A pretrained planets classifier that sorts an image into one of 10 categories — what type of planet it is. Use the planets 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 30 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": "55 Cancri E",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 10 planets 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 used by researchers and astronomers to filter out false images in datasets involving celestial bodies. By accurately identifying and classifying images of planets, researchers can focus on authentic data for their analysis and studies.
Educational technology platforms can implement this function to enhance learning materials in astronomy classes. By distinguishing false images from genuine ones, students can engage with verified content, fostering a more accurate understanding of planetary science.
Online astronomy forums can utilize this function for content moderation to ensure users share only accurate representations of planets. By automatically flagging or removing false images, these platforms maintain a high standard of content integrity and user trust.
Space agencies can employ this identifier in the analysis of images taken from planetary exploration missions. By eliminating false images, mission teams can concentrate on valid data for mission planning and scientific analysis, ensuring resource efficiency.
Social media platforms can leverage this technology to identify and curate space-themed content, ensuring that users see genuine images of planets. This would not only improve user experience but also reduce misinformation and promote educational content.
AI developers can use the “planets” identifier to refine training datasets for machine learning models focused on space imagery. By filtering out false images, developers can improve model accuracy and reliability, leading to better performance in applications such as autonomous robotic navigation in space.
Online galleries and digital art websites can utilize this identifier to ensure that images labeled as planets are genuine representations, aiding artists and curators in maintaining high standards. By filtering out manipulative or false representations, platforms can enhance user trust and experience in their digital collections.
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 planets 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.