A pretrained weather conditions classifier that sorts an image into one of 10 categories — what weather conditions are present. Use the weather conditions 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 17 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": "Clear",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 10 weather conditions 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 automatically classify weather conditions by analyzing images from public and private cameras. By providing real-time insights into current weather conditions, businesses can make informed decisions about operations, safety measures, and customer communications.
Farmers can utilize this image classification function to monitor weather conditions affecting their crops. By accurately identifying weather types, farmers can better plan irrigation, pest control, and planting schedules, ultimately enhancing yield and reducing resource wastage.
Airlines and travel agencies can implement this function to analyze weather conditions in various destinations. This capability ensures that travelers are informed about potential weather-related disruptions, allowing them to plan their travels more effectively and reducing customer service inquiries.
Emergency response teams can leverage this function to assess weather conditions during natural disasters like floods or storms. Real-time image analysis can enhance situational awareness, enabling faster and more effective response strategies to protect lives and property.
Shipping companies can use this technology to classify weather conditions at different checkpoints along their routes. By anticipating adverse weather conditions, companies can reroute shipments, optimize delivery schedules, and improve overall supply chain efficiency.
City planners and engineers can employ this function to assess the impact of different weather conditions on urban infrastructure. By understanding how various weather scenarios may affect public spaces and amenities, they can design more resilient, weather-adaptive structures and transit systems.
Retail businesses can analyze weather conditions through this function to optimize stock levels and marketing campaigns. By predicting customer behavior based on weather classifications, companies can tailor their promotions, ensuring the right products are available at the right time to maximize sales.
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 weather conditions 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.