Quality pains
Common labeling quality problems include 1) Missing labels (15%-30% objects not labeled) 2) Mislabeling (confusion of categories) 3) Inaccurate frames (IOU < 0.7)
Quality control steps
- Pre-labeling check::
- Enabling AI-assisted primary screening of apparent objects
- Setting the confidence threshold to 0.6 balances the detection/accuracy rate
- Three-tier review mechanism::
- Primary labeling: outsourced personnel complete basic labeling
- Expert review: using the "Stats" function to check the distribution of numbers by category
- Model validation: training simple classifiers with completed annotations for reverse validation
- Tool-assisted optimization::
- Fine tune the border with the magnifying glass tool (shortcut Z)
- Polygon labeling for fuzzy targets
- Setting mandatory must-see tags (e.g., "unknown" class needs to be double-checked)
Typical issues addressed
- Occluded objects: label the visible part and add the "occluded" attribute.
- Small goal: zoom in on the image to 200% before labeling it
- Category ambiguity: Establishment of annotation manuals to provide subdivision rules
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 optimize the annotation quality of target detection datasets?
