A pretrained closet space type classifier that sorts an image into one of 10 categories — what type of closet space you have. Use the closet space type 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 16 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": "Built-In",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 10 closet space type 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 application identifies the types of closet spaces based on the organization and style of items stored. Homeowners can receive tailored recommendations for storage solutions, maximizing the utility of their closet space while enhancing aesthetic appeal.
Retailers can utilize the false image classification function to categorize and manage their inventory based on closet space types. This allows for more accurate forecasting and inventory control, improving stock placement and sales strategy development.
Smart home systems can integrate this functionality to automatically adjust lighting or temperature based on the identified closet space type. This creates a personalized environment suitable for various types of clothing and accessories, improving user experience.
Online clothing retailers can employ this classifier to optimize product presentation by grouping items based on closet space type. This enhances the shopping experience, encouraging customers to buy complementary items suited for their closet organization.
Real estate agents can use this functionality to assess closet space types in homes. By offering improvement suggestions based on the classifications, agents can better stage homes to attract buyers, highlighting optimal usage of closet space.
Interior designers can leverage this classifier in augmented reality applications to visualize how different closet types would look in a given space. This helps clients make informed decisions about layout and organization, increasing client satisfaction.
Personal organization applications can incorporate this function to help users categorize their closet spaces effectively. The app can provide users with style recommendations and organizational tips based on their specific closet type, aiding in decluttering and enhancing overall usability.
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 closet space type 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.