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Version: v0.2.0

Tutorial 2B: Annotation task: Image regression

This tutorial will annotate a dataset that will enable you to understand the process of annotating and specifying an annotation task rubric for an image regression 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 plant-pathology-demo demo dataset for this tutorial. The dataset contains 32 images depicting healthy and diseased apple leaves. Let's quickly explore the dataset.

  1. On the Label Genie navigation menu, click Datasets.
  2. In the datasets table, click plant-pathology-demo.

healthy-or-diseased-apple-leaves

Step 2: Create annotation task​

Now that we understand 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 example, an image regression annotation task refers to assigning one continuous target label to each input image. Let's create an annotation task.

  1. Click New annotation task.
  2. In the Task name box, enter Tutorial 5A.
  3. In the Task description box, enter Annotate dataset containing images of healthy and diseased apple leaves.
  4. In the Select task list, select Regression.
  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 a dataset. We will label each image on a scale between 0 to 9 to rank the severity of the plant disease. A 0 refers to the plant having the least disease severity, and 9 refers to the plant having the most disease severity.

  1. In the Data minimum value box, enter 0.
    • The Data minimum value value refers to the minimum value in your continuous values (digits ranging between 0 to 9)
  2. In the Data maximum value box, enter 9.
    • The Data maximum value value refers to the maximum value in your continuous values (digits ranging between 0 to 9)
  3. In the Data step size (interval) box, enter 1.
    • The Data step size (interval) value refers to the value the label range slider interval will take (the slider is utilized in step 4) image-regression-annotate-tab
  4. In the Annotation selection list, select Slider. 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. In the Label slider, slide to label 3. annotate-dataset
    Note

    As you annotate the dataset, you can use the slider to attribute a value to a particular image.

  2. Click Save and next.
    Note
    • Save and next saves the annotated image
    • To skip an image to annotate later: Click Skip.
      • Skipped images will reappear after all non-skipped images are annotated

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 approved 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: Image regression.

Summary​

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

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