A pretrained cavities classifier that sorts an image into one of 5 categories — what type of cavity it is. Use the cavities 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 5 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": "Advanced Cavity",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 5 cavities 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 assist dental professionals by automatically identifying images of teeth with cavities during patient consultations. By providing accurate classifications, it can enhance diagnostic processes, allowing dentists to focus on treatment options rather than image evaluation alone.
In telehealth settings, this function can facilitate remote dental assessments by identifying cavities in images submitted by patients. It helps dental practitioners provide preliminary evaluations and advice before scheduling in-person visits, improving patient engagement and satisfaction.
Educational institutions can integrate this function into their teaching tools to help dental students learn about cavity identification. By providing feedback on students' image assessments, it can enhance learning outcomes and prepare students for real-world scenarios.
Insurance companies can leverage this image classification function to verify claims related to dental treatments for cavities. By automating the identification process, it streamlines claims processing and minimizes fraudulent claims while ensuring legitimate ones are rapidly approved.
Dental clinics can use this function to engage in preventive care by identifying at-risk patients through their dental images. By tracking cavity developments over time, clinics can implement targeted educational campaigns and treatment plans to promote better oral hygiene practices.
This feature can be incorporated into mobile dental health applications, enabling users to scan their teeth and receive instant feedback on cavity detection. Such functionality can empower individuals to take proactive steps in seeking dental care and increase regular monitoring of their oral health.
Researchers developing new dental treatments can utilize this classification function to analyze large datasets of dental images. By understanding patterns in cavity formation and response to treatments, they can contribute to advancements in dental science and improved patient outcomes.
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 cavities 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.