turicreate.image_similarity.create

turicreate.image_similarity.create(dataset, label=None, feature=None, model='resnet-50', verbose=True, batch_size=64)

Create a ImageSimilarityModel model.

Parameters:
dataset : SFrame

Input data. The column named by the ‘feature’ parameter will be extracted for modeling.

label : string

Name of the SFrame column with row labels to be used as uuid’s to identify the data. If ‘label’ is set to None, row numbers are used to identify reference dataset rows when the model is queried.

feature : string

Name of the column containing either the input images or extracted features. ‘None’ (the default) indicates that only feature column or the only image column in dataset should be used as the feature.

model: string, optional

Uses a pretrained model to bootstrap an image similarity model

  • “resnet-50” : Uses a pretrained resnet model.
  • “squeezenet_v1.1” : Uses a pretrained squeezenet model.
  • “VisionFeaturePrint_Scene”: Uses an OS internal feature extractor.
    Only on available on iOS 12.0+, macOS 10.14+ and tvOS 12.0+.

Models are downloaded from the internet if not available locally. Once downloaded, the models are cached for future use.

verbose : bool, optional

If True, print progress updates and model details.

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 performance.
Returns:
out : ImageSimilarityModel

A trained ImageSimilarityModel model.

See also

ImageSimilarityModel

Examples

# Train an image similarity model
>>> model = turicreate.image_similarity.create(data)

# Query the model for similar images
>>> similar_images = model.query(data)
+-------------+-----------------+-------------------+------+
| query_label | reference_label |      distance     | rank |
+-------------+-----------------+-------------------+------+
|      0      |        0        |        0.0        |  1   |
|      0      |       519       |   12.5319706301   |  2   |
|      0      |       1619      |   12.5563764596   |  3   |
|      0      |       186       |   12.6132604915   |  4   |
|      0      |       1809      |   12.9180964745   |  5   |
|      1      |        1        | 2.02304872852e-06 |  1   |
|      1      |       1579      |   11.4288186151   |  2   |
|      1      |       1237      |   12.3764325949   |  3   |
|      1      |        80       |   12.7264363676   |  4   |
|      1      |        58       |   12.7675058558   |  5   |
+-------------+-----------------+-------------------+------+
[500 rows x 4 columns]