How We Trained an 83.7% mAP50 Valve Detection Model with Iterative Pseudo-Labeling
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How We Trained an 83.7% mAP50 Valve Detection Model with Iterative Pseudo-Labeling Building a production-grade industrial inspection model from 30 images to 18,000+ using a self-improving training pipeline. The Problem Underground gas infrastructure inspection generates hundreds of thousands of photos. Each valve well contains multiple valves of different types — gate valves, globe valves, ball valves, and others. Manually classifying and cataloging these is slow, expensive, and error-prone. We…
1Key Takeaways
- How We Trained an 83.7% mAP50 Valve Detection Model with Iterative Pseudo-Labeling Building a production-grade industrial inspection model from 30 images to 18,000+ using a self-improving training pipeline.
- The Problem Underground gas infrastructure inspection generates hundreds of thousands of photos.
- Each valve well contains multiple valves of different types — gate valves, globe valves, ball valves, and others.
- Manually classifying and cataloging these is slow, expensive, and error-prone.
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3Why it matters
Coding AI shifts how fast software ships and how much human review each change needs. DEV — ML reports that how We Trained an 83.7% mAP50 Valve Detection Model with Iterative Pseudo-Labeling Building a production-grade industrial inspection model from 30 images to 18,000+ using a self-improving training pipeline.
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