MobileFaceNet Face Landmark Detector

author: David Wang

Description

MobileFaceNets is a class of extremely efficient CNN models to extract 68 landmarks from a facial image. It use less than 1 million parameters and is specifically tailored for high-accuracy real-time face verification on mobile and embedded devices. This repository is an adaptation from cuijian/pytorch_face_landmark.

Code Example

Extract facial landmarks from './img1.jpg'.

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', 'landmark', ops.face_landmark_detection.mobilefacenet())
        .output('img', 'landmark')
)

DataCollection(p('./img1.jpg')).show()

Factory Constructor

Create the operator via the following factory method:

face_landmark_detection.mobilefacenet(pretrained = True)

Parameters:

​ pretrained

​ whether load the pre-trained weights.

​ supported types: bool, default is True, using pre-trained weights.

Interface

An image embedding operator takes an image as input. it extracts the embedding as ndarray.

Parameters:

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

​ The input image.

Returns: numpy.ndarray

​ The extracted facial landmarks.

More Resources

• Approximate Nearest Neighbors Oh Yeah (Annoy) - Zilliz blog: Discover the capabilities of Annoy, an innovative algorithm revolutionizing approximate nearest neighbor searches for enhanced efficiency and precision.
• How to Get the Right Vector Embeddings - Zilliz blog: A comprehensive introduction to vector embeddings and how to generate them with popular open-source models.
• Hugging Face Inference Endpoints & Zilliz Cloud: nan
• Transforming Text: The Rise of Sentence Transformers in NLP - Zilliz blog: Everything you need to know about the Transformers model, exploring its architecture, implementation, and limitations
• Understanding ImageNet: A Key Resource for Computer Vision and AI Research: The large-scale image database with over 14 million annotated images. Learn how this dataset supports advancements in computer vision.
• Build a Multimodal Search System with Milvus - Zilliz blog: Implementing a Multimodal Similarity Search System Using Milvus, Radient, ImageBind, and Meta-Chameleon-7b
• Zilliz-Hugging Face partnership - Explore transformer data model repo: Use Hugging Face’s community-driven repository of data models to convert unstructured data into embeddings to store in Zilliz Cloud, and access a code tutorial.