A pretrained cat size category classifier that sorts an image into one of 5 categories — what size category the cat falls into. Use the cat size category 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": "Extra Large",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 5 cat size category 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.
Pet adoption websites can utilize the "cat size category" identifier to help potential adopters find cats that fit their living space and lifestyle. By categorizing cats into size groups, adopters can make more informed decisions about which pets are best suited for their homes.
Insurance companies can leverage the size classification of cats to evaluate potential pet insurance policies more accurately. Different size categories could indicate different risk profiles for health issues or behavioral problems, allowing for tailored premiums.
Manufacturers of pet food can use this identifier to create size-specific products targeting cat owners. By understanding size categories, brands can develop appropriate portion sizes and nutritional formulations that meet the needs of various cat breeds.
Veterinary clinics can implement the identifier for automating size-related aspects of pet care, such as dosage calculations for medications or vaccinations. This ensures accurate administration according to the pet’s size category, improving overall healthcare outcomes.
Online pet product retailers can enhance their shopping experience by recommending size-appropriate items based on the "cat size category." This would help reduce returns and increase customer satisfaction as buyers will select products suitable for their pets.
Training facilities can use the size identifier to tailor their training programs for different cat sizes, ensuring that classes accommodate specific behavioral traits associated with each category. This approach could improve the training effectiveness and client satisfaction.
Researchers studying feline behavior can utilize the "cat size category" classification to analyze trends and patterns based on size. This data could lead to insights about the social behaviors, stress responses, and adaptability of different sized cats in various environments.
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 size category 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.