create(style_dataset, content_dataset, style_feature=None, content_feature=None, max_iterations=None, model='resnet-16', verbose=True, batch_size=1, **kwargs)¶
- style_dataset: SFrame
Input style images. The columns named by the
style_featureparameters will be extracted for training the model.
- content_dataset : SFrame
Input content images. The columns named by the
content_featureparameters will be extracted for training the model.
- style_feature: string
Name of the column containing the input images in style SFrame. ‘None’ (the default) indicates the only image column in the style SFrame should be used as the feature.
- content_feature: string
Name of the column containing the input images in content SFrame. ‘None’ (the default) indicates the only image column in the content SFrame should be used as the feature.
- max_iterations : int
The number of training iterations. If ‘None’ (the default), then it will be automatically determined based on the amount of data you provide.
- model : string optional
Style transfer model to use:
- “resnet-16” : Fast and small-sized residual network that uses
- VGG-16 as reference network during training.
- batch_size : int, optional
If you are getting memory errors, try decreasing this value. If you have a powerful computer, increasing this value may improve training throughput.
- verbose : bool, optional
If True, print progress updates and model details.
- out : StyleTransfer
# Create datasets >>> content_dataset = turicreate.image_analysis.load_images('content_images/') >>> style_dataset = turicreate.image_analysis.load_images('style_images/') # Train a style transfer model >>> model = turicreate.style_transfer.create(content_dataset, style_dataset) # Stylize an image on all styles >>> stylized_images = model.stylize(data) # Visualize the stylized images >>> stylized_images.explore()