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Tutorial 3B: Object detection annotation task

Overview​

This tutorial describes the process of creating an object detection 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.

Multiple objects: Leftmost side is a parked red volkswagen vehicle, to the right is a red cup of coffee, next object is a mug raised with a sunrise as background, the rightmost object is Formula 1 race car

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 object detection annotation task refers to specifying one or more object classes (labels) to each input image. Let's create an annotation task.

  1. Click New annotation task.
  2. In the Task name box, enter tutorial-3b.
  3. In the Task description box, enter Annotate a dataset containing images of cars and coffee.
  4. In the Select task list, select Object detection.
  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 labels (object classes) we want to specify, car and coffee. Let's define the annotation task rubric.

  1. In the Add new object class box, enter car.
  2. Click Add object class.
  3. In the Add new object class box, enter coffee.
  4. Click Add.
  5. Click Continue to annotate.

Annotation task rubric configuration of Car and Coffee classes

Step 4: Annotate dataset​

Now that we have specified the annotation task rubric, let's annotate the dataset (the first image).

  1. A zero-shot learning model is on by default when you annotate an object detection annotation task. The model accelerates the annotation (labeling) process by capturing the object observed in the image with a bounding box. 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. Refresh
    info

    To learn about the utilized model for an object detection annotation task, see Zero-shot learning models: Object detection.

  2. Click Refresh. Annotated dataset with selection of Car class
    note

    If the zero-shot learning model did not accurately capture the object on the image, inside the bounding box, double-click inside the bounding box. Double-clicking inside the bounding box enables you to move and resize the bounding box to accurately cover the area the car (object) takes on the image.

  3. 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 annotate
  4. 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 object detection 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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