README.md

Image classification

Given an image, the goal of an image classifier is to assign it to one of a pre-determined number of labels. Deep learning methods have recently been shown to give incredible results on this challenging problem. Yet this comes at the cost of extreme sensitivity to model hyper-parameters and long training time. This means that one can spend months testing different model configurations, much too long to be worth the effort. However, the image classifier in Turi Create is designed to minimize these pains, and making it possible to easily create a high quality image classifier model.

Loading Data

Suppose we have a dataset containing labeled cat and dog images.

import turicreate as tc

# Load images
data = tc.image_analysis.load_images('train', with_path=True)

# From the path-name, create a label column
data['label'] = data['path'].apply(lambda path: 'dog' if 'dog' in path else 'cat')

# Save the data for future use
data.save('cats-dogs.sframe')

# Explore interactively
data.explore()

Introductory Example

The task is to predict if a picture is a cat or a dog. Let’s explore the use of the image classifier on the Cats vs. Dogs dataset.

import turicreate as tc

# Load the data
data =  tc.SFrame('cats-dogs.sframe')

# Make a train-test split
train_data, test_data = data.random_split(0.8)

# Automatically picks the right model based on your data.
model = tc.image_classifier.create(train_data, target='label')

# Save predictions to an SArray
predictions = model.predict(test_data)

# Evaluate the model and save the results into a dictionary
metrics = model.evaluate(test_data)
print(metrics['accuracy'])

# Save the model for later use in Turi Create
model.save('mymodel.model')

# Export for use in Core ML
model.export_coreml('MyCustomImageClassifier.mlmodel')

Here are some predictions on our own favorite cats and dogs:

new_cats_dogs['predictions'] = model.predict(new_cats_dogs)

Advanced Usage

Refer to the following chapters for:

• Advanced usage options including the use of GPUs and deployment to device.
• Techincal details on how the image classifier works

In addition, the following chapters contain more information on how to use classifiers:

• Accessing attributes of the model
• Evaluating Results
• Multiclass Classification