Turi Image Similarity

DEPRECATION WARNING

This code example was intended for use by the legacy Skafos platform and is no longer being maintained. On 05/29/2019, a new version of Skafos was released, streamlining model delivery to the edge.

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The following repo contains code for training an image similarity model on Skafos using the Turi Create framework. The example model is based on images from the popular open source images data set Caltech 101, containing images from 101 different categories. To get a list of these categories, check out the final page of this research paper

What is here?

The components of this repo are:

image_similarity.ipynb - a Python notebook that trains and saves an image similarity model to use in your app. Start here.
utilities/ - a directory that contains helper functions used by image_similarity.ipynb.
advanced-usage/ - a directory that contains additional information about this image similarity model, how to incorporate your own data, advanced usage, and more.
requirements.txt - a file describing all required Python dependencies.

About the model

The data used to train this example model are 9,145 images from 101 different categories. This data set can be found here. To get a list of these categories, check out the final page of this research paper
Once trained, you can give the model an image, and it will identify images similar to the photo you've provided. The model will perform better on new images that fall into the 101 categories the model was trained on.
To retrain this image similarity model on new data, we highly recommend doing it on a GPU. As benchmarks, we've found that training this Turi Create image similarity model takes about 60 minutes on a GPU and about a day and a half on Skafos with 6 CPU's and 10G of memory. Read more about requesting a GPU.

Going beyond the example

If you wish to incorporate your own data or try another type of image similarity model, check out the advanced_usage/ section.
Similar to Turi Create's image classifier model, Turi Create's image similarity uses model='resnet-50' by default. Using this model can result in a large .mlmodel file. If you are concerned about model size, using a smaller pre-trained model might help conserve memory without sacrificing a significant amount of accuracy. For more about this, read here.

Need Help?

Please contact us with questions or feedback! Here are two ways:

Also check out Turi Create's documentation on image similarity basics.

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