Backlog
Why?
High-quality training data is essential yet challenging to procure affordably and efficiently. By automating data annotation with foundational models, we aim to:
- Lower data collection and annotation costs.
- Enhance dataset creation flexibility and speed.
- Improve data quality and annotation accuracy.
- Improve models quality.
What?
Implementing large foundational models for auto-annotation streamlines the preparation of diverse datasets. This approach:
- Accelerates dataset readiness.
- Adapts easily to different data types, from text to images.
Action items
- Evaluate publicly available models
- Create an interface that allows easy extension to new models (abstract the actual foundation model network inference, so different models can be supported or even a call to Neusphere)