Label Quality Check¶

Analyze ▸ Label Quality Check…

Checks every labeled frame in the project and flags labels that are probably wrong. Running it before a training run is a much better use of ten seconds than almost anything else you could do with them — training on a systematically mislabeled set teaches the model to be systematically wrong.

Nothing runs when the dialog opens. It has two tabs, and each has its own run button:

Rules — click Run checks for the rule-based checks below.
Anomalies — click Run analysis for a statistical score per instance.

Click any result row to jump to that frame and instance. After you fix things, click Re-run to check again.

What it checks¶

Structural checks¶

Issue	What it means
Duplicate	Two instances on the same frame that are really the same animal — overlapping bounding boxes, or nodes sitting within a few pixels of each other
Incomplete frame	Fewer instances than the project's typical (median) count — an animal was probably missed
Negative frame	A frame marked as containing no animals that nevertheless has instances on it
Sparse instance	An instance with almost no visible nodes
Empty instance	An instance with no visible nodes at all
Out of range	Points outside the image bounds

Duplicate detection also catches the split duplicate case: one animal labeled as two instances with disjoint node sets — say head-and-torso in one instance, torso-and-tail in another. Their boxes may barely overlap, so plain IoU misses them; the check scores the combination of disjoint visible-node sets and body contact instead.

Geometric checks¶

These are the ones you cannot do by eye at scale.

Chirality (L/R) — a whole-instance left/right flip. This is invisible to distance-based checks, because a mirrored pose has all the same node-to-node distances. The check fits a body midline to each instance (the principal axis of its points) and asks which side of it each symmetric pair's "left" member sits on. It learns the canonical side from the majority of your instances, then flags the ones that disagree.

Declare your symmetries

This check needs to know which nodes are left/right pairs. Declare them as symmetries in the skeleton; failing that, the check infers pairs from node names containing left/right markers. No pairs, no chirality check.

Chain order — nodes along a chain (a limb, a spine, a tail) labeled out of sequence. The check finds the longest chain in your skeleton and looks at the turning angles along it; a correctly ordered chain turns smoothly, a transposed one doubles back on itself.

Working through the results¶

On the Rules tab, results are a table of Issue, Frame, and Details. Click a row to jump to it and fix it.

Add flagged frames to Suggestions pushes every flagged frame into the Suggestions list, so you can work through them with Space like any other labeling pass.

Anomalies¶

The Anomalies tab scores every instance by how unusual its pose is compared with the rest of your labels — an unusual joint angle, edge length, or visibility pattern, a likely L/R swap, and the like. Click Run analysis; on a large project it shows progress and a Cancel button.

The table lists Score, Confidence, Top issue, and Frame, worst first.
The Threshold slider sets the score at which an instance is flagged. Lower it to flag more.
Export CSV saves the scores for every instance, not just the flagged ones.

When to run it¶

Before your first training run — cheapest possible model improvement
After a merge — merges are the main source of duplicates
After a bulk import — format conversions can put points out of range
Before publishing or sharing a dataset

Instance Size Distribution catches a different class of problem: instances whose size is anomalous, which usually means a stray point dragged far from the animal.