Every project has a single labeling queue. The default method for queueing data rows to a project's labeling queue is by using batches.
Labelbox is making batch-based queueing the default for all new projects on a rolling basis. By the end of 2022, all new projects will automatically be configured with batch-based queuing. For more information, visit this guide.
Click Batch history to view a changelog of added and removed data rows within this project. You'll also be able to see which batch the data rows belong to.
Dataset-based queues are useful when you intend to label all the data rows in a data set.
Dataset limits per project
Please note that you can add at most 1500 datasets to a single project.
You can attach up to 1500 datasets to a labeling project. When you attach a dataset to a project, Labelbox will add all of the data rows in that dataset to the labeling queue.
If you have a large dataset (over 100k data rows), we recommend dividing the large dataset into smaller datasets. This will keep the queue size small and maintain high queue performance, especially if you are working with a large labeling team.
You can attach a dataset via the app or you can do it via the Python SDK.
When you detach a dataset from a project, Labelbox will remove all its data rows from the project. However, you'll still be able to view the labels in the project. If the dataset is detached during a live labeling session, some of its data rows may remain reserved in the queue (depending upon the number of active labelers).
Once a dataset is attached to one or more projects, you can continuously append new data rows to the label queue for those projects by adding them to the attached dataset. You can add more data rows via the app or you can use the Python SDK (recommended).
Updated 16 days ago