IoU a better detection evaluation metric

Choosing the right object detection model means looking at more than just mAP. Choosing the best model architecture and pretrained weights for your task can be hard. If you’ve ever worked on an object detection problem then you’ve undoubtedly come across plots and tables similar to those below while comparing different models.

I performed Error Analysis on Open Images and now I have trust issues

I reassessed Open Images with a SOTA object detection model only to discover that over 1/3 of all false positives were annotation error!