A pretrained star clusters classifier that sorts an image into one of 10 categories — what type of star cluster it is. Use the star clusters 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 26 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": "Andromeda",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 10 star clusters 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 'star clusters' identifier can assist researchers in classifying astronomical images to identify and catalog star clusters more accurately. This can enhance data collection for studies related to the formation and evolution of galaxies.
Amateur and professional astrophotographers can use the function to automatically identify and categorize star clusters in their images. This feature can help photographers refine their work and add precision to their astrophotography portfolios.
The identifier can be integrated into educational software for astronomy courses, allowing students to learn about star clusters interactively. This use case can foster engagement and provide practical experience in astrophysical classification.
Observatories can implement the image classification function to improve their data analysis workflow. By automating the identification of star clusters, staff can focus on interpreting results rather than spending time on manual classifications.
Agencies involved in space missions can utilize the identifier to analyze satellite and space-based telescope imagery. Accurate identification of star clusters is crucial for selecting targets for further exploration and study during missions.
The classification function can be used in citizen science platforms, enabling volunteers to contribute to identifying star clusters in vast datasets. This community engagement can accelerate discoveries in the field of astronomy and encourage public participation in scientific research.
Companies developing AI models for space and astronomy can use the identifier to enrich their training datasets. By precisely labeling images with star clusters, developers can improve the accuracy and performance of their predictive models in celestial object recognition.
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 star clusters 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.