Oleg Serov

Thanks, Ahmet! Apologies for a delayed response. That's an important point. The ECE applies only to the fixed set selected using original scores ≥0.25. It doesn’t validate lower-scoring detections that a deployment pipeline might admit after correction. The correction was also fitted on that filtered population, so applying it below the original-score floor would need separate validation. Reporting that floor alongside each operational threshold would make the scope clearer. That's a great direction for the next step, thank you!

Thanks, Kartik! Apologies for a long response. The CI analogy makes sense - one summary number can hide quite a lot. In my experiment Platt scaling produced the large improvement, reducing ECE from 16.64 to 1.31 percentage points, however the temperature scaling only brought it down to 15.28. Both methods kept the boxes and rankings unchanged. I haven’t tested adaptive-width bins in this experiment, so I can’t say they caught something the fixed bins missed. I did check 10, 15, and 20 equal-width bins: YOLO26n’s original ECE stayed at 16.64 points across all three. That confirms underconfidence finding, but still doesn’t really establish how sensitive the corrected result is to binning. Thank you, comparing equal-count bins would be a useful follow-up, especially in the high-confidence region.

Ahila, thank you for you comment, that's an interesting metric to check! To do this I defined rare classes as the 20 COCO classes with the fewest labeled objects in the calibration split. Together, they had 773 eligible test detections. On this subset the ECE value for Temperature was about 9.89 percentage points, while Platt gave 3.74 (lower is better). So Platt still showed better calibration on the rare classes, although individual class results varied and many had small sample sizes.

Jam, thank you, that's a great point! I have the saved per-detection results, so it's possible report precision and retained counts at several corrected-score thresholds. I have added it to the article.