A pretrained door style classifier that sorts an image into one of 10 categories — what style of door it is. Use the door style 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 25 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": "Arched Door",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 10 door style 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 assist interior designers in quickly identifying and selecting appropriate door styles for their projects. By classifying door styles in images, designers can streamline their design proposals and ensure that chosen styles harmonize with other elements in the space.
Real estate agents can enhance property listings by including classified door styles in their marketing materials. Featuring accurate descriptions of door styles can attract potential buyers who have specific aesthetic preferences and elevate the perceived value of the property.
Online furniture and home improvement retailers can utilize this function to categorize door styles in their product images. By accurately classifying doors, retailers can improve search functionalities, making it easier for customers to find products that match their desired styles and home aesthetics.
Contractors can leverage this classification tool to assess clients' existing door styles during renovation consultations. Understanding current door styles helps in making informed recommendations for replacements or upgrades that maintain or enhance the property's overall aesthetic.
Custom door manufacturers can use this function to analyze customer-uploaded images of existing doors. By classifying these styles, manufacturers can better understand market trends and preferences, allowing them to tailor their product offerings to meet consumer demands.
Developers of augmented reality (AR) home design applications can implement the door style identifier to provide accurate simulations of various door styles in virtual environments. Users can visualize how different doors would look in their homes, ultimately aiding in the decision-making process.
Home staging companies can use this classification function to quickly assess and recommend appropriate door styles when setting up homes for showings. Accurate identification of door styles helps stagers create cohesive and appealing presentations that attract potential buyers.
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 door style 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.