Benchmarks enable you to designate a labeled asset as a “gold standard” and automatically compare all other annotations on that asset to the benchmark label.

In order for a benchmark agreement to be calculated, there must be one benchmark label and at least one non-benchmark label on the asset. When a data row is labeled (or the annotations are updated), the benchmark agreement will be recalculated as long as there is at least one non-benchmark label on the data row.

When the benchmarks tool is active for your project, the individual performance section under the performance tab will display a benchmarks column that indicates the average benchmark score for that labeler.

Currently, benchmark agreement calculations are only supported for the following asset and annotation types:

Asset typeBounding boxPolygonPolylinePointSegmentation maskEntityRadioChecklistDropdownFree-form text
Tiled imagery-N/A-


Benchmarks in the queue

The Labelbox queuing system will serve benchmarks for all data types. If the annotation type or data type is not supported by our calculation, no benchmark score will be shown in our application, but the labels will be grouped together under the benchmark.

After the first 5 benchmarks that are at the top of the queue, Labelbox does not guarantee any order on the remaining benchmarks. After the first 5, there is a 10% chance that the next asset you see will be a benchmark label.

Set up benchmarks

When you create a project, you will be prompted to select a quality setting for your project.

Note that you can only select one quality mode per project. It is not possible to have both benchmarks and consensus enabled at the same time for a project.


Cannot update quality mode later

Once you select a quality mode for your project at project creation, you will not be able to update the quality mode afterward.

Only labeled data rows can be designated as a benchmark. To designate a data row as a benchmark follow these steps:

  1. In the editor, label the data row and click Submit.
  2. Navigate back to the project homepage.
  3. Go to the Data rows tab.
  4. Select the labeled data row from the list. This will open the Data row browser.
  5. Click on the three dots next to the data row and select Add as benchmark from the dropdown.


How are object-type annotations factored into the benchmark calculation?

The benchmark agreement for bounding box, polygon, and segmentation mask annotations is calculated using Intersection over Union (IoU). The agreement between point annotations and polyline annotations is calculated based on proximity.

  1. First, Labelbox compares each annotation to its corresponding benchmark annotation to generate IoU scores for each annotation. The algorithm first finds the pairs of annotations to maximize the total IoU score, then it assigns an IoU value of 0 for the unmatched annotations.

  2. Then, Labelbox averages the IoU scores for each annotation belonging to the same annotation class to create an overall score for that annotation class.

"Tree" annotation class agreement = 0.99 + 0.99 + 0.97 + 0 + 0 / 5 = 0.59

How are classifications factored into the benchmark calculation?

The calculation for each classification type varies. One commonality, however, is that if two classifications of the same type are compared and there are no corresponding selections between the two classifications at all, the agreement will be 0%.

  • A radio classification can only have one selected answer. Therefore, the agreement between the two radio classifications will either be 0% or 100%. 0% means no agreement and 100% means agreement.

  • A checklist classification can have more than one selected answer, which makes the agreement calculation a little more complex. The agreement between two checklist classifications is generated by dividing the number of overlapping answers by the number of selected answers.

  • A dropdown classification can have only one selected answer, however, the answer choices can be nested. The calculation for dropdown is similar to that of checklist classification, except that the agreement calculation divides the number of overlapping answers by the total depth of the selection (how many levels). Answers nested under different top-level classifications can still overlap if the classifications at the next level match. On the flip side, answers that do not match exactly can still overlap if they are under the same top-level classification.

For child classifications, if two annotations containing child classifications have 0 agreement (resulting in a false positive), the child classifications will automatically be assigned a score of 0 as well.

Labelbox then creates a score for each annotation class by averaging all of the per-annotation scores.

For example, when Image X loads in the editor, a labeler has 3 classification questions to choose from (Q1, Q2, Q3) each with two answers. The green boxes indicate the benchmark answers.


Say, for example, a labeler answers Q1 correctly, but answers Q2 and Q3 incorrectly.

For classifications, the benchmark agreement is calculated based on: 1. How many unique answer schemas the labeler selects AND 2. Out of those selected, how many are correct?

  • Q1-A: 1

  • Q1-B: N/A <-- not included in the final calculation.

  • Q2-A: 0

  • Q2-B: 0

  • Q3-A: 0

  • Q3-B: 0

So the final consensus calculation for the classifications on Image X is:

(1 + 0 + 0 + 0 + 0) / 5 = .20

How is the benchmark score calculated for the data row?

Labelbox averages the scores for each annotation class (object-type & classification-type) to create an overall score for the asset. Each annotation class is weighted equally. Below is a simplified example.

Benchmark score = (tree annotation class agreement + radio class agreement) / total annotation classes

0.795 = (0.59 + 1.00) / 2

You can use this metric as an initial indicator of label quality, the clarity of your ontology, and/or the clarity of your labeling instructions.