A pretrained plane type classifier that sorts an image into one of 10 categories — what type of plane it is. Use the plane type 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 15 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": "Biplane",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 10 plane type 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.
Airlines can utilize the 'plane type' identifier to efficiently manage and optimize their fleet. By accurately classifying aircraft types, airlines can make informed decisions about maintenance schedules, operational efficiency, and resource allocation.
Security personnel can employ the plane type identifier to quickly identify and classify aircraft during pre-flight checks. This function can assist in identifying suspicious activities or unauthorized aircraft, thereby enhancing airport security measures.
Air traffic controllers can use this function for monitoring aircraft on the runway or taxiing areas. By ensuring accurate identification, it helps maintain safe distances between different types of planes based on their size and operational capabilities.
Aviation insurance companies can leverage the plane type identifier to assess risk levels associated with different aircraft. By classifying planes accurately, insurers can refine their underwriting processes and establish more precise premium rates based on flight operations.
Maintenance crews can implement the plane type identifier to streamline service schedules for various aircraft models. This function helps in prioritizing maintenance tasks and ensuring compliance with specific regulatory requirements for each aircraft type.
Aviation training organizations can utilize the plane type identifier to enhance their training curriculum for pilot students. By classifying aircraft accurately, they can ensure that trainees are familiar with the specifications and handling protocols of various plane types.
Companies that provide aerial imaging services can use the plane type identifier to determine appropriate equipment and methodology for capturing images. This function assists in optimizing flight paths and equipment used based on the type of aircraft being utilized for aerial surveys.
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 plane type 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.