A pretrained dog color classifier that sorts an image into one of 10 categories — what color the dog is. Use the dog color 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 18 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": "Albino",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 10 dog color 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.
A pet adoption platform can use the 'dog color' identifier to filter search results based on desired dog colors, helping potential adopters find their perfect match quickly. This tool can enhance decision-making by providing visual matches with accurate color identification.
Dog breeders can utilize this function to categorize their litters by color, facilitating easier management of their breeding operations. This can also streamline marketing efforts, as breeders can showcase specific color traits that potential buyers may be interested in.
Insurance companies can integrate the 'dog color' identifier into their claim processing systems to validate information submitted by policyholders. This can help prevent fraudulent claims related to the expected appearances of insured pets, ensuring more accurate assessments and pricing.
Training centers can use color identification to tailor training materials and assessments for different dog breeds and their color variations. This can improve the training experience by acknowledging behavioral tendencies associated with certain colors and breeds.
Online pet supply stores can implement this feature to recommend products based on the dog's color, such as collars, leashes, or clothing. This personalized shopping experience can lead to increased sales and customer satisfaction as owners find products that match their pets’ appearances.
Social media platforms focused on pets can leverage the color identification feature to enhance user engagement by tagging and categorizing images of dogs. By allowing users to search and filter photos by dog color, the platform can create a more curated and visually appealing experience.
Veterinary clinics can apply this function in their patient management systems to streamline data entry for pet records. By automatically identifying and recording the pet's color, clinics can maintain more accurate patient information and improve the personalization of care plans for each individual animal.
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 color 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.