Custom AI labeling blocks

Custom AI labeling blocks are a way to extend the AI labeling feature within Edge Impulse. If none of the blocks created by Edge Impulse that are built into the platform fit your needs, you can modify them or develop from scratch to create a custom AI labeling block. This allows you to integrate your own models or prompts for unique project requirements.

Ready to dive in and start building? Jump to the examples!

Block structure

AI labeling blocks are an extension of transformation blocks operating in standalone mode and, as such, follow the same structure without being able to pass a directory or file directly to your scripts. Please see the custom blocks overview page for more details.

Block interface

The sections below define the required and optional inputs and the expected outputs for custom AI labeling blocks.

Inputs

AI labeling blocks have access to environment variables, command line arguments, and mounted storage buckets.

Environment variables

The following environment variables are accessible inside of AI labeling blocks. Environment variable values are always stored as strings.

You can also define your own environment variables to pass to your custom block using the requiredEnvVariables key in the metadata section of the parameters.json file. You will then be prompted for the associated values for these keys when pushing the block to Edge Impulse using the CLI. Alternatively, these values can be added (or changed) by editing the block in Studio.

Command line arguments

The parameter items defined in your parameters.json file will be passed as command line arguments to the script you defined in your Dockerfile as the ENTRYPOINT for the Docker image. Please refer to the parameters.json documentation for further details about creating this file, parameter options available, and examples.

In addition to the items defined by you, specific arguments will be automatically passed to your AI labeling block.

AI labeling blocks are an extension of transformation blocks operating in standalone mode, the arguments that are automatically passed to transformation blocks in this mode are also automatically passed to AI labeling blocks. Please refer to the custom transformation blocks documentation for further details on those parameters.

Along with the transformation block arguments, the following AI labeling specific arguments are passed as well.

Additional CLI arguments can also be specified using the CLI arguments field when editing the block in Studio.

Mounted storage buckets

One or more cloud data storage buckets can be mounted inside of your block. If storage buckets exist in your organization, you will be prompted to mount the bucket(s) when initializing the block with the Edge Impulse CLI. The default mount point will be:

/mnt/s3fs/<bucket-name>

The mount point can be changed by editing the block in Studio after pushing.

Outputs

There are no required outputs from AI labeling blocks. In general, all changes are applied to data using API calls inside the block itself.

Running in preview mode

AI labeling blocks can run in 'preview' mode, which is triggered when a user clicks Label preview data within an AI labeling action configuration. When a user is previewing label changes, the changes are staged and not applied directly.

For preview mode, the --propose-actions <job-id> argument is passed into your block. When you see this option, you should not apply changes directly to the data samples (e.g. via raw_data_api.set_sample_bounding_boxes or raw_data_api.set_sample_structured_labels) but rather use the raw_data_api.set_sample_proposed_changes API call.

if args.propose_actions:
    raw_data_api.set_sample_proposed_changes(project_id=project_id, sample_id=sample.id, set_sample_proposed_changes_request={
        'jobId': args.propose_actions,
        'proposedChanges': {
            'structuredLabels': structured_labels,
            'metadata': new_metadata
        }
    })
else:
    raw_data_api.set_sample_structured_labels(
        project_id, sample.id, set_sample_structured_labels_request={
            'structuredLabels': structured_labels
        }
    )
    raw_data_api.set_sample_metadata(project_id=project_id, sample_id=sample.id, set_sample_metadata_request={
        'metadata': new_metadata
    })

if (proposeActionsJobId) {
      await api.rawData.setSampleProposedChanges(project.id, sample.id, {
          jobId: proposeActionsJobId,
          proposedChanges: {
              boundingBoxes: newBbs,
          },
      });
  }
else {
      await api.rawData.setSampleBoundingBoxes(project.id, sample.id, {
          boundingBoxes: newBbs,
      });
}

Initializing the block

Testing the block locally

AI labeling blocks are not currently supported by the blocks runner in the Edge Impulse CLI. To test you custom AI labeling block, you will need to build the Docker image and run the container directly. You will need to pass any environment variables or command line arguments required by your script to the container when you run it.

docker build -t custom-ai-labeling-block .
docker run --rm -e EI_PROJECT_API_KEY='ei_...' -e CUSTOM_ENV_VAR='<env-value>' custom-ai-labeling-block --data-ids-file ids.json --custom-param-one foo --custom-param-two bar

Pushing the block to Edge Impulse

Using the block in a project

Examples

Edge Impulse has developed several AI labeling blocks that are built into the platform. The code for these blocks can be found in public repositories under the Edge Impulse GitHub account. The repository names typically follow the convention of ai-labeling-<description>. As such, they can be found by going to the Edge Impulse account and searching the repositories for ai-labeling.

Below are direct links to some examples:

• Bounding box labeling with OWL-ViT

• Bounding box re-labeling with GPT-4o

• Image labeling with GPT-4o

• Image labeling with pretrained models

• Audio labeling with Audio Spectrogram Transformer

Troubleshooting

Additional resources

• Custom blocks

• AI labeling

• edge-impulse-blocks

• parameters.json

• ids.json