A pretrained racing flags classifier that sorts an image into one of 10 categories — what type of racing flag is being displayed. Use the racing flags API immediately, no training required, then adapt it to your own data when you need more.
Drop in a photo and get the prediction back. No signup, no setup.
A sample of the 20 labels this pretrained classifier chooses between.
Need a label that isn't here? Clone the classifier into your Nyckel console and edit the label set to fit your data.
Once you've added this classifier to your console, you get your own copy of it behind your own endpoint. Invoke it with any HTTP client:
curl
curl -X POST "https://www.nyckel.com/v1/functions/YOUR_FUNCTION_ID/invoke" \
-H "Authorization: Bearer $NYCKEL_ACCESS_TOKEN" \
-H "Content-Type: application/json" \
-d '{"data": "https://example.com/photo.jpg"}'Python
import requests
# Get an access token: https://www.nyckel.com/docs/api/overview/authentication/
token = "YOUR_ACCESS_TOKEN"
response = requests.post(
"https://www.nyckel.com/v1/functions/YOUR_FUNCTION_ID/invoke",
headers={"Authorization": "Bearer " + token},
json={"data": "https://example.com/photo.jpg"},
)
print(response.json())Example response
{
"labelName": "Black",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 10 racing flags categories, served on Nyckel's own infrastructure — your image stays on Nyckel.
Send an image URL or file to the invoke endpoint; the response is a label with a confidence score.
Clone it, then correct predictions and add your own samples in the console — Nyckel retrains automatically, turning this into a custom model tuned to your data.
This function can be integrated into sports analytics platforms to automatically identify racing flags during live events. By processing video feeds, it can help analysts and teams understand race dynamics and safety measures more effectively.
Media companies can utilize the racing flags identifier to enhance live broadcasts of motorsport events. By providing real-time updates and visual alerts when specific flags are displayed, viewers can gain deeper insights into race conditions and incidents.
Racing organizations can employ this function to ensure that regulatory compliance measures are met during events. By continuously monitoring flag displays, the system can alert officials to any infractions in real time, improving race safety.
Simulator software for racing drivers could incorporate the racing flags identifier to teach drivers how to respond to different racing conditions. By simulating real-time scenarios with accurate flag detection, drivers can better prepare for actual race situations.
After races conclude, teams and analysts can leverage the racing flags identifier to review how often and when different flags were presented. This data can be critical in understanding race strategy, decisions made under pressure, and overall performance.
Manufacturers of racing equipment can use insights from the racing flags identifier to design better safety gear. By studying flag incidents and corresponding reactions, developers can pinpoint critical moments that require enhanced protective measures for drivers.
Sport franchises and event organizers can use the racing flags identifier to develop interactive apps for fans attending events. By alerting fans on their devices when flags are displayed, it can enhance their understanding of race strategies, fostering greater engagement with the sport.
A zero-shot classifier uses a large foundation model's general knowledge to pick between your labels — no task-specific training, so new or edited labels work immediately. A Nyckel-trained classifier has been trained on labeled examples and runs on Nyckel's own infrastructure, which typically makes it faster, cheaper per call, and more accurate on data that resembles its training set. The "Under the hood" section on this page shows which kind this classifier is, and any classifier can be adapted into a trained one by adding your own examples.
Honestly: we can't know in advance — it depends on your data stream and how closely it resembles what this classifier has seen. The reliable way to find out is to measure it on your own data: start invoking the classifier with real traffic, or upload and annotate a set of images in the console — make sure they look like your production data, not idealized examples. Nyckel's evaluation metrics then show you exactly how it performs on that data before you rely on it.
No classifier is perfect, so Nyckel is built around the correction loop: invokes can be captured for review, you confirm or correct predictions in the console, and corrections become training data. Over time the model adapts to your data distribution — accuracy on your traffic improves with use rather than staying fixed.
No. This racing flags classifier works out of the box — clone it into your console and you'll have your own API endpoint in under a minute. Training data only enters the picture when you want to adapt it: your corrected predictions and uploaded samples improve the model, and you can also edit the label set to match your needs.
Trying the classifier on this page is free with no signup. Cloning it requires a free account, and the free tier covers your first API calls each month — see nyckel.com/pricing for current limits and paid tiers.
Add this pretrained classifier to your Nyckel console — you'll get a live API endpoint in under a minute, and a path to a custom model when you need one.