A pretrained tongue position classifier that sorts an image into one of 10 categories — what the tongue position indicates. Use the tongue position 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": "Asymmetrical Left",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 10 tongue position 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.
The tongue position identifier can assist speech therapists in working with clients who struggle with articulation. By analyzing tongue positioning during speech, therapists can provide targeted feedback and strategies to improve pronunciation.
In virtual reality applications, the tongue position identifier can enhance user immersion by providing real-time feedback on users' speech and pronunciation. This technology can be applied in language learning platforms, helping users adjust their articulation to more closely match native speakers.
Dentists can utilize the tongue position identifier to monitor patients' oral health during regular check-ups. By analyzing the positioning of the tongue, practitioners can identify potential issues such as tongue-tie, which may affect oral hygiene and overall health.
Vocal coaches can leverage this technology to assess and improve the tongue positions of singers and performers. By providing immediate feedback on tongue positioning, coaches can help their clients achieve better vocal tone and style.
In AR applications aimed at language learning, the tongue position identifier can offer feedback on pronunciation. This can create a more interactive and insightful learning experience, encouraging users to improve their spoken language skills effectively.
Game developers can incorporate the tongue position identifier in gaming experiences that involve voice commands or singing. This feature would enhance gameplay by ensuring that player inputs are accurate and responsive to their tongue movements.
Medical researchers can use the tongue position identifier as part of diagnostic tools for neurological conditions that affect speech and movement. By analyzing variations in tongue positioning, healthcare practitioners can gain insights into patients' neurological health and tailor interventions accordingly.
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 tongue position 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.