Custom Auto-Label
Custom Auto-Label refers to the AI-enabled automatic labeling feature that is trained and customized using a labeled dataset that a user provides. Users can easily train a machine learning model without any additional coding whenever they obtain a small labeled dataset in the process of creating a new dataset and apply the trained Custom Auto-Label AI to a set of unlabeled images to accelerate the data labeling process.
Custom Auto-Label ultimately aims to leverage AI to reduce the amount of time and costs that go into a labeling project. The resources that a user saves by using this AI-enabled feature can then be re-invested in other high-stakes areas such as sourcing and labeling additional batches of edge case or hard example data.

Using Custom Auto-Label

1. Export labeled datasets
To create a Custom Auto-Label AI, you first need a labeled dataset. Label a small set of raw data (manually, or by importing existing labeled data), and then export the labeled dataset.
2. Create Custom Auto-Label AI
In the Export History menu, click Create a Custom Auto-Label AI to start training a custom model on the exported dataset. This can take anywhere from tens of minutes to several hours.
  • You can check the created Custom Auto-Label in the Export > Custom Auto-Label menu.
3. Label your data using the Custom Auto-Label AI you just created
You can use Custom Auto-Label in the same way you use a general Auto-Label model by configuring it in the Project Configuration page.
  • If there are many unlabeled images in the project, it is recommended that you apply the Custom Auto-Label to a subset of the data (i.e. a few hundreds to thousands) and then repeat the process above to keep improving the accuracy of the Custom Auto-Label AI.
  • How to Configure the Auto-Label Settings: [Step 2: Label Specs > Configure Auto-Label]
  • How to use Auto-Label: [Auto-Label]
4. Review labeled data
Once Custom Auto-Label is complete, review the labeled data and correct if there are any errors.
5. Re-export the labeled data, including with newly labeled & reviewed batch created above
Re-export the dataset, but this time including the newly labeled data that you've just created in the steps above. The re-exported dataset should now contain more labeled data and should be able to train a new Custom Auto-Label AI model that performs better than the model created in the previous step.
6. Create Custom Auto-Label AI
Once again, click Create a Custom Auto-Label AI in Labels Export to create a new Custom Auto-Label AI.
Repeat steps 2-6 to increase the performance and accuracy of the Custom Auto-Label AI. The higher the performance, the fewer labels that need to be corrected.

Additional Resources

Which annotation types does Custom Auto-Label Support?

Custom Auto-label AI
Bounding Box
Polygon Segmentation

Which usage plan do I need to use Custom Auto-Label?

Currently, the Custom Auto-Label feature is only available to Enterprise plan users. Auto-Label Credit is limited by Suite user plan and can be found in Settings > Billing & Usage tab.

Auto-Label Credits

Auto-Label Credits are deducted whenever you create a new Custom Auto-Label AI or apply it to label raw data. Please refer to the relevant documentations for more information.
Related Manual: Billing & Usage > Auto-Label Credits

Frequently Asked Questions (FAQ)

How can I improve the performance of my Custom Auto-Label AI?

  • The performance improves as the size of the labeled dataset used for training increases and as the accuracy of labeled dataset improved. The size of a labeled dataset refers to both the number of raw data in the dataset, as well as the number of annotations in each piece of raw data.
  • We recommend that you update and recreate a Custom Auto-Label AI as you gather more and more labeled raw data to continuously improve its performance.

What is the minimum number of labeled data required for Custom Auto-Label training?

The minimum number of images required for Custom Auto-Label training is 1,000-2,000. If there are only a small number of annotated objects in each image, then you will likely need more labeled data for adequate model training.

Will creating Custom Auto-Label stop when I run out of Auto-Label credits?

Yes, Custom Auto-Label creation will stop when all credits are exhausted. In this case, a pop-up will appear where you can choose whether to proceed or stop. If you do decide to proceed, you will be charged for overage.
Any other questions? E-mail us at [email protected]
Last modified 1mo ago