A pretrained the color of a shower curtain classifier that sorts an image into one of 10 categories — the color of a shower curtain. Use the the color of a shower curtain 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 24 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 shower curtain 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 selecting and recommending shower curtains that match a client's desired color scheme. By quickly identifying the color of existing items in a bathroom, designers can provide tailored suggestions that enhance the overall aesthetic.
Online retailers can integrate this function to improve customer experience by suggesting shower curtains that complement or match other bathroom items in a user's cart. By analyzing product images, the system can recommend items based on color harmony, increasing the likelihood of additional purchases.
Retailers can utilize this function to efficiently categorize and manage their shower curtain inventory based on color. By automating the classification process, they can streamline restocking and ensure that popular colors remain available to meet consumer demand.
Market researchers can use the shower curtain color classifier to analyze consumer preferences and trends over time. By aggregating data on popular colors, businesses can make informed decisions on product development and marketing strategies.
Home improvement applications can integrate this function to allow users to visualize how different colored shower curtains would look in their bathroom. This interactive feature can enhance user engagement and assist in the decision-making process for home decor.
Insurance companies can utilize the image classifier in claims assessment for water damage or property loss claims related to bathrooms. By quickly identifying the color of the shower curtain, adjusters can verify and document damages more efficiently.
Influencers and marketers can leverage this function to create visually appealing content that highlights shower curtain trends and color pairings. By analyzing and categorizing images based on color, campaigns can focus on trending aesthetics and improve engagement with targeted audiences.
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 shower curtain 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.