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Model zoo

In our experiments, we re-trained a set of models with the harmonization loss proposed in the paper. You can easily download the weights of each models here:

In order to load them easily, we have set up utilities in the github repository. For example, to load the model lives harmonized:

from harmonization.models import (load_ViT_B16, load_ResNet50,
                                  load_VGG16, load_EfficientNetB0,
                                  load_tiny_ConvNeXT, load_tiny_MaxViT,

vit_harmonized = load_ViT_B16()
vgg_harmonized = load_VGG16()
resnet_harmonized = load_ResNet50()
efficient_harmonized = load_EfficientNetB0()
convnext_harmonized = load_tiny_ConvNeXT()
maxvit_harmonized = load_tiny_MaxViT()
levit_harmonized = load_LeViT_small()

# load images (in [0, 255])
# ...

images = preprocess_input(images)
predictions = vit_harmonized(images)