Cross-domain correspondence network is a exemplar-based image translation model, consisting of correspondence and translation parts. Model was pre-trained on ADE20k dataset. For details see paper and repository.
Metrics were calculated between generated images by model and real validation images from ADE20k dataset. For some GAN metrics (IS and FID) you need to use classification model as verification network. In our case it is Inception-V3 model.
|Metric||Original model||Converted model|
name: "ref_image", shape: [1x3x256x256] - An reference image (exemplar) in the format [BxCxHxW], where:
Expected color order is BGR (At original model expected color order is RGB).
Image, name: "exemplar_based_output", shape: [1x3x256x256] - A result (generated) image based on exemplar in the format [BxCxHxW], where:
Output color order is RGB.
The original model is distributed under the following license:
The Synchronized-BatchNorm-PyTorch (dependency for model) is distributed under the following license:
''' MIT License
Copyright (c) 2018 Jiayuan MAO
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