- In the cars-on-the-bridge image, the red bounding box for the semitruck in the oncoming lanes is too small, with its upper bound just above the top of the semi's windshield, ignoring the much taller roof and towed container.
- In the same image, there are red bounding boxes around cars that exist, and also red bounding boxes around non-cars that don't exist. If false positives and false negatives are going to be represented in the same picture, it'd be nice to use different colors for them, so the viewer can tell whether the error was identified correctly or spuriously.
- I have trouble understanding the "bus" screenshot. The caption says "(green pictures are valid errors) – The pink dotted boxes are objects that have not been labelled but that our error spotting algorithm highlighted." In other words, the green-highlighted pictures are false negatives considered from the perspective of the original data set, and the red-highlighted pictures are true negatives. Or alternatively, the green-highlighted pictures are true positives from the perspective of the error-spotting algorithm, and the red-highlighted pictures are false positives. What confuses me is that all 9 pictures are labeled "false positive" by the tabbing at the top of the screenshot.
Comments
I've added screenshots of errors in the blogpost so that you have an idea of the errors we spotted. Let me know what you think of them.
A couple notes on those screenshots:
- In the cars-on-the-bridge image, the red bounding box for the semitruck in the oncoming lanes is too small, with its upper bound just above the top of the semi's windshield, ignoring the much taller roof and towed container.
- In the same image, there are red bounding boxes around cars that exist, and also red bounding boxes around non-cars that don't exist. If false positives and false negatives are going to be represented in the same picture, it'd be nice to use different colors for them, so the viewer can tell whether the error was identified correctly or spuriously.
- I have trouble understanding the "bus" screenshot. The caption says "(green pictures are valid errors) – The pink dotted boxes are objects that have not been labelled but that our error spotting algorithm highlighted." In other words, the green-highlighted pictures are false negatives considered from the perspective of the original data set, and the red-highlighted pictures are true negatives. Or alternatively, the green-highlighted pictures are true positives from the perspective of the error-spotting algorithm, and the red-highlighted pictures are false positives. What confuses me is that all 9 pictures are labeled "false positive" by the tabbing at the top of the screenshot.