A pretrained if there's a bike classifier that sorts an image into one of 2 categories. Use the if there's a bike 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 2 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": "Bike Present",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 2 if there's a bike 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 utilized by bike-sharing companies to monitor bike availability at different stations. By identifying whether bikes are present or missing, the system can optimize maintenance schedules and restocking strategies to enhance user satisfaction.
City authorities can deploy this function to analyze bike traffic patterns in real-time. By incorporating bike identification into traffic management systems, cities can improve infrastructure planning and promote cycling as a sustainable transportation option.
Insurance companies can use this identifier to determine the prevalence of bicycles in various neighborhoods when underwriting policies. This data allows for better risk assessments and tailored premiums based on biking activity levels.
Retailers can integrate this classification into their logistics systems to identify areas with high bicycle activity. By doing so, they can develop eco-friendly delivery options, such as bike couriers, for faster and more efficient deliveries.
Researchers can apply this function in public health studies to examine the relationship between bike presence and community health outcomes. By collecting data on bike usage, they can promote cycling as a means to improve public health initiatives and reduce obesity rates.
Organizations focused on cyclist safety can use this technology to gather data on bike presence in accident-prone areas. This information can be crucial in proposing new bike lanes or safety measures, ultimately leading to safer riding conditions.
Event organizers can use this function to monitor bike congestion in areas where cycling events take place. By understanding bike flow and inventory, they can enhance the event experience and improve logistical planning for both participants and spectators.
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 if there's a bike 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.