A pretrained color palette type classifier that sorts an image into one of 10 categories — the type of color palette used. Use the color palette 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 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": "Analogous",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 10 color palette 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.
Companies can utilize a color palette type identifier to analyze their brand's visual identity against competitors. By assessing the predominant colors used in branding, they can refine their marketing strategies to ensure better brand recognition and differentiation in the market.
Fashion designers can leverage this function to identify emerging color trends by analyzing runway images and social media posts. This can help brands stay ahead in terms of style, ensuring their collections resonate with current consumer preferences.
Interior design firms can use this technology to suggest color palettes for home or office spaces based on client preferences or existing decor. It assists designers in creating harmonious color schemes that align with the client's vision and aesthetic.
Online retailers can implement the color palette identifier in their platforms to enhance product searchability. By allowing users to filter products based on preferred color palettes, retailers can improve user experience and increase sales conversions.
Social media managers can analyze the color palettes of successful posts in their industry to inform their content strategies. By understanding which colors attract more engagement, they can optimize their visuals to enhance brand visibility and audience interaction.
Art restorers can employ this function to analyze historical artworks and determine original color palettes used by artists. This information is crucial in authentic restoration efforts, ensuring that any touch-ups or reproductions reflect the artist's true intent.
Graphic design teams can utilize this tool to ensure that color palettes remain consistent across various marketing materials. By systematically identifying any deviations from established brand colors, teams can maintain a cohesive visual identity across all platforms.
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 color palette 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.