A pretrained damaged phone screens classifier that sorts an image into one of 2 categories. Use the damaged phone screens 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": "Undamaged Screen",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 2 damaged phone screens 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.
Implement the damaged phone screens identifier to inspect smartphones during the manufacturing process. This will automate the quality assurance procedure by quickly identifying screens with defects, ensuring only flawless products proceed to assembly and packaging.
Use the classification function in retail environments to assess returned or exchanged phones. By analyzing the condition of screens, retailers can make informed decisions on refund eligibility and manage inventory more effectively.
Offer repair shops an AI-driven tool to quickly diagnose screen damage on incoming devices. This will allow technicians to provide accurate repair estimates and streamline the workflow by prioritizing devices based on damage severity.
Utilize the identifier in the insurance industry to evaluate claims related to phone damages. By automating the assessment of screen conditions in claims, insurers can expedite processing times and minimize fraud by accurately verifying damage.
Incorporate the damage identification function into warranty service platforms. This will help differentiate between manufacturer defects and user-caused damages, enabling companies to manage warranty claims more effectively and reduce costs associated with false claims.
Enhance customer support services by integrating the classification tool into support chatbots or apps. Customers can upload images of their damaged screens, and the system can classify the damage, providing instant feedback and possible repair solutions.
Leverage the identifier to gather data on screen damage trends across different phone models. This information can guide design improvements, inform marketing strategies, and help manufacturers understand which models are more prone to damage for targeted quality enhancements.
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 damaged phone screens 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.