June 17, 2018
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This is a minimal example to show you how CreateML works. We will create a CoreML model that is able to detect bananas and tomatoes.

🏋️‍♀️ Training Data

First, we need data to train the model with. CreateML makes labeling very easy: Create a new folder for your images. In this folder, create two new folders: one named banana, the other one named tomato. Now download a bunch of images from the internet (search for “banana” or “tomato”) and put them into their according folders. The result should look like this:

Folder

Setup Xcode Playground

Now, got to Xcode and select File ➡️ New ➡️ Playground ➡️ macOS ➡️ blank.

create new playground

Please note: Xcode 10 and macOS 10.14 Mojave are required to run CreateML.

👩‍💻 Code

To show the CreateML drag and drop user interface we need to write some very minimal code:

import CreateMLUI

let builder = MLImageClassifierBuilder()
builder.showInLiveView()

This imports the framework and generates the user interface (.showInLiveView).

Now, run your code!

Training

A new UI should appear in the assistant editor / live view. You can now drag the image folder from earlier into this UI – CreateML will start training with your data immediately. After a few seconds the process is complete and you can see the results in the console: result

+------------------+--------------+------------------+
| Images Processed | Elapsed Time | Percent Complete |
+------------------+--------------+------------------+
| 1                | 7.64s        | 5.75%            |
| 2                | 7.69s        | 11.75%           |
| 3                | 7.74s        | 17.5%            |
| 4                | 7.79s        | 23.5%            |
| 5                | 7.83s        | 29.25%           |
| 10               | 8.19s        | 58.75%           |
| 17               | 8.48s        | 100%             |
+------------------+--------------+------------------+
Skipping automatic creation of validation set; training set has fewer than 50 points.
Beginning model training on processed features.
Calibrating solver; this may take some time.
+-----------+--------------+-------------------+
| Iteration | Elapsed Time | Training-accuracy |
+-----------+--------------+-------------------+
| 1         | 0.033924     | 1.000000          |
+-----------+--------------+-------------------+
SUCCESS: Optimal solution found.
Trained model successfully saved at /var/folders/77/d8w7sf5n1wxd9g33wtjq9wy40000gn/T//ImageClassifier.mlmodel.

🚀 Executing and Testing the Model

To see how well the model performs we can test it with some images that it has never seen before. This is important to verify that the model is not overfitted on our training data!

Just download some more banana and tomato images and drag them onto the model: testing

As you can see, both bananas and tomatoes are classified correctly…yaayy!

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