A pretrained if barcode is scratched classifier that sorts an image into one of 2 categories. Use the if barcode is scratched 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 if barcode is scratched 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 use case involves using the barcode identification system in manufacturing plants to detect scratched barcodes on products. If a barcode is scratched, the system can flag the item for further inspection or automatic rework, ensuring that only quality products reach customers.
In warehouses, this function allows businesses to monitor inventory levels effectively. When a scratched barcode is identified, the system can prompt staff to re-scan or replace the barcode, maintaining accurate inventory records and reducing the risk of mismanagement.
Retail environments can implement this system at checkout counters to quickly identify scratched barcodes on items. This helps cashiers manage transactions more efficiently by alerting them to items that require manual assistance, minimizing checkout delays and improving customer satisfaction.
Shipping companies can utilize this identifier to verify the condition of barcodes on packages before dispatch. Scratched barcodes can hinder delivery processes, so identifying them allows for retagging or resealing, facilitating smooth logistics and timely delivery.
In e-commerce, this function helps identify products with unreadable or scratched barcodes during returns processing. By quickly flagging these items, returns can be managed more efficiently, ensuring proper handling and reducing losses from returned merchandise.
Service teams can use this identification feature while on-site for equipment servicing. If a scratched barcode is detected on machinery, technicians can be prompted to document or replace the barcode before completing the service ticket, ensuring accurate service records.
Food manufacturers and retailers can use this function to ensure that compliance labels on food products remain legible. Detecting a scratched barcode can trigger a protocol for immediate repackaging or re-labeling, thereby enhancing traceability and adherence to health regulations.
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 barcode is scratched 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.