A pretrained color vs black and white illustrations classifier that sorts an image into one of 2 categories. Use the color vs black and white illustrations 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 2 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": "Color Illustrations",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 2 color vs black and white illustrations 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 utilized by online art marketplaces to automatically categorize illustrations as either color or black and white. This aids users in filtering artwork based on their preferences, enhancing the overall shopping experience.
Publishers can integrate this classification functionality in their content management systems to streamline the organization of visual assets. By identifying images, editors can efficiently manage the display of illustrations according to the desired style, improving content consistency.
Marketing teams can leverage this functionality to analyze engagement trends between color and black and white illustrations used in social media campaigns. This data can inform future design choices, tailoring content to resonate more effectively with target audiences.
Educational institutions can use the identifier to classify and curate images for e-learning materials. This ensures that resource materials match specific pedagogical approaches, such as emphasizing mood or tone through color versus black and white imagery.
Graphic design software can incorporate this binary classification to suggest color or black and white illustrations based on user project requirements. This feature can expedite the design process, helping users maintain style coherence while also saving time.
Archival organizations can utilize this classification tool to digitize and categorize historical documents and illustrations. It aids researchers and historians in extracting and analyzing visual materials from certain periods, facilitating better preservation and study of art and culture.
E-commerce platforms can implement this function to tailor product recommendations or advertisements based on user preferences for color or black and white visuals. By enhancing personalization efforts, platforms can potentially increase conversion rates through targeted outreach.
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 vs black and white illustrations 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.