A pretrained daytime or nighttime skyline classifier that sorts an image into one of 2 categories. Use the daytime or nighttime skyline 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": "Daytime Skyline",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 2 daytime or nighttime skyline 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.
By identifying whether a skyline image was taken during the daytime or nighttime, urban planners can assess lighting conditions and urban aesthetics. This function can assist in the evaluation of how city landscapes are perceived at different times, ultimately influencing design decisions and zoning regulations.
Real estate agencies can leverage this classification to enhance property listings by featuring images of buildings with appealing skyline views. By promoting daytime and nighttime images strategically, agencies can attract buyers who appreciate the visual ambiance a location offers.
Social media platforms can use this function to categorize user-generated skyline images effectively. This categorization can fuel targeted advertising strategies and improve content recommendations based on user preferences for daytime or nighttime photography.
Tourism boards can use this feature to curate promotional materials that highlight the beauty of city skylines at different times. By analyzing which images attract more engagements, they can optimize marketing strategies to promote nighttime attractions versus daytime activities.
Mobile photography apps can incorporate this feature to guide users in capturing the best skyline shots. With the classification, the app can provide tips and enhancements tailored for day or night settings, improving user photography skills.
Surveillance systems can utilize this classification to better assess security footage based on time of day. By distinguishing between daytime and nighttime activities, security teams can make more informed decisions regarding potential threats and monitoring efforts.
Researchers studying light pollution can use this function to analyze skyline images over time. By determining when images were taken, the data can support studies on how city lighting affects nighttime environments and wildlife, informing sustainable urban development practices.
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 daytime or nighttime skyline 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.