A pretrained bite classification classifier that sorts an image into one of 10 categories — what type of bite it is. Use the bite classification 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": "Anterior Open Bite",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 10 bite classification 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 be utilized in veterinary clinics to automatically classify images of animal bites, distinguishing between bites that require immediate medical attention and those that can be treated at home. By streamlining the diagnosis process, veterinarians can focus on more complex cases and increase their efficiency.
Insurance companies can employ the bite classification feature to automatically assess and classify images of bite injuries submitted in claims. By accurately identifying the severity and type of bite, insurers can expedite claims processing and reduce fraudulent claims.
Emergency services can integrate this function into their triage systems to quickly classify bite injuries in real-time. This would allow first responders to prioritize cases based on urgency, ensuring the most serious cases receive immediate care.
Law enforcement agencies and legal teams can use the bite classification tool to analyze evidence in cases involving animal attacks. By providing a standardized analysis of bite types and severity, it can aid in legal proceedings and ensure accurate representation of incidents.
Public health organizations can use the classifier to analyze and track image data of animal bites reported in various regions. This information can be invaluable for monitoring trends, identifying high-risk areas, and informing public safety campaigns or interventions.
Veterinary clinics and pet care organizations can implement the bite classification function on their websites or apps to educate pet owners. By allowing owners to submit images of bites for analysis, they can receive tailored advice and understand when to seek veterinary assistance.
Researchers studying animal behavior can classify bite images as part of their studies on aggression and social interactions in animals. This data can provide insights into the causes and contexts of bites, which may contribute to behavioral understanding and improvements in training techniques.
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 bite classification 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.