Training Projects
Enhance your annotation teams’ skills with Encord’s Training feature. Train annotators using benchmark projects for improved data quality and performance assessment.
Encord’s Annotation Training feature - called ‘Training’ in the application - provides a practical way to train your annotation and operation teams, thereby improving the quality of your training data.
Annotator teams are trained from a Benchmark Project. Benchmark Projects serve as the ‘gold standard’ that your team’s annotations are compared to. It scores the performance of each trainee based on various metrics allowing them to improve the quality of their annotations, and provide clear insights on the quality of their work.
Important Information
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Annotator training Projects can only use Workflow Projects as a benchmark source. Workflow Projects include Consensus and non-consensus Projects.
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Only ONE Workflow Project can be used as a Benchmark Project for a Training Project.
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Benchmark Projects can be any Workflow Project that has at least 1 task in the COMPLETE stage.
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Training Projects can have up to 100 tasks for users to annotate. The number of tasks depends on the number of tasks in the COMPLETE stage of the Benchmark Project.
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The latest tasks in the COMPLETE stage of a Benchmark Project are used in a Training Project. A maximum of the latest 100 tasks become part of the Training Project.
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Up to 24 users can be added to participate in a Training Project during project creation. You can add more users (groups and individual users) to the Training Project from the Settings page after project creation.
Supported Modalities and Ontology Shapes
Supported modalities for Training Projects
| Modality | Supported? |
|---|---|
| Single images | ✅ |
| Image groups | ✅ |
| Image sequences | ✅ |
| Videos | ✅ |
| DICOM | ✅ |
| Audio | ❌ |
| Text | ❌ |
| HTML | ❌ |
| ❌ |
Supported Ontology shapes
| Shape | Supported? |
|---|---|
| Bounding box | ✅ |
| Rotatable bounding box | ✅ |
| Polygon | ✅ |
| Polyline | ❌ |
| Keypoint | ✅ |
| Bitmask | ✅ |
| Object primitive | ❌ |
| Audio region | ❌ |
| Text region | ❌ |
Creating training Projects
1. Create the source Project(s)
The first step to training annotators is creating a source/benchmark Project that contains ground-truth labels. These labels provide a ‘gold-standard’ for your annotators to be trained and graded on.
See our documentation on creating annotation Projects to learn how to create a Project.
2. Create the ground-truth labels
After creating the source/benchmark Project, you need to add the ground-truth labels. You can add ground-truth labels by performing one of the following:
- Labeling data on the Encord platform.
- Using an Agent to label your data: Task Agent or Editor Agent
- Uploading labels using the SDK.
A labeling task needs to have been annotated before it can be used as a ground-truth source. The task’s status must be Completed. We recommend that the task appears in the source Project’s Labels Activity tab with a status of Completed.
3. Create the training Project
After labeling a ground-truth Project, it is time to create the annotator training Project.
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Go to Annotate > Projects > Training projects. The Training projects page appears.
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Click +New training project.
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Specify a meaningful name and description for your Training Project.
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Select or create any Project Tags that might be required.
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Click Next step to continue. The Select project page appears.
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Select the Workflow Project that contains the desired ground-truth labels.
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Set up the initial configuration of the benchmark function, which determines how trainees are evaluated against the ground-truth labels.
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Single frame-level classification only assesses classification instances.
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Use Dynamic benchmark for combinations of objects and classifications. Dynamic benchmarks can be calibrated by setting the relative weights of two evaluation metrics.
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Intersection over Union (IoU) is an evaluation metric that assesses the accuracy of labels compared to the ground truth / gold standard. If labels fully overlap with those in the ground truth full points are awarded. Conversely, if there’s no overlap between a label and the ground truth labels then no points are awarded.
For Keypoints, the IoU represents a measure of distance between the annotator’s point, and the benchmark point. The larger the distance, the lower the IoU score.- Category is an evaluation metric based on correctly identifying the Ontology category. In the example above correctly identifying an ‘Apple’ awards 100 points, while a wrong or a missing category awards no points.
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Here, a single source Project with 100% annotation progress is selected, and the benchmark function is customized for several ontology classes. Once you’re satisfied with your configuration click Next step to continue.
- Add trainees and create the Project. Add trainees as a group, or as individuals. Click Create training program to create the training Project.
Working with training Projects
This section explains how to run a successful annotator training Project. If you do not yet have a training Project, head over to creating a training Project to get started.
Roles and permissions
| Permission | Admin | Team Manager | Annotator |
|---|---|---|---|
| View benchmark project source | ✅ | ❌ | ❌ |
| Edit benchmark scoring function | ✅ | ❌ | ❌ |
| Add annotation instructions | ✅ | ❌ | ❌ |
| Delete | ✅ | ❌ | ❌ |
| Invite team members | ✅ | ✅ | ❌ |
| Manage team permissions | ✅ | ❌ | ❌ |
| Manage admins | ✅ | ❌ | ❌ |
| Annotate tasks in the task management system | ❌ | ❌ | ✅ |
| Control assignments & status in the task management system | ✅ | ✅ | ❌ |
How to run annotator training
1. Onboard your annotators
You can add annotators during the creation phase, or by going to Settings > Team and inviting new annotators. Remember that unlike in annotation Projects where each piece of data can only be seen by one annotator at a time, training Projects score each annotator against the same set of benchmark tasks. Therefore, a copy of each benchmark task will be added to the Project for each annotator added.
You can confirm annotators and tasks are ready to go by checking the summary screen. In this case, our source Project had 4 tasks and we have 4 annotators assigned. We should expect a total of 16 tasks.
After preparing the Project, share the Project URL with each of your team members so they can join and start the training.
2. Annotators proceed through the benchmark tasks
Annotators can access the training Project using the URL you share with them.Annotators see a simplified interface which shows only their tasks in both the summary and labels queue pages. Annotators can start their evaluation tasks by clicking the Start labeling button in the upper right or clicking Initiate next to any given labeling task.
Creating labels in a training Project is identical to creating labels in any other Project. Guide your team to the Label Editor documentation to get them started. Once an annotator submits a task, it can not be re-opened.
3. Evaluate annotator performance
Submitted tasks are automatically run through the benchmark function, and the annotators performance on the task is computed. Project administrators can confirm annotator progress and performance in the Summary page. Use the Overview tab for quick insights into overall annotator performance. Use the Annotator submissions tab to confirm individual task submissions on a per-label basis.
At this stage, you can communicate with your annotators in whichever manner is easiest for you and your team. Use the CSV to download the entire set of results and share with relevant team members. Or perhaps it makes more sense to schedule a live review, using the Annotator submissions’ ‘View’ functionality to verify the benchmark labels and a given annotator’s submission in the label editor.
For Projects that have hundreds of evaluation labels per annotator, where an ‘evaluation label’ is defined as an annotation per frame, we limit the number of evaluation labels displayed in the dashboard for performance reasons. The labels displayed will be some random sampling of the submitted labels. You can always access the full set of evaluation labels by downloading the CSV. Larger downloads may require significant time, and may prompt you to run the downloads in a separate tab so the download can proceed while you can continue working in the current tab.
4. Adjust the benchmark function and re-calculate scores
If you feel that annotator score distributions do not correctly reflect the skill displayed, the benchmark function can be adjusted and annotator scores can be recalculated.
Go the Settings page, and find the section marked ‘Benchmark scoring function’. Press the Edit button to enable the function’s weight editor and change the values to match your new plan. Finally, press Save in the upper right to persist the new function configuration.
To see the changes applied against previous submissions, return to the ‘Summary’ page and press the Re-calculate scores button. If a given annotator’s annotations were affected by the weighting change, the ‘Benchmark results’ column will change to reflect their new score with the new weights! In this case, we see the score of an annotator, on the left and right respectively before and after we changed the scoring function (as above), and pressed the Re-calculate scores button. The annotator’s change in score is noticeable, but doesn’t seem to change their performance from unskilled to skilled. Likely, this annotator should undergo another round of training.
5. Repeat until finished
You can continue to adjust scores even after all the annotators have finished all their tasks, until you feel the score distribution matches your intent.
You can also add new annotators to existing Projects, as you did in step #1.