A pretrained 3d modeling brands classifier that sorts an image into one of 10 categories — what brand of 3D modeling software it is. Use the 3d modeling brands 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 20 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": "3Ds Max",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 10 3d modeling brands 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 used by retailers to verify the authenticity of 3D models associated with products. By identifying brands, companies can prevent the sale of counterfeit goods and maintain their reputation in the market.
E-commerce platforms can utilize this classification function to efficiently manage their inventory of 3D models. By indexing models by brand, organizations can streamline the process of listing, tracking, and organizing their product offerings.
Businesses can leverage the identifier to create targeted augmented reality advertisements. By classifying 3D models into specific brands, companies can enhance user experiences through personalized interactions with products.
Designers and manufacturers can ensure that their 3D models conform to industry regulations by using the identifier to classify brands. This can mitigate legal risks and assure customers of compliance with safety and quality standards.
The function can aid in identifying potential licensing agreements and partnerships by classifying and categorizing 3D models by brand. This allows businesses to explore new revenue streams through collaborations with recognized brands.
Companies can utilize this classification to conduct competitive analysis by identifying brand-related 3D models in their industry. This insight can inform strategic decisions around product development, marketing initiatives, and market positioning.
Social media platforms can implement this function to supervise 3D models created by users. By identifying brands, platforms can filter content for compliance with brand guidelines and ensure that user-generated content supports brand integrity.
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 3d modeling brands 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.