A pretrained dog toy presence classifier that sorts an image into one of 2 categories. Use the dog toy presence 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 2 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": "Dog Absent",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 2 dog toy presence 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 be implemented in pet stores to automatically identify and categorize dog toys in inventory. The system can streamline stock management by notifying staff when specific toy types are running low or require restocking, ultimately enhancing operational efficiency.
Dog walking businesses can use this function to monitor the types of toys their canine clients have at home. By understanding toy preferences, service providers can tailor their walks or play sessions with appropriate toys, ensuring a more engaging experience for the dogs.
Animal shelters can utilize this identifier to assess the presence and variety of toys available in dog adoption environments. This information can help staff improve the play areas, making them more appealing to potential adopters and promoting dog well-being.
Online pet supply retailers can integrate this function to enhance their recommendation engines. By analyzing user-uploaded photos of their dogs alongside toys, the platform can suggest products that match the dog's interests, boosting customer satisfaction and sales.
Veterinarians and dog trainers can use toy presence data to gain insights into a dog’s behavior and preferences. Understanding which toys are present can help in diagnosing anxiety or aggression issues, leading to better-targeted training and care strategies.
Brands can leverage this image classification function to engage pet influencers in campaigns. By identifying the presence of their toys in influencer content, brands can track the effectiveness of marketing initiatives and refine their outreach strategies based on what resonates with the audience.
Subscription box services can use the identifier to curate customized toy selections based on the client's dog's preferences shown in uploaded images. This targeted approach can enhance customer loyalty and satisfaction, leading to higher retention rates.
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 toy presence 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.