A pretrained dog fur type classifier that sorts an image into one of 10 categories — the type of fur your dog has. Use the dog fur type 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": "Bristly",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 10 dog fur type 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 system can be used by pet grooming salons to recommend the best grooming products and services based on the identified fur type of dogs. By understanding whether a dog's fur is short, long, curly, or wiry, groomers can tailor their services to meet the specific needs of each pet, improving customer satisfaction and outcomes.
Veterinarians can utilize this classification function to provide personalized care advice based on a dog's fur type. Different fur types may require distinct approaches to skin care, parasite prevention, and nutritional advice, leading to improved health and wellness for pets.
Animal shelters and rescue organizations can leverage this technology to provide potential adopters with insights into the fur characteristics of dogs available for adoption. This information can help match adopters with dogs that fit their grooming availability, lifestyle preferences, and allergy considerations.
Online pet supply retailers can use this classification function to recommend grooming tools and products tailored to specific fur types. This targeted marketing approach can enhance customer experience and boost sales by ensuring customers find the right products for their pets quickly.
Dog breeders can use the fur type identification function to analyze and make informed breeding decisions. Understanding the fur types that result from different breeding pairs can help breeders select traits that meet market demand and improve the health and appearance of future litters.
Clothing brands specializing in dog apparel can employ this technology to design and market fashion items based on the different fur types. This can lead to innovations in style and functionality, ensuring that products cater effectively to dogs' comfort and unique grooming needs.
Insurance companies can use fur type information to assess risk and tailor pet insurance plans more effectively. Certain fur types may be prone to specific health issues, and understanding these aspects can help insurers create products that offer comprehensive coverage that aligns with the animal's care requirements.
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 dog fur type 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.