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Auto-Annotation

Use AI to help you build AI. Auto annotation uses your model predictions to label your training data

This tutorial demonstrates how auto-annotation workflows can be configured within the Clarifai platform. With auto-annotation, you can use model predictions to label your inputs. Auto-annotation can help you to prepare training data, or assign other useful labels and metadata to your inputs.

When a concept is predicted by a model, it is predicted with a confidence score between 0 and 1. For example, when your model predictions are confident (close to 1), you can have your data automatically labeled with that concept. When your predictions are less-than-confident, you can have your input sent to a human being for review.

This enables you to speed-up and scale-up your annotation process while ensuring quality standards.

Workflows

In the Clarifai platform, the outputs from one model can be used as inputs to another model. This forms a workflow. Different models accept and produce different types of inputs and outputs.

objective

In this tutorial, we'll create a workflow that detects bounding box regions in images of cats and dogs. Once a certain threshold is met, the workflow will automatically generate annotations for these detected regions. If the threshold is not met, the annotation will be marked as pending review.

Here's what our final workflow will look like:

Prerequisites​

  • Create an application, add images of cats and dogs to a dataset in the app, and add bounding box labels of cat and dog to the images, respectively. You could use the following images:
https://samples.clarifai.com/dog1.jpeg
https://samples.clarifai.com/dog2.jpeg
https://samples.clarifai.com/dog3.jpeg
https://samples.clarifai.com/cat1.jpeg
https://samples.clarifai.com/cat2.jpeg
https://samples.clarifai.com/cat3.jpeg
  • Create a visual detector model and train it with the cat and dog concepts.
  • Create a labeling task. Remember to choose Detection as the modeling objective. Then, go to the Tasks listing page and copy the ID of the task.

Step 1: Set up Visual Detector​

visual detector

Input: image

Output: regions[…].data.concepts, regions[…].region_info.bounding_box

The visual detector model will detect bounding box regions in images and then classify objects within the boxes.

a. Go to the workflow builder page.

b. Search for the visual-detector node in the left sidebar of the page.

c. Drag it onto the empty workspace and connect it to the IN element.

d. Use the pop-up that appears in the right sidebar to search for the detection model you created and add it to the node. After selecting the model, we'll use the default settings for the other output configuration options for the purpose of this illustration.

note

You can use the tools on the left side of the workspace pane to manage the workflow creation process. These tools allow you to zoom in and out, fit the view, arrange the workflow, reset the workspace, and more.

Step 2: Set up Region Thresholder​

Region Thresholder

Input: regions[…].data.concepts

Output: regions[…].data.concepts

This operator allows you to filter regions based on the concepts they contain using specific thresholds for each concept and an overall operator (>, >=, =, <=, or <). In this example, we use the " > " (GREATER_THAN) threshold type. Thus, only the cat and dog concepts outputted by the visual detector model above a certain threshold will be processed further downstream.

a. Search for the region-thresholder node in the left sidebar and drag it onto the workspace. Connect it to the visual-detector node.

b. Click the SELECT CONCEPTS button in the right sidebar.

In the window that appears, select the relevant concepts already existing in your application. For this example, let's select the cat and dog concepts, and use the slider to set their threshold values to 0.95 each. This threshold will determine which annotations are accepted and which are set aside for follow-up review.

Click the OK button to save the changes. You'll see the selected concepts highlighted in the right sidebar, along with their threshold values.

c. Select the concept_threshold_type as GREATER_THAN. Keep the other configuration options set to their default values.

Step 3: Set up Annotation Writer​

Annotation Writer

Input: Any

Output: Any

Annotation writer allows you to write the input data to the database in the form of an annotation with a specified status as if a specific user created the annotation.

a. Search for the annotation-writer node in the left sidebar and drag it onto the workspace. Connect it to the region-thresholder node.

b. In the right sidebar, set up the following output configurations:

  • Set the annotation_status as ANNOTATION_SUCCESS. This will write the annotations with the ANNOTATION_SUCCESS status.
  • Set the annotation_user_id. This is the user_idfor which to write the annotations on their behalf as if they manually did the work themselves. You could also choose your own user_id.
  • In this example, let's leave the annotation_info field empty.
  • Set the task_id. This is the ID the task annotation work belongs to. You can retrieve it from the Tasks listing page — as earlier mentioned.

Step 4: Set up Low Confidence Score Flow​

In the flow described above, the visual detector model identifies bounding box regions for the provided inputs. These regions are then sent to the region thresholder node. The GREATER_THAN operator filters out regions that do not meet the threshold and forwards the remaining regions to the annotation writer model. Bounding boxes that pass this filter are written to the input with an ANNOTATION_SUCCESS status.

To capture predictions with low prediction scores, we'll set up a separate region thresholder node that uses the LESS_THAN operator. This operator will filter out regions with prediction scores below the threshold and write these regions to the input with an ANNOTATION_AWAITING_REVIEW status.

a. Search for the region-thresholder node in the left sidebar and drag it onto the workspace. Connect it to the visual-detector node. Also, click the SELECT CONCEPTS button in the right sidebar, and set the threshold values for the cat and dogconcepts to 0.95 each. Lastly, set the concept_threshold_type as LESS_THAN_OR_EQUAL.

LESS_THAN_OR_EQUAL

The LESS_THAN_OR_EQUAL (<=) operator ensures that all values that are either less than or exactly equal to the specified threshold are included in the result. In this case, if we set a threshold of 0.95 using the operator, any value that is 0.95 or lower will meet the condition.

b. Search for another region-thresholder node in the left sidebar and drag it onto the workspace. Connect it to the previous region-thresholder node.

Also, click the SELECT CONCEPTS button in the right sidebar, and set the threshold values for the cat and dogconcepts to 0.50 each. Lastly, set the concept_threshold_type as GREATER_THAN. This ensures that only values that are higher than the 0.50 threshold are included in the result.

c. Search for an annotation-writer node in the left sidebar and drag it onto the workspace. Connect it to the previous region-thresholder node.

In the right sidebar, set up annotation_status as ANNOTATION_AWAITING_REVIEW. This will write the annotations with a pending review status. Also, set the values for annotation_user_id and task_id — as earlier described.

Step 5: Save Workflow​

Lastly, click the Save Workflow button at the upper-right corner of the page.

Your workflow is now ready for use!

Step 6: Use Workflow​

Go to your app's settings page and change the Base Workflow to the workflow you just created. This ensures that the workflow runs every time you add an input to your app.

For example, adding a new image of a dog will trigger the auto-annotation process Then, a bounding box label will be added to the image and it will be assigned with the dog concept.

You can view the annotation in the Input-Viewer page:

That's it!

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