For detection, a common way to determine if one object proposal was right is Intersection over Union (IoU, IU). This takes the set $A$ of proposed object pixels and the set of true object pixels $B$ and calculates:
$$IoU(A, B) = \frac{A \cap B}{A \cup B}$$
Commonly, IoU > 0.5 means that it was a hit, otherwise it was a fail. For each class, one can calculate the
- True Positive ($TP(c)$): a proposal was made for class $c$ and there actually was an object of class $c$
- False Positive ($FP(c)$): a proposal was made for class $c$, but there is no object of class $c$
- Average Precision for class $c$: $\frac{\#TP(c)}{\#TP(c) + \#FP(c)}$
The mAP (mean average precision) = $\frac{1}{|classes|}\sum_{c \in classes} \frac{\#TP(c)}{\#TP(c) + \#FP(c)}$
If one wants better proposals, one does increase the IoU from 0.5 to a higher value (up to 1.0 which would be perfect). One can denote this with mAP@p, where $p \in (0, 1)$ is the IoU.
But what does mAP@[.5:.95]
(as found in this paper) mean?
[.5:.95]
part refers to a range of IoU values, but how that range is assessed into a single mAP I would not know. $\endgroup$