Proposal Filtering

Non Maximum Suppression

Non Maximum Suppression is a computer vision method that selects a single entity out of many overlapping entities (for example bounding boxes in object detection). The criteria is usually discarding entities that are below a given probability bound. With remaining entities we repeatedly pick the entity with the highest probability, output that as the prediction, and discard any remaining box where a $\text{IoU} \geq 0.5$ with the box output in the previous step.

Image Credit: Martin Kersner


Paper Code Results Date Stars


Task Papers Share
Object Detection 176 32.77%
Semantic Segmentation 24 4.47%
Instance Segmentation 19 3.54%
Real-Time Object Detection 18 3.35%
Test 18 3.35%
Pedestrian Detection 12 2.23%
Image Classification 11 2.05%
General Classification 11 2.05%
Classification 9 1.68%


Component Type
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