# Non-Maximum Suppression — Computer Vision

Source: https://www.skillbyai.com/en/computer-vision/d-nms

> One box per object.

## Keep the best, drop overlapping duplicates

Detectors usually produce several overlapping boxes for the same object. **Non-maximum suppression (NMS)** sorts boxes by score, keeps the highest, removes others that overlap it above an IoU threshold, and repeats. A low threshold removes more boxes (risking merging nearby objects); a high threshold keeps more (risking duplicates). NMS is applied per class. Some newer detectors are trained to avoid duplicates and need little or no NMS.

## NMS on five boxes covering two objects, run

I ran this on CPU with Python 3, OpenCV 5.0.0, scikit-image 0.26.0, PyTorch 2.14.1 and torchvision 0.29.1, using scikit-image's bundled sample photos and torchvision's published pretrained weights. Three boxes cover the first object and two cover the second. torchvision's NMS at IoU threshold 0.5 keeps box 0 (score 0.92) and box 3 (score 0.81), one per object.

```python
import torch
from torchvision.ops import nms
boxes = torch.tensor([[50, 50, 150, 150], [55, 52, 152, 148], [60, 58, 158, 160],   # three boxes on object 1
                      [300, 80, 380, 200], [305, 85, 382, 198]], dtype=torch.float)  # two on object 2
scores = torch.tensor([0.92, 0.88, 0.75, 0.81, 0.79])
keep = nms(boxes, scores, iou_threshold=0.5)
print("kept box indices:", keep.tolist())
print("kept scores:", scores[keep].tolist())
```

Output:

```
kept box indices: [0, 3]
kept scores: [0.9200000166893005, 0.8100000023841858]
```

## Tune the threshold for crowded scenes

In dense scenes (crowds, shelves) a higher NMS threshold avoids merging neighbouring objects.

**Quiz:** What does NMS remove?

- [x] Lower-scoring boxes that heavily overlap a higher-scoring box
- [ ] All boxes below 0.99
- [ ] Every other box
- [ ] Ground-truth labels

*Answer:* Lower-scoring boxes that heavily overlap a higher-scoring box. It removes duplicates of the same object.