A pretrained skyline silhouette classifier that sorts an image into one of 2 categories. Use the skyline silhouette 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": "City Skyline",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 2 skyline silhouette 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.
The skyline silhouette identifier can help urban planners assess the aesthetic impact of new buildings in relation to existing cityscapes. By visualizing proposed structures against the skyline, planners can make informed decisions about zoning and development.
Tourism boards can utilize the skyline silhouette identifier to create visually appealing marketing materials that highlight a city’s iconic landmarks. By showcasing distinct silhouettes, they can enhance brand identity and attract visitors looking for unique city experiences.
Real estate developers can use the silhouette identifier to evaluate how new structures will interact with the skyline. This analysis can guide architectural decisions and help in presenting proposals to stakeholders and potential buyers.
The skyline silhouette identifier aids in conducting environmental assessments to understand how new developments might alter scenic views and natural landscapes. This function can provide crucial data to mitigate potential negativity during planning and regulatory approval processes.
Cultural organizations can leverage the silhouette identifier to monitor and document changes to historic skylines, ensuring that development adheres to preservation guidelines. This can help in advocacy efforts to maintain the integrity of culturally significant areas.
Filmmakers and photographers can use the skyline silhouette identifier to select optimal locations for shoots that feature dramatic city backdrops. By identifying key silhouettes, they can enhance visual storytelling and create compelling visual narratives.
Companies developing augmented reality (AR) apps can integrate the skyline silhouette identifier to overlay virtual content on real-world cityscapes. This functionality can enhance user experiences by providing informative graphics that blend seamlessly with the skyline.
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 skyline silhouette 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.