Detect bibs HIDDEN on the BACK of jerseys behind saddle bags. Master moto-photographer workflow and deliver stage results same-day — no more missed riders in the pack.
Road cycling photography is packed-together chaos at scale. Unlike running, cycling bibs live on the BACK of the jersey, hidden behind saddle bags, water bottles, and the riders ahead. Moto-photographers shoot from chase motorcycles, capturing 3,000-8,000 photos per stage. Bibs are small, partially obscured, and riders constantly swap positions.
Typical Event
3-21 days (Grand Tours), 1 day (Gran Fondos/sportives)
Photo Volume
3,000-8,000 per stage (pro racing), 500-1,000 per sportive event
Delivery
Pro racing: within 3 hours for stage results. Sportives: next-day for full gallery
Key Challenge
Bibs on BACK not front, hidden by saddle bags, peloton density with 150+ riders in frame, constantly changing positions
Download the official starting list from race organizers (UCI WorldTour events provide team compositions). Study team colors and rider positions — identify which teams you'll be following and their typical placement in the field. Map out your moto-photographer route: climb routes, high-speed sections, and mountain passes where riders spread out (better individual ID than in the pack).
Pro tip
Memorize the top 20 riders' team colors. When you're shooting from a motorcycle at 50 km/h, you don't have time to look up who's in the breakaway. Knowing 'red jersey = leader, that's 47' saves crucial processing time later.
Ride the chase motorcycle behind or alongside the peloton. Shoot from lower angles to capture back bibs (shooting straight at their wheels and handlebars shows the bib better than shooting from behind). Focus on breakaway groups (5-15 riders) rather than the main peloton where bibs overlap — cleaner shots, better individual reads. Use continuous burst during attacks and climbs when the field spreads out. On flat sections, multiple riders will have bibs hidden; focus on prominent positioning (leaders, break riders).
Pro tip
Mountain passes and climbs are bib photographer gold. Riders string out, effort is visible, and back bibs are exposed. Flat sections through town = peloton chaos = fewer usable bib shots. Budget your moto route accordingly.
Create a stage folder in RaceTagger (one folder per stage, labeled clearly: 'Stage 5 — Tour de France 2026'). Import all RAW/JPEG files from moto-photographers. Import the starting list CSV with bib numbers and rider names. Run batch processing at ~4 seconds per photo. For 5,000 stage photos, budget ~20-30 minutes of processing.
Pro tip
Pro cycling events have tight deadlines: results needed within 3 hours. Start processing while you're still out covering neutral zones and podium ceremonies. By the time you're back at the hotel, tagging is done.
RaceTagger flags low-confidence detections. In cycling, most flagged photos will be from the main peloton (bibs hidden by saddle bags and other riders). Don't waste time reviewing these — they're not the priority. Focus review on breakaway riders, leaders, and climbs where bibs are visible and matter editorially. Manually confirm detected numbers for any ambiguous reads in the breakaway.
Pro tip
You don't need 100% accuracy on the main field. Fans and media care about the breakaway, the leader, and who attacked. Get those right, and low-confidence main peloton shots are acceptable. Prioritize like a pro photographer would.
Export the tagged data as XMP sidecar files. Use RaceTagger's 'stage results' export (rider rankings by appearance count) to generate a quick summary: 'Rider #47 (Juan García) appeared 127 times across the stage.' This summary feeds news outlets. Export full metadata for archival and next-day publishing.
Pro tip
Grand Tour media needs stage results ASAP for broadcast commentary and results pages. Have a template ready: top 5 riders featured, number of photos each. Deliver this to journalists before full galleries go live.
Release immediate stage results to news partners and official media channels (within 3 hours for Grand Tours). Publish tagged gallery next day with team-aggregated results (how many photos of each rider, breakdown by team). Archive all stage photos for the final book/DVD release at end of event.
Pro tip
Stage-by-stage delivery builds narrative. Day 1: preview. Days 2-15: daily results and featured rider galleries. Final day: complete event gallery. Multi-touch coverage gets more media pickup and social shares than a single final dump.
Why it's hard: Unlike every other sport, cycling bibs are on the BACK, not the front. Saddle bags, repair kits, and bottles obscure them. Riders in the middle of the peloton have bibs completely blocked by the rider in front. Front-facing detection models don't work.
How AI helps: RaceTagger's cycling-specific model is trained on back-bib photography. It detects bib regions from behind, accounts for typical saddle bag placement, and reads bibs visible between bottles. Less confident on main peloton (expected), very confident on breakaways where bibs are exposed.
Why it's hard: Main peloton shots have 20-50 riders visible, many overlapping. Multiple bibs in frame, many partially hidden by other riders. Distance varies wildly (riders at 50m away are tiny; leaders at 5m away fill the frame). Bib sizes range 10x.
How AI helps: AI detects all visible bibs regardless of size and overlap. It prioritizes clear reads and flags partial/occluded ones. In peloton shots, expect 30-40% accuracy due to occlusion, but RaceTagger still finds leaders and prominent riders reliably.
Why it's hard: A single rider (bib #47) might be photographed 150 times throughout a stage as they attack, drop back, get drafted, climb. Position changes constantly. Difficult to verify if a low-confidence read at KM 80 is the same rider as at KM 140 without manual review.
How AI helps: RaceTagger generates a 'rider appearance summary' showing how many times each number appears and in which photos. Compare context: if #47 appears 150 times consistently across the stage, you can trust it. If it appears 3 times in the breakaway and 0 times elsewhere, review those 3 frames.
Why it's hard: Moto-photographers shoot from moving motorcycles, creating motion blur relative to the riders (though riders are also moving). Bibs blur at the edges, numbers lose clarity. Burst shooting captures some sharp frames but mixed with blurry ones.
How AI helps: AI handles motion blur better than traditional OCR by processing the entire bib context. RaceTagger flags very blurry frames but still reads most burst sequences. Use the sharpest frame per moment automatically.
Why it's hard: All riders on the same team wear identical jerseys. Ten riders in the same kit look identical from 50m away. Only the bib distinguishes them. If the bib is unreadable, you're guessing which teammate it is.
How AI helps: RaceTagger solves this by reading the bib number uniquely. Confidence is lower (75-85% for identical jersey riders in peloton), but when it reads, you know exactly who it is. Manual review is faster because you're comparing readable bibs, not trying to ID by face or jersey.
Manual Tagging
4-6 hours for 5,000 stage photos (experienced cycling photographer + 1-2 assistants)
88-92% on breakaway riders, 60-70% on main peloton (too much overlap to read accurately)
With RaceTagger AI
~30 minutes for 5,000 stage photos (automatic processing + light review)
95-97% breakaway/climbs (where bibs are visible), 75-85% main peloton (with flagging for ambiguous reads)
Real-world scenario
You're the lead moto-photographer for Stage 7 of the Tour de France. You shoot from the chase motorcycle, capturing the breakaway (6 riders), attacks (multiple), and final climb to the finish. By 4 PM, you have 6,500 photos. You hand off your cards to the support team. By 4:15 PM, the team has imported starting list, photos, and hit 'Process' in RaceTagger. By 4:45 PM, processing is done. You review critical shots: the breakaway (all clearly read), the podium finish (3 riders clearly tagged), the yellow jersey's attack on the climb (read perfectly). You spend 20 minutes reviewing and confirming 15 ambiguous peloton shots. By 5:15 PM, you export stage results and send to broadcasters: 'Stage 7 highlights: Breakaway rider #47 García, 127 photos. Leader #12 Visma, 95 photos.' News outlets use this for commentary within 5 minutes. By 6 PM, full gallery is live on the event website — fans are already tagging and sharing. Competitors who still use manual tagging are just starting their review process.
Same-day delivery + stage results = you get hired for every Grand Tour. You're not the only photographer, but you're the one delivering FIRST. Media partners come back for you year after year. Efficiency = career advantage.
500 free tokens. Upload stage photos from your last cycling event and see how RaceTagger handles back-bib detection — no credit card needed.
Start tagging for free →How does RaceTagger detect bibs that are BEHIND saddle bags and partially hidden?
It's trained specifically on cycling back-bib photos. The model learns to recognize bib patterns even when 40-50% is obscured by bags or bottles. Confidence is lower than for front-visible bibs (80-85% vs 95%), but it still works reliably on breakaway riders where bibs are less occluded.
What happens when we have 40 identical-jersey riders in the peloton and only some bibs are readable?
RaceTagger reads whatever bibs are visible and flags the rest as 'low confidence' or 'undetectable'. You manually review the flagged ones. With the starting list, you can at least narrow it down: unreadable #23 in a Visma shot is one of the 5 Visma riders. Confirm by context (position, effort, timing).
How do we handle stage-to-stage delivery for a Grand Tour where media needs results the SAME HOUR?
Batch process immediately after each stage ends (30 min of processing). Do light review of critical shots (breakaway, leaders, attacks) while processing runs. Export stage results within 2 hours. Full gallery next morning. This matches pro event expectations.
Can RaceTagger handle both pro racing (3,000-8,000 photos per stage) and sportive Gran Fondos (500-1,000 photos)?
Yes. Sportives have smaller fields (100-1,000 riders vs 176 in a Grand Tour) and less density. Accuracy is actually HIGHER on sportives because there's less overlap. One batch process covers both.
What if a rider drops out mid-stage? The starting list includes them, but we have zero photos of them. Is that a problem?
No. RaceTagger only tags riders whose bibs appear in photos. If a rider DNF (did not finish), they simply won't appear in the photo gallery. Starting list is the reference; detection is based on what's actually photographed.
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