A pretrained length of pool in feet classifier that sorts an image into one of 10 categories — the length of the pool in feet. Use the length of pool in feet 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 12 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": "1-5 Feet",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 10 length of pool in feet 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 a pool construction business to accurately assess the required materials and costs based on the pool length. By classifying lengths, the system can optimize quotes and improve project planning.
Real estate agents can utilize the pool length identifier to assess property value accurately. Pools are significant selling points, and knowing their dimensions can influence buyer decisions and inform pricing strategies.
Pool maintenance companies can leverage this identifier to create customized maintenance schedules based on pool dimensions. Different pool lengths require varied maintenance frequency and service levels, enhancing customer satisfaction.
Insurance companies can use the pool length data to determine risk factors and appropriate coverage levels. Longer pools may present different liabilities or requirements than shorter ones, helping to establish fair premiums.
Event planners can benefit from this identifier when organizing poolside events. Understanding pool dimensions can assist in planning for seating arrangements, entertainment, and safety measures, resulting in smoother operations.
The function can be utilized by regulatory agencies or pool safety inspectors to ensure compliance with safety standards. Knowing the correct pool length can help assess safety measures like fencing and signage requirements.
Aquatic centers can implement this identifier in managing their facilities more efficiently. Accurate pool length data aids in scheduling swimming lessons, lifeguard assignments, and maintenance downtime to maximize operational efficiency.
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 length of pool in feet 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.