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Updated 1 year ago


Pipeline: Image Embedding using resnet50

Authors: Filip


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.


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.

  1. 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)
Filip 1ee99d169d upload 28 Commits
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