A pretrained if a phone is a smartphone classifier that sorts an image into one of 2 categories. Use the if a phone is a smartphone 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": "Non Smartphone",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 2 if a phone is a smartphone 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.
E-commerce platforms can implement the smartphone identifier to filter and categorize products effortlessly. This feature would allow customers to search for smartphones explicitly, enhancing user experience and increasing the likelihood of conversions.
Insurance companies can utilize the smartphone identifier to assess risk based on the type of device owned by applicants. By determining whether a phone qualifies as a smartphone, insurers can apply differential premiums depending on the phone's value and potential for theft or damage.
Market researchers can leverage the smartphone identifier to collect data about the prevalence and distribution of smartphone ownership across different demographics. This information can help brands tailor their marketing strategies and product offerings to meet consumer demands.
Companies can enhance their customer support systems by implementing the identifier to route inquiries based on the user's device type. By recognizing whether a customer uses a smartphone, the support system can provide relevant, device-specific troubleshooting steps.
Developers can use the smartphone identifier to optimize mobile applications for performance based on the type of devices being used. By distinguishing between smartphones and other devices, app features can be fine-tuned, ensuring a better user experience.
Advertisers can employ the smartphone identifier to refine their targeting strategies for campaigns. Knowing whether users own smartphones allows for more tailored and effective ads, thus enhancing engagement and conversion rates.
Organizations can implement the identifier as part of their security protocols to ensure that only authorized smartphone devices can access sensitive data. By verifying that a device is a smartphone, companies can enforce additional security measures specific to mobile access.
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 a phone is a smartphone 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.