A pretrained if a tattoo is visible classifier that sorts an image into one of 2 categories. Use the if a tattoo is visible 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": "Tattoo Not Visible",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 2 if a tattoo is visible 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.
Organizations can utilize the tattoo visibility identifier during the hiring process to assess potential employees' alignment with company culture. This can help employers maintain a specific brand image, particularly in customer-facing roles where tattoos might be deemed unprofessional.
Insurance companies may leverage tattoo identification to evaluate risks associated with lifestyle choices, as certain tattoos may indicate affiliations or behaviors linked to higher risk profiles. This insight allows insurers to better tailor their policies and pricing strategies for individuals.
Brands can analyze tattoo visibility in customer demographics to tailor marketing strategies and product offerings for specific audiences. By understanding the prevalence of tattoos within a target market, companies can create campaigns that resonate more effectively with that group.
Social media platforms can employ the tattoo identifier to filter content based on user preferences or community guidelines. This ensures a safer and more comfortable environment for users who may wish to avoid content featuring visible tattoos.
Event organizers can use tattoo visibility data to create environments that are better suited to their audience's preferences. For instance, they can curate dress codes or thematic elements that either embrace or discourage visible tattoos according to the event’s tone.
Security personnel at venues or events can utilize the tattoo identifier to spot individuals with distinctive tattoos related to specific gangs, groups, or criminal activities. This can enhance safety measures and inform responses to potential security threats.
Healthcare providers can use the tattoo visibility identifier to better understand patient backgrounds and potential cultural significances of tattoos. This understanding can improve patient interactions and foster an inclusive environment, particularly in settings where tattoos may have personal or medical relevance.
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 tattoo is visible 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.