A pretrained tooth color classifier that sorts an image into one of 10 categories — what shade of tooth color it is. Use the tooth color 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 14 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": "Black",
"labelId": "label_...",
"confidence": 0.92
}No labeled training data behind this function — it picks between the 10 labels using a foundation model's general world knowledge (currently GPT-4o-mini). Your image is forwarded to the model provider at inference time. Because it's zero-shot, cloned label edits take effect immediately, no retraining needed.
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 utilized by dental practices to assess the color and condition of patients' teeth. By accurately identifying tooth color, dentists can provide tailored treatment recommendations, improving patient outcomes and satisfaction.
In remote dental consultations, the tooth color identifier can help practitioners analyze patients’ dental health without a physical visit. This technology provides an additional layer of assessment to ensure patients receive appropriate care and advice from the comfort of their homes.
Dental health apps can leverage this function to offer users insights into their oral hygiene. By allowing users to check their tooth color periodically, the app can prompt users to improve their dental habits and notify them of potential issues.
Cosmetic dental services can use the tooth color identifier to create personalized marketing campaigns. By analyzing before-and-after images, clinics can identify ideal candidates for whitening or other cosmetic procedures, leading to targeted promotions and higher conversion rates.
Companies developing oral care products, like whitening toothpaste or mouth rinses, can utilize tooth color analysis to fine-tune their products. Scientific data on how different products improve tooth color can enhance product effectiveness and marketability.
Insurance companies can incorporate tooth color analysis in their dental claim evaluations. By determining the current state of a patient's tooth color, they can assess risk levels and tailor premiums or coverage options accordingly.
Educational institutions can use the tooth color identifier in dental training programs. Students can learn about dental aesthetics and disease progression through practical assessments, enhancing their understanding of oral health indicators.
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 tooth color 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.