What Is Intersection over Union (IoU)?

What is intersection over union?

IoU quantifies the overlap between a predicted region and a ground-truth region. You take the area where they overlap, the intersection, and divide it by the total area they cover together, the union. The result runs from 0 (no overlap) to 1 (a perfect match). It is symmetric and scale-relative and works for any two regions, which is why it is the default metric for object detection and segmentation.

IoU = area of (A ∩ B) / area of (A ∪ B)

Key takeaways

IoU is overlap divided by combined area, scored from 0 to 1.
It is the standard accuracy metric for bounding boxes and segmentation masks.
A detection usually counts as correct only above a chosen IoU threshold, commonly 0.5.

How it's used

Detection: a predicted box is a true positive when its IoU with a ground-truth box clears a threshold, often 0.5. Mean average precision (mAP) is computed across thresholds.
Segmentation: IoU between predicted and ground-truth masks, also called the Jaccard index, measures pixel overlap.
Thresholds: stricter values like 0.75 or 0.9 demand tighter localization.

How it works, and how FiftyOne fits

In FiftyOne, evaluation matches predictions to ground truth by IoU and tags each as a true positive, false positive, or false negative, so you can sort by IoU to find the loosest matches or the confident-but-wrong predictions. Explore all available IoU utilities available in FiftyOne.

IoU vs related metrics

How IoU compares to the other overlap and accuracy metrics used to score visual AI.

How IoU compares to the other overlap and accuracy metrics used to score visual AI.
Metric	What it measures	Where it's used
IoU (Jaccard)	Overlap over union, 0 to 1	Detection and segmentation, the default overlap score
Boundary IoU	Overlap measured only near the object's edges	Segmentation where edge accuracy matters, thin or wispy objects
Rotated / 3D IoU	Overlap that accounts for orientation, not just position	Rotated boxes, cuboids, LiDAR and AV perception
Dice / F1	Overlap weighted toward the intersection, more forgiving on small regions	Segmentation, especially medical imaging
mAP	Detection accuracy aggregated across IoU thresholds	Object detection benchmarks
Panoptic quality (PQ)	Class accuracy and instance accuracy combined in one score	Panoptic segmentation

Why it matters

IoU is how "did the model find the object" becomes a number. It is harsh on small and thin objects, a few pixels off on a tiny object can drop IoU below threshold even when the detection is essentially right, so small-object benchmarks often look worse than the model really is. And because standard IoU is axis-aligned and orientation-blind, a cuboid or rotated box can score a high IoU while pointing the wrong way, which is why 3D and rotated tasks use rotated or 3D IoU.

Frequently asked questions

What is a good IoU score?

It depends on the task, but 0.5 is the common pass threshold for detection, and 0.7 or higher is considered tight.

Is IoU the same as the Jaccard index?

Yes, IoU is the Jaccard index applied to regions.

Why do detections use an IoU threshold?

To decide whether a predicted box overlaps a ground-truth box enough to count as a correct detection.

Try it in FiftyOne

Match predictions to ground truth by IoU and sort by overlap in the detection evaluation tutorial, and see exactly where IoU turns unforgiving in the small object detection tutorial.

Learn more

See how evaluation comes together end to end on the model evaluation page.

Jesse Mostipak is the SEO and Content Manager at Voxel51, where the work is helping humans find and trust what the brand knows, and teaching the Google knowledge graph and the LLMs answering on their behalf to do the same. That question, how knowledge gets built inside a system, is one Jesse has been chasing for years. Earlier versions of it ran through a New York City high school science classroom, data science and machine learning, and developer relations at Kaggle, Posit (formerly RStudio), and Baseten. The answer doesn't change much depending on whether the learner is a teenager, a software engineer, or a knowledge graph. Jesse holds a Master's in Education from CUNY Hunter College. LinkedIn

Intersection over union (IoU)

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