A pretrained if image shows artifacts classifier that sorts an image into one of 2 categories. Use the if image shows artifacts 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": "Artifacts Present",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 2 if image shows artifacts 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 utilized by museums to automatically identify and classify artifacts in images. By analyzing images of potential artifacts, museums can streamline their inventory processes, ensuring that each item is documented and categorized correctly.
E-commerce platforms can implement this image classification function to assess product images for authenticity or quality issues. By flagging images that show artifacts, companies can maintain high standards for product presentation and reduce the risk of customer dissatisfaction.
Libraries and archives can leverage this function to identify and catalog historical documents that may have artifacts. This classification aids in the preservation of cultural heritage by directing resources towards the restoration of damaged or deteriorating items.
Art conservation specialists can use this classification system to assess artworks for signs of degradation or damage. By automatically identifying artifacts in images, professionals can prioritize restoration efforts and allocate funding more effectively.
Manufacturing companies can integrate this function into their quality control processes to detect artifacts in product images. By automating the identification of defects or anomalies, businesses can enhance production efficiency and reduce the rate of returns.
Auction houses can employ this function to verify the authenticity of items by examining images of artifacts. Accurate classification helps in preventing the sale of counterfeit or misidentified items, ultimately building trust with buyers.
Organizations managing vast media libraries can utilize this function for image sorting and cataloging. By identifying images that display artifacts, they can streamline their database, making it easier to find high-quality images while maintaining accurate records of items.
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 image shows artifacts 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.