Google Cloud AutoML is pretty cool, in that it allows developers to easily train custom machine learning models for vision, natural language, and translation without writing model code.

We did fair justice to the earlier short piece about what AutoML really is, and today, we’ll look into getting started with Google Cloud AutoML Vision in this tutorial, and build a model to help classify images.

The steps are straightforward:

Let’s dive in

Prepare image dataset

I used to really like Wilson’s lemonade, so I have downloaded a number of their three different lemonade types (about thirty of each), and labelled them accordingly.

dataset folder

Upload labelled images

Head over to console.cloud.google.com/vision

Go ahead and create a project. You’ll need to enable billing to activate AutoML in your console. Under the AutoML options, click the Image Classification arrow to get started. classifying images

Go on to enable the API

You should see the Datasets' interface now

Next, let’s go ahead to create a new dataset. I have named my dataset “wilsons_lemonade”, and selected the model objective to be single-labelled.

classifying images

The difference between a Single-Label classification and Multiple-Label is that while a single-label classification takes on the assumption that each sample is only one label, the multiple-label classification allocates each sample to a set of labels.

There’s only one output in single-label classification as expected, whereas the outputs in multiple-label must be the exact number of labels.

Next, go ahead to import the images folder, right after selecting it’s destination on your cloud storage. classifying images

After importing the images, you can eventually see them listed as below. You can easily add new labels (and images) from here. classifying images

Train the model

Next, let’s train our model with the uploaded images.

In the ‘Train’ section, you’ll find how that AutoML Vision has automatically segmented the images into training, validation, and testing — of which training and validation images are using during the period of training, while the model is then tested on the test images. classifying images

Before training, AutoML requires to determine whether the model is to be cloud-hosted or for edge (downloadable, etc) purposes. This is so that the chosen option is which would be made available after the model has been trained.

Evaluate model performance

After training (usually up to an hour or much more), under the ‘Evaluation’ section, you’ll find the evaluation metrics. In my case, there are no false negatives — which may have happened in cases where there aren’t enough images for one label, and the confusion matrix looks good. confusion matrix

Use/export model

What follows after this is how you want to use your model, and the options from AutoML Vision are: