Labelling prioritization

This describes how to choose samples for labelling within the platform

Why Label Selection Matters

In many real-world workflows, teams maintain a large pool of unlabeled data, but only a limited labeling budget. Periodically, a small subset of that data is selected for annotation — often based on heuristics, random sampling, or gut feeling.

But not all samples are equally valuable. Labeling redundant, uninformative, or already well-represented data leads to:

• Slow improvements in model performance

• Wasted annotation effort

• Missed edge cases and critical blind spots

The real challenge is deciding what to label — selecting the most informative, high-impact samples from a sea of unlabeled inputs.

How Tensorleap Helps Prioritize What to Label

Once a model has been evaluated within the platform, Tensorleap enables you to prioritize labeling with a single click. It analyzes the model’s latent space to rank samples from the unlabeled pool based on their potential value to the model.

This means you can:

• Focus labeling effort on what matters most

• Avoid over-labeling redundant samples

• Make measurable progress with each new annotation cycle

You can choose how many samples to retrieve — or let Tensorleap recommend a number based on the current model state.

📸 [Insert screenshot placeholder: prioritized sample selection UI]

A Full Labelling prioritization Walkthrough

Coming Soon