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1.5 KiB
Image Embedding Pipeline with Resnet50
Authors: derekdqc, shiyu22
Overview
This pipeline is used to extract the feature vector of the image. First step is to normalize the image, and then use resnet50 model to generate the vector.
Interface
Args:
img_tensor(PIL.Image):
The image to be encoded.
Returns:
(Tuple[('cnn', numpy.ndarray)])
The embedding of the image.
How to use
- 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.
- Run it with Towhee
>>> from towhee import pipeline
>>> from PIL import Image
>>> img = Image.open('path/to/your/image') # for example, './test_data/test.jpg'
>>> embedding_pipeline = pipeline('towhee/image-embedding-resnet50')
>>> embedding = embedding_pipeline(img)
How it works
You can learn the pipeline and operator in Towhee architecture. This pipeline includes two main functions: towhee/transform-image and towhee/resnet50-image-embedding. It is necessary to ensure that the input and output of the four Operators correspond to each other, and the input and output data types can be defined by DataFrame.
1.5 KiB
Image Embedding Pipeline with Resnet50
Authors: derekdqc, shiyu22
Overview
This pipeline is used to extract the feature vector of the image. First step is to normalize the image, and then use resnet50 model to generate the vector.
Interface
Args:
img_tensor(PIL.Image):
The image to be encoded.
Returns:
(Tuple[('cnn', numpy.ndarray)])
The embedding of the image.
How to use
- 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.
- Run it with Towhee
>>> from towhee import pipeline
>>> from PIL import Image
>>> img = Image.open('path/to/your/image') # for example, './test_data/test.jpg'
>>> embedding_pipeline = pipeline('towhee/image-embedding-resnet50')
>>> embedding = embedding_pipeline(img)
How it works
You can learn the pipeline and operator in Towhee architecture. This pipeline includes two main functions: towhee/transform-image and towhee/resnet50-image-embedding. It is necessary to ensure that the input and output of the four Operators correspond to each other, and the input and output data types can be defined by DataFrame.