Most photo-sorting tools ask you to trust a demo. We measure accuracy the way the academic field does, on held-out riders the system has never seen, across multiple championships, and we put the results on this page. Here is exactly how well RaceLabs identifies a rider, and why it keeps working in the conditions that break everyone else.
RaceLabs identifies each rider by appearance, helmet livery, suit and machine, tracked from photo to photo and through video, rather than by reading a bib or number plate. On held-out validation across multiple championships, this reaches a mean Average Precision (mAP) above 0.99 and a same-rider precision of 99.6%: when RaceLabs places two photos in one rider's folder, they are the same rider 99.6% of the time. Because identity does not depend on a legible number, accuracy holds when the number is occluded, blurred, dirty, angled away or absent entirely, the exact frames where bib- and number-recognition taggers drop or misfile a photo.
Scoring a system on riders it has trained on is easy and tells you little. The real test is riders it has never seen. Every number on this page is measured on held-out identities: riders that appear nowhere in training. We report the standard metrics used in peer-reviewed person re-identification research.
The reference metric in academic image retrieval. For every photo of a rider, it asks how cleanly the system ranks that rider's other photos above everyone else's. 1.000 is perfect retrieval. RaceLabs sits at 0.99+, and up to 0.998 on a hard cross-championship split.
Of every photo placed into a rider's folder, the share that genuinely belongs there. This is the number that decides whether a client ever opens their gallery and finds a stranger's bike. RaceLabs holds 99.6%, and we deliberately tune toward precision.
B³ recall asks whether all of one rider's photos landed in a single folder. When it dips it's almost never photos lost, it's a rider split across two folders (a bike head-on and from behind genuinely look different), which is a one-click merge. Precision stays at 99.6%, so nothing is misfiled, and a readable number merges the split automatically.
Validation spans clean single events, an unseen set of brand-new riders, and a harder cross-championship split (MotoGP / WSBK-class footage). One model, multiple domains, so the score reflects a real race weekend, not a cherry-picked clip.
No averaging away the hard cases. Below is the deployed production model measured on each validation set, the easy ones and the brutal ones. Higher is better on all columns.
| Validation split | Held-out riders | mAP | Precision | Recall | Cluster agreement (ARI) |
|---|---|---|---|---|---|
| Single event, clean conditionsbaseline split | 25 | 1.000 | 1.000 | 1.000 | 1.000 |
| Cross-championship, hardMotoGP / WSBK-class | 78 | 0.998 | 0.993 | 0.961 | 0.981 |
| Unseen new ridersstrangers, conservative by design | 73 | 0.982 | 0.996 | 0.722 | 0.819 |
| Full mixed benchmarkProductionall riders, all conditions | 176 | 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.
On the full production benchmark, 123 of 176 rider identities are reconstructed perfectly end-to-end. Lower recall on unseen riders is over-segmentation, not loss, a rider's photos occasionally span two clean folders that merge in one click; with precision at 0.996, nothing is misfiled.
Most sorting tools, number-plate readers, bib-recognition taggers, face-match galleries, work by reading the race number or face in the frame, a technique called optical character recognition (OCR). If that number or face isn't legible, they have nothing. In motorsport, illegible is not an edge case; it's most of the weekend. RaceLabs identifies by appearance and tracks identity across frames, so it holds where token-reading collapses. This is a structural difference, not a tuning gap.
A readable number is welcome, RaceLabs uses it to name the folder. It just isn't what the identity depends on.
A rider knows their own session. They lived every corner. So when a number-OCR tool slips someone else's bike into their gallery, the rider spots it instantly, and that gallery becomes the reason they don't buy, and the story they tell other riders. Same-rider precision is the number standing between you and that email. At 99.6%, it isn't sent.
Photographers who switch tell us the same thing: with number-only tools, clients sometimes found the wrong bike in their gallery before the photographer did.
Field feedback, motorsport photographers on RaceLabsWhen a frame is genuinely ambiguous, even to the photographer, RaceLabs doesn't guess: it sends the shot to the Unclear bin for a person to resolve in seconds. And when a rider changes bikes mid-session, their photos can split into two separate clusters; the pair finder surfaces those so you merge the pair in one step. That's why precision stays at 99.6% instead of inventing confidence it doesn't have.
These results are on motorsport footage, bikes and cars on track, which is exactly what we build for. We report the splits as they are, including the harder cross-championship and unseen-rider sets, so the numbers describe a real weekend rather than a friendly one.
Bring the weekend that breaks your current tool, the pack laps, the wet session, the practice with no numbers. That's where the gap shows.