A pretrained construction material classifier that sorts an image into one of 10 categories — the type of construction material used. Use the construction material 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 26 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": "Aluminum",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 10 construction material 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 manufacturing plants that produce construction materials. By automatically identifying false images of construction materials, businesses can ensure that defective or substandard products do not reach the market, thus maintaining product integrity and brand reputation.
The false image classification function can assist in managing inventory by verifying received materials. By cross-checking images against a database, it can prevent the acceptance of incorrect materials, reducing waste and improving the efficiency of supply chains.
This identifier can be utilized on construction sites to ensure that the correct materials are being used for specific tasks. By identifying erroneous images, it helps avoid hazardous substitutions or misuses of materials, thereby enhancing worker safety and compliance with building standards.
Construction companies can leverage this function to improve the accuracy of project estimations. By verifying images of construction materials, they can better assess costs and quantities, leading to more accurate budgeting and resource allocation.
The function can be used to ensure that materials meet industry regulations and standards. By identifying false images, it helps companies maintain compliance with safety and quality guidelines, minimizing the risk of legal issues or fines.
Construction firms can utilize the identifier in training programs for employees to recognize and distinguish various construction materials. By integrating false image classification into training modules, new workers can develop their skills in material identification more effectively.
Retailers of construction materials can implement this function in e-commerce platforms to verify customer-uploaded images of products before order fulfillment. This ensures that the correct item is shipped, reducing returns and enhancing customer satisfaction.
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 construction material 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.