We publish
our numbers.
We publish our internal June 2026 rider validation results, reported on identities excluded from training. Retrieval, group purity and group completeness are reported separately. Use the figures alongside a trial on your own event and a review of the resulting groups.
mixed benchmark
B³ precision · reported
B³ recall · reported
in the mixed sample
RaceLabs uses rider and vehicle signatures to group photographs across an event, including shots without a readable number. The June 2026 mixed benchmark covers 176 held-out identities and 6,505 photographs. It reports retrieval mAP 0.991, B³ precision 0.996 and B³ recall 0.863. These measure ranking, group purity and group completeness separately. They do not guarantee error-free or complete rider folders for every event; review and corrections remain part of delivery.
The record.
The limits.
Source: RaceLabs’ published June 2026 rider-validation summary. This card preserves its reported aggregate figures and identifies what that public record does not specify. It is an internal evaluation, not an independently reproduced benchmark.
Date, version and domain
The published record is labelled June 2026. It does not state an exact evaluation day or a model/evaluation build identifier. Its reported domain is motorcycle rider matching, including a cross-championship split described as MotoGP / WSBK-class.
Sample and held-out identities
The June summary reports 176 identities and 6,505 photographs, with identities reported as excluded from training. The three splits list 25, 78 and 73 identities. Photograph counts per split and the query/gallery selection protocol are not published.
Metric and label reporting
The summary reports retrieval mAP, B³ precision, B³ recall and Adjusted Rand Index. It does not document the exact averaging or weighting for that June run, the label-review procedure, or how many images were excluded or left unassigned. Those missing counts limit any interpretation of overall coverage. A current evaluator definition does not establish how these earlier results were calculated.
What the figures cover
The results describe this rider sample. They do not measure end-to-end delivery, review time, processing volume, video, car or kart driver attribution, or athletics coverage. No independent replication package is supplied. Test a representative event and review its groups before delivery.
Reported on held-out
rider identities.
The published internal validation reports riders excluded from training. The definitions below explain how to read the metric names; the public summary does not specify the exact query selection, averaging or weighting used to calculate them.
mAP, mean Average Precision
Retrieval mAP, mean Average Precision, summarises how well photographs of the same rider rank ahead of other riders across retrieval queries. The mixed benchmark reports 0.991; the hard cross-championship split reports 0.998. Retrieval ranking is different from the completeness of a delivered rider folder.
Same-rider precision
B³ precision measures group purity: for a sample item, the share of its assigned group carrying the same identity label, aggregated across the evaluation. The mixed benchmark reports 0.996. This is a grouping metric, not a guarantee that 99.6% of delivered photographs are correctly assigned in every event. Review the groups for wrong-rider assignments.
What recall really measures
B³ recall measures group completeness: for a sample item, the share of its labelled identity group found in the same assigned group, aggregated across the evaluation. The mixed benchmark reports 0.863. A rider split across groups can lower recall and require a merge. Review both incomplete groups and wrong-rider assignments; high precision does not eliminate either check.
Grouping agreement and scope
Adjusted Rand Index (ARI) describes agreement between assigned groups and identity labels, adjusted for chance. The mixed result is 0.919. The listed splits describe the evaluated sample; they do not establish performance at every event.
Reported riders.
Reported splits.
These are internal reported values from the June 2026 summary, not an independently reproduced current run. No versioned run record links the figures to an exact evaluation build. Compare precision and recall together, with the missing excluded/unassigned counts in mind.
| Validation split | Unseen riders | Photos | mAP | B³ precision | B³ recall | Cluster agreement (ARI) |
|---|---|---|---|---|---|---|
| Single event, clean conditionsbaseline split | 25 | Not published | 1.000 | 1.000 | 1.000 | 1.000 |
| Cross-championship, hardMotoGP / WSBK-class | 78 | Not published | 0.998 | 0.993 | 0.961 | 0.981 |
| Unseen new ridersreported new-rider split | 73 | Not published | 0.982 | 0.996 | 0.722 | 0.819 |
| Full mixed benchmarkInternalthe 176 listed identities | 176 | 6,505 | 0.991 | 0.996 | 0.863 | 0.919 |
mAP mean Average Precision · Precision / Recall B³ cluster metrics · ARI Adjusted Rand Index, agreement with ground-truth grouping.
The new-rider split reports B³ recall of 0.722, compared with 0.863 on the mixed benchmark. Split groups may need merging, and wrong assignments may need correction. The scores do not establish that no photos are lost or misfiled in every event.
Different views.
Groups to review.
Number recognition reads visible digits; facial recognition compares visible facial features. RaceLabs uses rider and vehicle signatures to group an event. A readable number is not required, but obstruction, blur and similar equipment can still make a match uncertain. Judge the resulting groups on your own photographs.
| Situation | Visible information | What to review with RaceLabs |
|---|---|---|
| Number hidden in a pack | Number may be obscured | Rider signatures can help; inspect the group |
| Number angled away | No readable digits | Different views can connect; inspect the match |
| Motion blur | Digits or features may be blurred | Blur can make a match uncertain |
| Practice without numbers | No number to read | Can group without a readable number; review the result |
| Rain or mud over the plate | Number may be obscured | Difficult views still need review |
| Full-face helmet | Face may be concealed | A visible face is not required for rider grouping |
A readable number can help name a group. Rider and vehicle signatures help connect photographs across the event. The number-reading and rider-signature guide explains that distinction in practice. Review uncertain matches before delivery.
Precision is what
the rider feels.
A rider expects the gallery to contain their photographs. B³ precision of 0.996 on the mixed benchmark measures group purity; it does not rule out errors in a customer gallery. Inspect wrong-rider assignments and split groups before publishing.
A practical review check
Inspect groups for photographs of another rider and for one rider split across several groups. The benchmark helps describe grouping quality; the event review determines what you are ready to publish.
Use the trackday grouping and review guide, then follow the editing, export and publication workflow to prepare the delivery.
The SensaPhoto customer story shows published rider galleries and photo/video offers. Those store examples illustrate delivery; they do not independently validate the benchmark or measure matching accuracy.
Use the scores.
Know their scope.
Review difficult matches
The Unclear bin provides a place to review uncertain images. Also inspect the rider groups, correct wrong assignments and merge split sets where appropriate. A change of motorcycle or an unusual view can make a match harder. The time required depends on the event.
Measured on our own domain
These are RaceLabs’ own published motorsport validation results. They cover the named splits and do not establish a result for every circuit, weather condition, rider or camera setup. Test a complete event and include review time when assessing the workflow.
Accuracy, answered.
What does an mAP of 0.99+ actually mean?
Do you need the bib or number plate to be visible?
How does rider grouping relate to number recognition?
What does 99.6% same-rider precision mean for my delivery?
Why is recall lower than precision on new riders?
Is this an independently validated benchmark?
Do these figures cover cars, karts or athletics?
Test it on your
hardest event.
Bring a complete event, including pack shots, difficult angles and practice sessions without numbers. Review the groups and corrections, then judge the workflow on your own photographs. Our guide to choosing motorcycle photo sorting software shows what to include in that evaluation.