A pretrained coffee maker presence classifier that sorts an image into one of 2 categories. Use the coffee maker 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": "Absent",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 2 coffee maker 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.
The coffee maker presence identifier can be integrated into smart home systems to enhance kitchen automation. By detecting the presence of a coffee maker, the system can adjust appliances and settings to optimize energy consumption and improve user convenience.
Coffee shops can utilize the identifier to monitor equipment presence and perform regular audits of coffee-making stations. This can help in inventory management by keeping track of available coffee makers, ensuring that all necessary equipment is operational and ready for service.
Retailers can leverage this technology to identify whether a customer owns a coffee maker, allowing for targeted marketing strategies. When a customer's device recognizes the presence of a coffee maker, promotional offers for coffee products or accessories can be sent directly to their mobile device.
Home energy management systems can incorporate the coffee maker presence identifier to provide insights into household energy consumption. By tracking active coffee makers, users can receive recommendations on usage patterns and tips for reducing energy costs.
The identifier can serve as a trigger point for interactions among IoT devices in a home setting. For instance, if a coffee maker is detected, the system can notify the user to prepare their coffee or even preheat the kitchen based on the time of day.
Hotels or Airbnb providers can use the coffee maker presence identifier to personalize guest experiences. If a coffee maker is detected in a room, staff can ensure that coffee supplies are stocked and offer complimentary coffee options to enhance comfort.
Commercial coffee equipment providers can implement this identifier to monitor their machines remotely. By ensuring that coffee makers are present and in use, service providers can receive alerts for maintenance needs, ensuring consistent performance and reducing downtime for businesses.
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 coffee maker 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.