Image Embedding with ISC

author: Jael Gu

Desription

An image embedding operator generates a vector given an image. This operator extracts features for image top ranked models from Image Similarity Challenge 2021 - Descriptor Track. The default pretrained model weights are from The 1st Place Solution of ISC21 (Descriptor Track).

Code Example

Load an image from path './towhee.jpg' and use the pretrained ISC model ('resnet50') to generate an image embedding.

Write the pipeline in simplified style:

import towhee

(
    towhee.glob('./towhee.jpg')
          .image_decode()
          .image_embedding.isc()
          .show()
)

Write a same pipeline with explicit inputs/outputs name specifications:

import towhee

(
    towhee.glob['path']('./towhee.jpg')
          .image_decode['path', 'img']()
          .image_embedding.isc['img', 'vec']()
          .select['img', 'vec']()
          .show()
)

Factory Constructor

Create the operator via the following factory method

image_embedding.isc(skip_preprocess=False, device=None)

Parameters:

skip_preprocess: bool

The flag to control whether to skip image preprocess. The default value is False. If set to True, it will skip image preprocessing steps (transforms). In this case, input image data must be prepared in advance in order to properly fit the model.

device: str

The device to run this operator, defaults to None. When it is None, 'cuda' will be used if it is available, otherwise 'cpu' is used.

Interface

An image embedding operator takes a towhee image as input. It uses the pre-trained model specified by model name to generate an image embedding in ndarray.

Parameters:

img: towhee.types.Image (a sub-class of numpy.ndarray)

The decoded image data in numpy.ndarray.

Returns: numpy.ndarray

The image embedding extracted by model.