A pretrained rust presence classifier that sorts an image into one of 2 categories. Use the rust 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": "Clean",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 2 rust 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.
Implementing a rust presence identifier can enhance predictive maintenance for heavy machinery across various industries. By monitoring equipment for signs of rust, companies can schedule timely repairs or replacements, preventing costly downtime and extending asset lifespan.
A rust identification system can be integrated into manufacturing lines to ensure the quality and durability of metal components. By detecting rust early in the production process, manufacturers can reduce defects and maintain higher standards for their products.
Municipalities and engineering firms can use rust detection technology for the inspection of bridges, buildings, and other structures. Early identification of rust allows for proactive maintenance strategies, enhancing public safety and reducing the risk of structural failures.
Automotive manufacturers and service centers can utilize rust presence detection to monitor vehicles during production and servicing. Identifying rust ensures that cars meet safety standards and that vehicles are protected against corrosion during their lifecycle.
Companies involved in the procurement of metal materials can use rust detection systems to assess the quality of incoming shipments. This helps ensure that only non-corroded materials are used in production, minimizing defects and maintaining product integrity.
The shipping and maritime industry can implement rust identification for regular maintenance checks on vessels. Detecting rust on hulls and other components helps prevent structural failures and ensures compliance with safety regulations.
In the oil and gas and renewable energy sectors, identifying rust in pipelines and equipment can be crucial to safety and operational efficiency. Using a rust presence identifier allows companies to anticipate maintenance needs, reduce environmental risks, and enhance the overall reliability of their operations.
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 rust 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.