Identify pro cycling teams
using AI
Below is a free classifier to identify pro cycling teams. Just upload your image, and our AI will predict what pro cycling team a rider belongs to - in just seconds.
Contact us for API access
Or, use Nyckel to build highly-accurate custom classifiers in just minutes. No PhD required.
Get started
import nyckel
credentials = nyckel.Credentials("YOUR_CLIENT_ID", "YOUR_CLIENT_SECRET")
nyckel.invoke("pro-cycling-teams", "your_image_url", credentials)
fetch('https://www.nyckel.com/v1/functions/pro-cycling-teams/invoke', {
method: 'POST',
headers: {
'Authorization': 'Bearer ' + 'YOUR_BEARER_TOKEN',
'Content-Type': 'application/json',
},
body: JSON.stringify(
{"data": "your_image_url"}
)
})
.then(response => response.json())
.then(data => console.log(data));
curl -X POST \
-H "Content-Type: application/json" \
-H "Authorization: Bearer YOUR_BEARER_TOKEN" \
-d '{"data": "your_image_url"}' \
https://www.nyckel.com/v1/functions/pro-cycling-teams/invoke
How this classifier works
To start, upload your image. Our AI tool will then predict what pro cycling team a rider belongs to.
This pretrained image model uses a Nyckel-created dataset and has 20 labels, including Ag2R Citroën, Alpecin Deceuninck, Astana Qazaqstan, Bikeexchange Jayco, Bora Hansgrohe, Cofidis, Dimension Data, Ef Education Nippo, Groupama Fdj and Ineos.
We'll also show a confidence score (the higher the number, the more confident the AI model is around what pro cycling team a rider belongs to).
Whether you're just curious or building pro cycling teams detection into your application, we hope our classifier proves helpful.
Recommended Classifiers
Need to identify pro cycling teams at scale?
Get API or Zapier access to this classifier for free. It's perfect for:
- Team Sponsorship Validation: Pro cycling teams can use the false image classification function to verify that images shared by potential sponsors, showing their branding on team kits, are authentic and appropriately placed. This improves trust and ensures that sponsors receive the visibility they paid for.
- Social Media Authenticity: The functionality can help pro cycling teams assess user-generated content on social media that claims to feature team members or events. By classifying images, teams can engage with legitimate fan posts while dismissing counterfeit or misrepresented content.
- Merchandise Protection: Pro cycling teams can utilize image classification to identify counterfeit merchandise being sold online. By detecting false images of team gear or apparel, they can take action against vendors that infringe on their brand and protect their revenue streams.
- Event Recognition: This function can assist in distinguishing between official photos from sanctioned events and unofficial or altered images claiming to be from such events. Accurate classification ensures that only credible images are used for marketing and promotional materials.
- Athlete Representation Monitoring: Teams can monitor images that claim to showcase their athletes engaging in competitions or events. This helps them maintain accurate representations of their athletes' appearances and activities while safeguarding their reputation.
- Brand Integrity Assurance: Pro cycling teams can employ the image classification function to ensure that images circulating in the press and online accurately represent their brand identity. This poses an opportunity to address misinformation and manage their public image effectively.
- Analysis of Competitor Presence: The functionality can be applied to assess images that indicate the presence of rival teams at events or in promotional content. Understanding competitor actions through verified images can help teams craft better strategies for marketing and sponsorship opportunities.