A pretrained the color of a blanket classifier that sorts an image into one of 10 categories — what color the blanket is. Use the the color of a blanket 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 blanket 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.
Utilize the color identification function to recommend home decor schemes based on customers' preferences. By analyzing images of existing home furnishings, the application can suggest complementary blanket colors that enhance overall aesthetics and create a cohesive look.
E-commerce platforms can integrate this function to assist customers in selecting blankets that match their previously purchased items or other products on the site. This can lead to increased sales by simplifying the decision-making process for consumers looking for color-coordinated home goods.
Interior designers can use the color classification tool to advise clients on blanket selections that harmonize with current color palettes in their spaces. This capability enhances client satisfaction by offering tailored advice based on visual data.
Retailers can analyze the colors of blankets they stock against current market trends and customer preferences identified by the tool. This can guide inventory decisions, ensuring the store is stocked with the most appealing color options to meet demand.
Marketers can harness the blanket color identification function to create personalized marketing campaigns targeting specific customer preferences. By analyzing past purchases, they can promote blankets in colors that match or contrast favorably with items already owned by the customer.
Event planners can use the function to suggest blanket colors that fit the theme or color scheme of an event. The ability to visually match materials can improve the overall ambiance and aesthetic value of events such as weddings or corporate functions.
Fashion designers can leverage this tool to analyze trends in blanket colors and incorporate similar palettes into their clothing or accessory lines. This can create synergy between home textiles and fashion, enhancing brand coherence across product offerings.
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 blanket 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.