A pretrained outfits classifier that sorts an image into one of 2 categories — what type of outfit it is. Use the outfits 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": "casual",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 2 outfits 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.
An AI-driven fashion platform can incorporate the outfit types identifier to recommend personalized outfits to users based on their preferences. By accurately identifying various outfit styles, the system ensures users receive tailored suggestions that match their individual tastes and occasions.
Online retailers can utilize the outfit type identifier to automatically categorize and tag their clothing inventory. This will enhance product discoverability, improve user experience through refined search filters, and ultimately boost sales by presenting customers with relevant options.
Fashion influencers and brands can leverage this function to analyze trending outfit types on social media platforms. By identifying popular styles, they can make informed marketing and inventory decisions, capitalizing on what resonates with audiences.
Businesses can utilize the outfit types identifier to segment their audience based on style preferences. This allows for the development of targeted email campaigns and advertisements that directly align with customers' tastes, increasing engagement and conversion rates.
Fashion brands can implement this identifier in their apps to help customers track their personal style evolution over time. By analyzing outfit choices and preferences, brands can provide insights and suggest new styles that align with the user’s progression and changing tastes.
Event organizers can use the outfit types identifier to suggest suitable attire for upcoming events when users register. This feature can enhance user experience by providing attendees with guidelines on appropriate dress codes, boosting satisfaction and attendance.
This function can be integrated into wardrobe management apps to help users catalog their clothing items by outfit type. By providing suggestions for outfits for various occasions, users can optimize their wardrobe usage, reduce decision fatigue, and promote sustainable consumption habits.
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 outfits 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.