A pretrained eye conditions classifier that sorts an image into one of 10 categories — what eye condition it is. Use the eye conditions 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 20 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": "Allergic Conjunctivitis",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 10 eye conditions 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.
Integrating the 'eye conditions' identifier into telemedicine platforms can help healthcare providers remotely assess patients' eye health. Patients can upload images of their eyes for analysis, allowing for timely diagnoses and treatment recommendations without in-person visits.
Eye clinics can utilize this function for automated screening of patients during check-in processes. By quickly classifying images, clinics can streamline patient flow and identify individuals who require immediate attention for potential eye conditions.
Develop mobile apps that educate users about common eye conditions using the classification function. Users can take pictures of their eyes, receive information about potential eye issues, and learn about prevention and treatment options.
Researchers can integrate the identifier into clinical studies focused on eye health to streamline data collection and analysis. By classifying eye images, they can easily find participants with specific conditions and monitor treatment effectiveness over time.
Insurance companies can use the eye conditions identifier to pre-screen claims related to vision issues. This can help expedite claims processing and identify potential cases of fraud by assessing the validity of submitted images.
Train healthcare workers in remote regions by utilizing the eye condition classification feature in educational programs. By analyzing images together, workers can learn to recognize signs of eye diseases and enhance their diagnostic skills.
Partner with manufacturers of smart glasses and vision devices to integrate the eye condition identifier. This can provide real-time feedback and analysis to users, enhancing their understanding of their eye health during daily activities.
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 eye conditions 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.