Non-Maximum Suppression

https://www.coursera.org/lecture/convolutional-neural-networks/non-max-suppression-dvrjH - this is enough to understand NMS.

When multiple proposals may correspond to a single object, which renders all but one proposal to be false-positive. Non-maximum suppression (NMS) solves this problem by clustering proposals by spatial closeness measured with IoU and keeping only the most confident proposals among each cluster.

Two of NMS:

• Greedy

• Optimal

Greedy

Greedy NMS algo

Optimal

Hyper-parameters

Score Threshold

Any proposals with confidence less than the score threshold are rejected.

Overlap Threshold

Two proposals are considered to be in the same cluster when their IoU is larger than the overlap threshold