Cost-benefit analysis
The measured data show that: pure manual annotation takes 120 seconds per figure, pure AI annotation accuracy is only 70%, and the hybrid solution can realize the best cost performance.
hybrid labeling strategy
- AI Priority Phase::
- For large clear targets (>15% of the frame): relies on AI auto labeling
- Enable "Batch AI Processing" to batch process similar images.
- Acceptance of initial accuracy for 70%-80%
- Manual finishing stage::
- Focused review of test frames with confidence levels <0.6
- Small targets (<32×32 pixels) all manually labeled
- Use the Tab key to quickly switch between tabs to be corrected
- quality control::
- Spot check 10% labeling quality for every 100 sheets completed
- Monitor the F1-score for each category in the "Stats" panel.
- Increased sample size for high-problem categories
Budget Optimization Tips
- Complex images: AI labeled first draft → outsourced correction → expert review
- Simple picture: complete reliance on AI+5% sampling
- Load domain-specific models (e.g. medical CT-specific detectors) using "Custom Model".
This answer comes from the articleMakeSense: a free-to-use image annotation tool to improve computer vision project efficiencyThe
May not be reproduced without permission:AI productivity tools " How to balance AI-assisted and manual labeling with limited data labeling budget?
