import pipe, ops, DataCollection

p = (
    pipe.input('path')
        .map('path', 'img', ops.image_decode())
        .map('img', ('bbox','score'), ops.face_detection.retinaface())
        .map(('img', 'bbox'),'crop', ops.image_crop())
        .output('img', 'crop', 'bbox', 'score')
)

DataCollection(p('turing.png')).show()

Factory Constructor

Create the operator via the following factory method:

face_detection.retinaface()

Interface

A face detection operator takes an image as input. It generates the bounding box positions and confidence scores in ndarray.

Parameters:

​ img: towhee.types.Image (a sub-class of numpy.ndarray)

​ the image to detect faces from.

​ supported types: numpy.ndarray

Returns:

List[(int, int, int, int)]

​ The position of the bounding boxes for the faces detected.

List[float]

​ The confidence scores for the face detected in the bounding boxes.

More Resources

• Zilliz Triumphed in Billion-Scale ANN Search Challenge of NeurIPS 2021 - Zilliz Newsroom; Zilliz Triumphed in Billion-Scale ANN Search Challenge of NeurIPS 2021: Zilliz team has won the first place in the Disk-based ANN Search track in NeurIPS 2021. The performance of BBAnn developed by Zilliz research team peaked during the search in the SimSearchNet++ dataset.
• Hugging Face Inference Endpoints & Zilliz Cloud: nan
• Understanding Computer Vision - Zilliz blog: Computer Vision is a field of Artificial Intelligence that enables machines to capture and interpret visual information from the world just like humans do.
• What is a Convolutional Neural Network? An Engineer's Guide: Convolutional Neural Network is a type of deep neural network that processes images, speeches, and videos. Let's find out more about CNN.
• Using Vector Search to Better Understand Computer Vision Data - Zilliz blog: How Vector Search improves your understanding of Computer Vision Data
• Comparing Vector Databases: Milvus vs. Chroma DB - Zilliz blog: Comparing Milvus and Chroma vector database regarding the scalability, functionality, ease of use, and purpose-built features.
• What are Vision Transformers (ViT)? - Zilliz blog: Vision Transformers (ViTs) are neural network models that use transformers to perform computer vision tasks like object detection and image classification.
• Zilliz partnership with PyTorch - View image search solution tutorial: Zilliz partnership with PyTorch