A pretrained the color of a sweater classifier that sorts an image into one of 10 categories — what color the sweater is. Use the the color of a sweater 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 21 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": "Beige",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 10 the color of a sweater 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 e-commerce platforms to automatically categorize sweater listings based on their color. By efficiently tagging products, retailers can enhance user experience, enabling customers to filter their searches by specific colors, thus increasing sales and customer satisfaction.
Retailers can utilize the color identifier to maintain and oversee their inventory more effectively. By analyzing the color distribution of sweaters in stock, businesses can make informed decisions on which colors are popular, leading to better restocking strategies and minimized overstock situations.
Companies can leverage this function to create targeted marketing campaigns based on customers’ color preferences. By identifying the prevalent colors in their product range and matching them with customer data, organizations can personalize promotions to boost conversion rates and improve customer engagement.
Fashion retailers can employ the color identification function to analyze market trends based on the colors of sweaters sold. This data can inform design teams about which colors are gaining popularity, aiding in future product development and ensuring alignment with consumer preferences.
Clothing manufacturers can implement this function during the production line to ensure consistent color quality in sweaters. By flagging any deviations from specified colors, it can reduce defects and improve overall product quality before items reach the market.
Retailers can enhance visual search capabilities on their platforms by integrating the color identifier. Customers can upload images of sweaters they like, and the system can automatically categorize and suggest similar products based on color, improving the shopping experience.
Brands committed to sustainability can use the color identifier to analyze the distribution of dyes used in their sweaters. This data can be leveraged in sustainability reports, showcasing efforts towards eco-friendly practices and helping to attract environmentally conscious consumers.
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 the color of a sweater 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.