Tutorial 1B: Image classification annotation task

Overview

This tutorial describes the process of creating an image classification annotation task, including specifying an annotation task rubric for it. To highlight the process, we are going to annotate a dataset that contains images of cars and coffee.

Step 1: Explore dataset

We are going to use the preloaded Car or coffee demo dataset for this tutorial. The dataset contains 40 images, each depicting a car or coffee. Let's quickly explore the dataset.

1. On the H2O Label Genie navigation menu, click Datasets.
2. In the Datasets table, click car-or-coffee-demo.

Step 2: Create an annotation task

Now that we have seen the dataset let's create an annotation task that enables you to annotate the dataset. For this tutorial, an image classification annotation task refers to assigning one or more categorical target labels to an input image. Let's create an annotation task.

1. Click New annotation task.
2. In the Task name box, enter tutorial-1b.
3. In the Task description box, enter Annotate a dataset containing images of cars and coffee.
4. In the Select task list, select Classification.
5. Click Create task.

Step 3: Specify an annotation task rubric

Before we can start annotating our dataset, we need to specify an annotation task rubric. An annotation task rubric refers to the labels (for example, object classes) you want to use when annotating your dataset. For our dataset, there are two categorical target labels we want to specify, car and coffee. Let's define the annotation task rubric.

1. In the New class name box, enter car.
2. Click Add.
3. Click Add class.
4. In the New class name box, enter coffee.
5. Click Add.
6. Click Continue to annotate.

note

H2O Label Genie supports multi-label image classification annotation tasks.

Step 4: Annotate dataset

In the Annotate tab, you can individually annotate each image in the dataset. Let's annotate the first image.

1. Select coffee or car.
   note

   • A zero-shot learning model is on by default when you annotate an image classification annotation task. The model accelerates the annotation (labeling) process by providing the percentage probability of an image (in this case, a car or coffee image) belonging to a certain label (one of the labels created in the Rubric tab). You can immediately start annotating in the Annotate tab or wait until the zero-shot model is ready to provide annotation suggestions. H2O Label Genie notifies you to Refresh the instance when zero-shot predictions (suggestions) are available.
     • During the annotation process of an image classification dataset, you can download generated zero-shot predictions (probabilities) in the Export tab. To learn more, see Download a dataset's zero-shot predictions.
     • For example, after refreshing the instance in this tutorial, the model provided probabilities for each label.
     • To learn about the utilized model for an image classification annotation task, see Zero-shot learning models: Image classification.
2. Click Save and next.
   Note

   • Save and next saves the annotated image
   • To skip an image to annotate later: Click Skip.
     • Skipped images (samples) reappear after all non-skipped images are annotated
3. Annotate all dataset samples.
   note

   At any point in an annotation task, you can download the already annotated (approved) samples. You do not need to fully annotate an imported dataset to download already annotated samples. To learn more, see Download an annotated dataset.

Summary

In this tutorial, we learned the process of annotating and specifying an annotation task rubric for an image classification annotation task.

Next

To learn the process of annotating and specifying an annotation task rubric for other various annotation tasks in computer vision (CV), natural language processing (NLP), and audio, see Tutorials.

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