Data Labelling and Annotation
Before a model can learn to recognise something, people often have to label examples for it — marking which email is spam or which image shows a defect. This unglamorous work is foundational.
This article explains why labelling matters and how to do it well.
Why It Matters
A model learns directly from labels. Inconsistent or careless labelling teaches it the wrong lessons, and no later cleverness fully recovers from that.
Keeping Labels Consistent
- Write clear guidelines with examples.
- Have more than one person label tricky cases.
- Measure agreement and resolve disputes.
- Review and refine the guidelines as you go.
Managing the Effort
Labelling can be time-consuming, but there are ways to keep it manageable without cutting corners on quality.
- Label a representative sample first.
- Train an initial model and let it pre-label the rest.
- Have people correct rather than start from scratch.
Frequently Asked Questions
Can we outsource labelling?
Yes, and many teams do, but clear guidelines and quality checks are essential or the labels — and the model — suffer.
If you need a hand with any of this, your Progressive Robot delivery team is ready to help. Raise a ticket from the Support area of your client portal or speak to your account manager and we will guide you through the next steps.