one of the answer

For detection, a common way to determine if one object proposal was right is Intersection over Union(IoU). This takes the set A of proposed object pixels and the set of true object pixels B and calculates:

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 $p∈(0,1)$ is the IoU.

other answer

mAP@[.5:.95] or mAP@[.5,.95] means average mAP over different IoU thresholds, from 0.5 to 0.95, step 0.05 (0.5, 0.55, 0.6, 0.65, 0.7, 0.75, 0.8, 0.85, 0.9, 0.95).

There is an associated MS COCO challenge with a new evaluation metric, that averages mAP over different IoU thresholds, from 0.5 to 0.95 (written as “0.5:0.95”). [Ref]

We evaluate the mAP averaged for IoU ∈ [0.5:0.05:0.95] (COCO’s standard metric, simply denoted as mAP@[.5, .95]) and mAP@0.5 (PASCAL VOC’s metric). [Ref]

To evaluate our final detections, we use the official COCO API [20], which measures mAP averaged over IOU thresholds in [0.5:0.05:0.95], amongst other metrics. [Ref]

BTW, the source code of coco shows exactly what mAP@[.5:.95] is doing:

self.iouThrs = np.linspace(.5, 0.95, np.round((0.95 - .5) / .05) + 1, endpoint=True)