IoU & Dice Coefficient Calculator
Intersection-over-Union and Dice/F1 overlap for object detection and image segmentation — from areas or set counts.
IoU=0.5 is the classic 'correct detection' threshold (PASCAL VOC); COCO averages IoU 0.5–0.95. Dice (identical to F1 on sets) is the default in medical image segmentation. Dice always ≥ IoU and is more forgiving of small objects.
Formula
About IoU & Dice Coefficient Calculator
Intersection-over-Union and the Dice coefficient measure how well two regions overlap — the foundational metric for object detection (does the predicted box match the ground-truth box?) and image segmentation (does the predicted mask match the true mask?). This calculator computes both from intersection and region sizes, plus the union, and shows their fixed relationship (Dice = 2·IoU/(1+IoU)). IoU ≥ 0.5 is the classic 'correct detection' bar; Dice is the medical-imaging default. The same math applies to any set overlap, making this a Jaccard-index calculator too.
How to use IoU & Dice Coefficient Calculator
- 1Enter your values into IoU & Dice Coefficient Calculator — sensible, domain-typical defaults are pre-filled so you see a real result immediately.
- 2The result recomputes live using the formula shown on the page; there is no button to press.
- 3Adjust any input to compare scenarios, then read the worked example to see the substituted numbers.
Why use IoU & Dice Coefficient Calculator?
- ✓Computes IoU & Dice Coefficient instantly in your browser — no sign-up, no upload, no server round-trip.
- ✓100% free and unlimited, with the exact formula shown: IoU = |A∩B| / |A∪B|.
- ✓Runs entirely client-side, so every value you enter stays private on your device.
- ✓Live recompute as you type, with a worked example and authoritative references for trust.
Frequently asked questions
IoU vs Dice — when to use which?+
They rank overlaps identically (one is a monotonic function of the other), so it's largely convention: object detection uses IoU (with thresholds like 0.5), medical image segmentation uses Dice. Dice is more forgiving — it always exceeds IoU for imperfect overlaps — which is why it's favored when objects are small and any penalty feels harsh.
Why is IoU = 0.5 the standard detection threshold?+
PASCAL VOC set it as the bar for counting a detection as correct: the predicted box must overlap the ground truth by at least half their union. It's a reasonable balance — strict enough to require real localization, lenient enough to tolerate minor box errors. COCO raised the bar by averaging across IoU 0.5 to 0.95.
Is the Dice coefficient the same as F1?+
Yes — for set membership (each pixel/element is in or out), Dice equals the F1 score exactly: 2·intersection / (prediction + truth) is 2PR/(P+R). So segmentation 'Dice' and classification 'F1' are the same formula applied to pixels vs labels. This calculator's Dice output is your per-image segmentation F1.
How do these extend to whole datasets?+
For detection, you compute IoU per predicted box, threshold it to decide correctness, then summarize with mAP (see our MAP calculator). For segmentation, you average Dice/IoU over images (or compute a global 'micro' version pooling all pixels). This tool handles the single-region computation those aggregates are built from.
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