Dataset quality guide
How to diagnose class imbalance in an image dataset
Review per-class coverage before interpreting model metrics.
Count by split
A class can exist overall but be absent from validation or test, which makes comparison unreliable.
Look past totals
Inspect rare classes, crowded scenes, and geographic or camera clusters before deciding how to rebalance.
Document the decision
Record whether you collected, sampled, or accepted the imbalance with the dataset revision.
Decide what blocks training
Set the decision rule before reading the report. Invalid annotations, missing media, split leakage, and incomplete coverage should block a training run until a reviewer resolves or deliberately accepts them. Less severe findings can still matter, but they need a documented disposition instead of being buried under a single aggregate score. The matching solution page explains which local checks apply to this dataset shape and why the result is limited when evidence is unavailable.
Keep evidence with the dataset revision
Save the local report, quality profile, and repair manifest beside the dataset version that entered training. That record lets a teammate reproduce the same finding, compare a later export, and distinguish a data change from a model change. A report is evidence from configured checks at a point in time. It is not a guarantee of model performance or an independent certification.
Repeat the check after every material change
Run the same local check again after relabeling, changing class mappings, moving images between splits, or accepting a vendor correction. Rechecking is more useful than carrying a historical score forward because the score only describes the records and coverage that were examined. For scripted review, keep the quality command and threshold in the training repository so a new dataset revision receives the same gate.
Check your dataset locally
Use the matching local check before starting a training run.