Data annotation guides
Guides for teams buying labeling work: how to measure label quality, how to run QA, what security to ask for, and how two companies or tools compare. Two guides at the end are for people looking for annotation work.
Guides for buying data annotation
- Annotation quality metrics: Accuracy against a gold set, IoU for boxes and masks, and agreement between labelers, with worked examples.
- Data annotation QA workflow: From the label guide to an accepted batch, and what to test in a vendor pilot.
- Data annotation security and compliance: The audits and laws each company and platform names, linked to its source, and the papers to ask for.
- Data labeling outsourcing: Costs, pilots and contracts when another company labels your data.
- Scale AI vs Surge AI: Scale AI vs Surge AI side by side.
- Scale AI vs Appen: Scale AI vs Appen side by side.
- Appen vs TELUS Digital: Appen vs TELUS Digital side by side.
- Labelbox vs V7 Darwin: Labelbox vs V7 Darwin side by side.
- CVAT vs Label Studio: The two most used free labeling tools side by side.
- Best data annotation tools: Open-source tools compared on license, formats and self-hosting.
Guides for annotators
- Data annotation jobs: Who hires annotators and what they pay.
- Data Annotation Tech review: Whether the platform is legit and what it pays.