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.
Which annotation types does Custom Auto-Label Support?
Custom Auto-label AI
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 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.
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.