deepface/README.md

Face Embeddings using Deepface

Author: Krishna katyal

Description

The pipeline is used to extract the feature vector of detected faces in images. It uses the for face embeddings Deepface.

Code Example

Load an image from path './test_face.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', 'vec', ops.face_embedding.deepface(model_name = 'DeepFace'))
        .output('img', 'vec')
)

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

Factory Constructor

Create the operator via the following factory method

face_embedding.deepface(model_name = 'which model to use')

Model options:

• VGG-Face
• FaceNet
• OpenFace
• DeepFace
• ArcFace
• Dlib
• DeepID
• Facenet512

Interface

A face embedding operator takes a face image as input. It extracts the embedding in ndarray.

Parameters:

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

​ The input image.

Returns: numpy.ndarray

​ The extracted image embedding.

Reference

https://github.com/serengil/deepface

You can refer to Getting Started with Towhee for more details. If you have any questions, you can submit an issue to the towhee repository.

More Resources

• Exploring Multimodal Embeddings with FiftyOne and Milvus - Zilliz blog: This post explored how multimodal embeddings work with Voxel51 and Milvus.
• 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.
• The guide to clip-vit-base-patch32 | OpenAI: clip-vit-base-patch32: a CLIP multimodal model variant by OpenAI for image and text embedding.
• Using Vector Search to Better Understand Computer Vision Data - Zilliz blog: How Vector Search improves your understanding of Computer Vision Data
• Sparse and Dense Embeddings - Zilliz blog: Learn about sparse and dense embeddings, their use cases, and a text classification example using these embeddings.
• Understanding Neural Network Embeddings - Zilliz blog: This article is dedicated to going a bit more in-depth into embeddings/embedding vectors, along with how they are used in modern ML algorithms and pipelines.
• Enhancing Information Retrieval with Sparse Embeddings | Zilliz Learn - Zilliz blog: Explore the inner workings, advantages, and practical applications of learned sparse embeddings with the Milvus vector database
• An Introduction to Vector Embeddings: What They Are and How to Use Them - Zilliz blog: In this blog post, we will understand the concept of vector embeddings and explore its applications, best practices, and tools for working with embeddings.