Pipeline: Image Embedding using Resnet50

Authors: derekdqc

Overview

The pipeline is used to extract the feature vector of a given image. It uses Resnet50 model to generate the vector.

Interface

Input Arguments:

• img_path:
  • path to the input image
  • 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: (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

>>> embedding_pipeline = pipeline('towhee/image-embedding-resnet50')
>>> embedding = embedding_pipeline('path/to/your/image') #such as './readme_res/pipeline.png'

How it works

This pipeline includes one operator: image embedding (implemented as towhee/resnet-image-embedding). The image will be encoded via image embedding operator, then we can get a feature vector of the given image.

Refer Towhee architecture for basic concepts in Towhee: pipeline, operator, dataframe.