Hi everyone,
I’m currently working on a large-scale annotation project using Label Studio and wanted to get some insights from the community on best practices for handling big datasets efficiently.
Our project involves labeling tens of thousands of text and image samples, and while Label Studio has been a great tool so far, we’re starting to run into performance bottlenecks and workflow challenges. Specifically, I’d love to hear your thoughts on the following:
- Database Optimization: We’re using the default SQLite setup, but I’ve read that switching to PostgreSQL can improve performance. Has anyone made the switch? If so, did you notice significant improvements, and were there any migration challenges?
- Task Distribution: What strategies have you found effective for distributing annotation tasks among multiple users while ensuring consistency? Are there any built-in Label Studio features or plugins that help with this?
- Pre-annotation & Active Learning: We’re looking into ways to speed up the process using model-assisted pre-annotations. Has anyone successfully integrated machine learning models with Label Studio for this purpose? If so, what tools or frameworks did you use?
- Scaling Up: Are there specific server configurations, caching mechanisms, or cloud solutions that you’d recommend for handling large-scale annotation projects?
I’d really appreciate any insights or lessons learned from those who have tackled similar challenges. Thanks in advance for your help!