How to entirely delete a class from project (not in dataset)

Is there a way to entirely remove a class from a project, or rename a class? I know I can omit or override them as a preprocessing step when generating datasets, but I don’t want them to stay in the project because they cause frustration for annotators (e.g. I have two classes with the same name and I want to remove one of them, because annotators don’t know which one to choose).

Yes, you can remove or rename classes in your project using Roboflow’s Modify Classes preprocessing tool. This tool allows you to omit specific classes or remap (rename) classes when generating a new version of your dataset. Here’s how you can do it:

  1. Navigate to your dataset’s page within the Roboflow platform.
  2. Proceed to generate a new version by selecting Generate New Dataset.
  3. Click to Add Preprocessing Step and choose Modify Classes from the preprocessing options.
  4. You will then be presented with options to remap (rename) or omit (remove) classes. For example, you can rename your classes to a more generic name or exclude certain class annotations.
  5. Hit Apply to save your changes.

After you finish your other preprocessing and augmentation settings and click Generate, the dataset version will export with these new class label settings.

Please note that these changes are non-destructive, meaning you can always go back to your original ontology later if you decide you actually do need the original classes.

For more detailed instructions, you can refer to the Roboflow documentation and this blog post.

However, please note that these changes are applied at the dataset level when generating a new version of it, not at the project level. The original classes will still exist in the project and will be available for annotators. If you want to permanently remove or rename a class at the project level, you might need to manually adjust your annotations or contact Roboflow support for further assistance.

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I want to permanently delete the class on the project level, not as preprocessing. I think this is a very reasonable thing to do and should be a supported function.