inceptionresnetv1/README.md

Inception-ResNet v1 Face Embedding Operator

author: David Wang

Description

This operator extracts embedding vector from facial image using Inception-ResNet. The implementation is an adaptation from timesler/facenet-pytorch.

Code Example

Extract face image embedding from './img.png'.

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.inceptionresnetv1())
        .output('img', 'vec')
)

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

Factory Constructor

Create the operator via the following factory method:

face_embedding.inceptionresnetv1(image_size = 160)

Parameters:

image_size: int

Scaled input image size to extract embedding. The higher resolution would generate the more discriminative feature but cost more time to calculate.

supported types: int, default is 160.

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.

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.
• 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.
• Sparse and Dense Embeddings: A Guide for Effective Information Retrieval with Milvus | Zilliz Webinar: Zilliz webinar covering what sparse and dense embeddings are and when you'd want to use one over the other.
• 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.
• Image Embeddings for Enhanced Image Search - Zilliz blog: Image Embeddings are the core of modern computer vision algorithms. Understand their implementation and use cases and explore different image embedding models.
• 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