A pretrained cat tail position classifier that sorts an image into one of 10 categories — the position of a cat's tail. Use the cat tail 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 10 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": "Curled",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 10 cat tail 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.
This use case involves analyzing the tail position of cats in various environments to better understand their emotional states and behavior. By classifying tail positions, pet owners and veterinarians can gain insights into anxiety, aggression, or contentment, leading to improved care and management of feline companions.
Veterinary schools can use the tail position classification function as a teaching aid. By providing students with a database of tail position images, they can learn to identify specific behaviors and emotional cues in cats, enhancing their diagnostic and communication skills with pet owners.
Pet tech companies can integrate the tail position identifier into smart cameras designed for monitoring pets at home. This feature would alert pet owners to potential stress or distress in their cats, enabling timely interventions and improving pet welfare.
Mobile applications aimed at cat owners could utilize the tail position identifier to create interactive features. Users could upload images of their cats, and the app would analyze the tail position to deliver insights or tips on how to improve their pet's happiness based on the identified behavior.
Animal shelters can leverage the tail position classification to assess the emotional health of cats in their care. Understanding tail positions can help staff identify cats that may be stressed or anxious, allowing for appropriate interventions to improve their chances of adoption.
Researchers in animal behavior can use the tail position identifier to conduct studies on feline emotions and social interactions. The data collected could help in understanding how environmental changes or social structures impact a cat's emotional well-being.
Companies that sell cat products can analyze tail position data to tailor their marketing strategies. By understanding the behaviors associated with different tail positions, they can create targeted campaigns that address specific concerns of cat owners, such as stress relief products or enrichment toys.
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 cat tail 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.