Skip to main content
Version: v0.2.0

Tutorial 3B: Annotation task: Object detection

This tutorial will underline the steps (process) of annotating and specifying an annotation task rubric for an object detection annotation task. This tutorial will also quickly explore how you can download the fully annotated dataset supported in H2O Hydrogen Torch.

Step 1: Explore dataset​

We will use the preloaded car-or-coffee-demo 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 Label Genie navigation menu, click Datasets.
  2. In the datasets table, click car-or-coffee-demo.

car-or-coffee-sample

Step 2: Create annotation task​

Now that we have seen the dataset let's create an annotation task that will enable you to annotate the dataset. An annotation task refers to the process of labeling data. 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 dataset containing images of cars and coffee.
  4. In the Select task list, select Object detection.
  5. Click Create task.

Step 3: Specify 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 (e.g., 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 Object class name box, enter Car.
  2. For the car class color, choose an option.
  3. Click Add object class.
  4. In the new Object class name box, enter Coffee.
  5. For the coffee class color, choose an option.

Annotation task rubric

Step 4: Annotate dataset​

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

  1. Click Continue to annotate.

In the Annotate tab, you can individually annotate each image in the dataset. Let's annotate 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. In particular, the zero-shot model selects the fitting label while indicating where you can observe the label object in the image. In this case, "fitting" refers to the zero-shot learning model's calculations. In this case, the model has selected the Car label while indicating where the car is located in the image.

    • To learn how to turn off the zero-shot learning model, see Smart annotation.
      Note

      As you annotate the dataset, you can select which label (object class) to use depending on whether the image depicts a car or coffee. To select the Coffee or Car object class: Click Car or Coffee.

  2. If the zero-shot learning model did not accurately capture the object on the image, inside the bounding box, double-click. 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) will reappear after all non-skipped images are annotate

Export annotated dataset​

After annotating all the images, you can download the dataset in a format that H2O Hydrogen Torch supports. Let's download the annotated dataset.

  1. In the Annotate tab, click Export annotated samples.

    Export annotated samples

    Note
    • In the Dashboard tab, you can download all annotated images at any point in the annotation process of a dataset. To download all annotated images so far, consider the following instructions:
      1. Click the Dashboard tab.
      2. Click Export approved samples.
    • H2O Label Genie will download a .zip file containing the annotated dataset in a format aligning with the dataset's problem type (annotation task type). To learn more, see Downloaded dataset formats: Object detection.

Summary​

In this tutorial, we learned the process of annotating and specifying an annotation task rubric for an object detection annotation task. We also learned how to download a fully annotated dataset supported in H2O Hydrogen Torch.

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.

Feedback