The consensus tool allows you to automatically compare the annotations on a given asset to all other annotations on that asset. Consensus works in real-time, so you can take immediate and corrective actions toward improving your training data and model performance.
Once an asset is labeled more than once, a consensus score is automatically calculated. Whenever an annotation is created, updated, or deleted, the consensus score will be recalculated as long as at least 2 Labels exist on that data row. Recalculations may take up to 5 minutes or so, depending on the complexity of the labeled asset.
Consensus agreement calculations are only supported for the following asset and annotation types.
|Asset type||Bounding box||Polygon||Polyline||Point||Segmentation mask||Entity||Relationship||Radio||Checklist||Free-form text|
When you create a new project, you’ll be prompted to select a quality setting (benchmark or consensus).
Cannot update quality setting
You cannot switch your quality setting once the project has been created.
If you have consensus enabled for your project, you can configure consensus settings for any batch added to that project.
Cannot update batch settings
You cannot change the priority, coverage, or number of labels values after batch creation.
|Data row priority||This value indicates where in the labeling queue these data rows will be slotted based on priority.|
|% coverage||This value indicates what percentage of the data rows in the batch will enter the labeling queue as consensus data rows.|
|# labels||This value indicates how many times each consensus data row will be labeled.|
At project creation, when you select consensus as the quality mode, Labelbox automatically enables multi-labeling for the data rows queued to that project. This means that data rows included in the % coverage (see section above) can be labeled by more than one labeler.
After a data row is labeled, it can be reviewed. By default, Labelbox will preselect the first entry of annotations on a data row as the “winner”. During the review process, the reviewer can reassign the consensus selection to another set of annotations after the data row is labeled more than once.
If your data row has been labeled more than once, you'll be able to see all of the labeler entries on that data row in the data row browser. In the example below, you can see that the data row has been labeled twice, and the first entry of annotations is designated as the “winner” (indicated by the green trophy icon).
You can change the “winner” designation to another set of annotations by clicking on the trophy icons.
Watch this video to learn about approving and rejecting data rows in a consensus project.
Within a project, navigate to Performance > Quality and you will see two charts. The histogram on the left displays the average consensus score for labels created in certain date ranges. The histogram on the right shows the number of labels created that have a consensus score within the specified range.
The consensus column in the data row activity table contains the agreement score for each labeled data row and how many labels are associated with that score. When you click on the consensus icon, the activity table will automatically apply the correct filter to view the labels associated with that consensus score.
When you click on an individual labeler in the performance tab, the consensus column reflects the average consensus score for that labeler.
View consensus scores in a label export
consensus_scorefield in the JSON for exported labels will have a value between 0 and 1 that denotes the associated consensus score for the label. This field can be found in the
performance_detailssection of an exported label.
For assets that have only been labeled once, and thus do not have a consensus score, the
consensus_scorefield will not show up in the label export.
Consensus 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.
First, Labelbox compares each annotation to its corresponding 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 the IoU value of 0 to any unmatched annotations.
Labelbox then 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
The consensus score for two text entity annotations is calculated at the character level. If two entity annotations do not overlap, the consensus score will be 0. Overlapping text entity annotations will have a non-zero score. When there is overlap, Labelbox computes the weighted sum of the overlap length ratios, discounting for already counted overlaps. Whitespace is included in the calculation.
- Since the consensus agreement for NER is calculated at the character level, spans of text are partly inclusive. For example, If two labelers make an overlapping text entity annotation on the word "house" and the first labeler submits an annotation with
houseand the second labeler submits an annotation on the same word in the text file with
hous, the agreement score between these two annotations would be 0.80.
- Labelbox then averages the agreements for each annotation created using that annotation class to create an overall score for that annotation class.
The calculation method for each classification type is different. 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.
For child classifications, if two annotations containing child classifications have 0 agreement (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 annotation scores.
For example, when Image X loads in the editor, the labelers have 3 classification questions to choose from (Q1, Q2, Q3) each with two answers.
Say, for example, these 2 labelers have the same answer for Q1 but different answers for Q2 and Q3.
For classifications, the consensus agreement is calculated based on how many unique answer schemas are selected by all labelers.
Q1-A: 1 (both labelers picked this answer)
Q1-B: N/A (neither labeler picked this answer) <-- not included in the final calculation.
Q2-A: 0 (Labeler 1 selected, Labeler 2 did not)
Q2-B: 0 (Labeler 2 selected, Labeler 1 did not)
Q3-A: 0 (Labeler 1 selected, Labeler 2 did not)
Q3-B: 0 (Labeler 2 selected, Labeler 1 did not)
So the final consensus calculation for the classifications on Image X is:
(1 + 0 + 0 + 0 + 0) / 5 = .20
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.
Consensus score = (annotation class agreement + radio class agreement) / total annotation classes
0.795 = (0.59 + 1.00) / 2
You can use the metric as an initial indicator of label quality, the clarity of your ontology, and/or your labeling instructions.
Updated 10 days ago