A pretrained if diaper is clean classifier that sorts an image into one of 2 categories. Use the if diaper is clean 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": "Diaper Clean",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 2 if diaper is clean 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 integrated into smart diaper systems that monitor the cleanliness of a diaper. Parents receive real-time notifications on their mobile devices, ensuring timely changes and reducing the risk of diaper rash.
In hospitals or daycare centers, this identification function could track the cleanliness status of diapers used on infants. It helps staff maintain hygiene protocols and ensures that all infants receive timely diaper changes, enhancing overall care.
This capability can be embedded into smart home systems for new parents. By connecting with smart diaper bins, the system can alert parents when a diaper needs changing, streamlining care routines and minimizing noise disruptions during sleep hours.
Apps catering to new parents can leverage this function to provide suggestions on changing schedules based on the diaper's cleanliness. This ensures that parents have push notifications for optimal diaper maintenance, promoting better health for infants.
Retailers of diapers can use this identifier in conjunction with machine vision to reflect real-time product quality. By ensuring that only clean, defect-free diapers make it to market shelves, they can improve customer satisfaction and brand loyalty.
Diaper manufacturers can utilize this function during the product testing phase to gauge performance. By analyzing data on how well their products keep infants dry and clean, manufacturers can innovate and enhance their product lines.
By integrating this identifier into connected diapers, businesses can gather anonymized usage data over time. Analyzing this information helps identify patterns in diaper changing habits, helping companies tailor products and marketing strategies to meet consumer needs effectively.
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 if diaper is clean 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.