Pipeline: Image Embedding using resnet50

Authors: Filip

Overview

The pipeline is used to extract the feature vector of a given image. It uses the the resnet50 model from Ross Wightman's timm to generate the vector.

Interface

Input Arguments:

• img_path:
  • the input image path
  • supported types: str

Pipeline Output:

The pipeline returns a tuple Tuple[('feature_vector', numpy.ndarray)] containing following fields:

• feature_vector:
  • the embedding of input image
  • data type: numpy.ndarray
  • shape: (1, 2048)

How to use

1. Install Towhee

$ pip3 install towhee

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.

2. Run it with Towhee

>>> from towhee import pipeline

>>> img_path = 'path/to/your/image'
>>> embedding_pipeline = pipeline('towhee/image-embedding-resnet50')
>>> embedding = embedding_pipeline(img_path)

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.
• Building Production Ready Search Pipelines with Spark and Milvus - Zilliz blog: A step-by-step process to build an efficient and production-ready vector search pipeline using Databricks Spark and Milvus.
• 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
• 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.
• 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