A pretrained dog size category classifier that sorts an image into one of 5 categories — what size category the dog belongs to. Use the dog 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": "Giant",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 5 dog 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.
This function can enhance pet adoption websites by categorizing dogs into size categories, such as small, medium, and large. This enables potential adopters to easily filter and find pets that fit their living space and lifestyle.
Veterinary clinics can use this image classification to automate pre-visit assessments. By classifying dogs' size categories, clinics can better prepare for the appropriate care, equipment, and space needed for each visit.
Businesses can implement this function to identify the size of dogs entering their premises. This allows establishments to tailor their environment and services to accommodate various dog sizes, enhancing the experience for pet owners.
Insurance companies can leverage the size category identification to assess risk profiles for different breeds and sizes. This data can lead to more nuanced underwriting processes and potentially better pricing models for dog insurance policies.
Brands targeting dog owners can utilize this function to segment their audience based on dog size. Tailored marketing campaigns can be developed for distinct segments to promote size-appropriate products, such as food, toys, and accessories.
Training facilities can categorize clients based on their dogs' sizes to create more effective training programs. This facilitates the development of specialized training solutions that cater to the specific needs and temperaments of different size categories.
Retailers can use this function to recommend suitable products based on the dog size category detected from images. This offers personalized shopping experiences, thereby increasing sales of size-appropriate accessories, such as collars, harnesses, and beds.
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 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.