A pretrained the color of a frame classifier that sorts an image into one of 10 categories — the color of a frame it is. Use the the color of a frame 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 18 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 frame 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 art galleries to automatically categorize and manage their inventory based on the color of picture frames. By quickly identifying and classifying frames, galleries can enhance their organization and streamline their exhibition setups.
Online retailers can employ this function to sort and classify decorative frames by their colors in inventory management systems. This will improve the user experience by allowing customers to filter products based on frame color, thereby increasing sales conversions.
Interior design apps can integrate this image classification function to recommend color-coordinated frames to users based on their existing decor. By analyzing frame colors efficiently, these applications can suggest personalized design items that complement a user’s style.
Real estate services can incorporate this function to identify the colors of frames in staging photos. This information can enhance virtual staging tools by helping potential buyers visualize how different frame colors would look in their new homes, thereby influencing purchasing decisions.
Social media marketing tools can use this function to analyze posts featuring frames in user-generated content. By identifying the color of frames, brands can tailor their marketing strategies based on the colors that resonate most with their target audience.
Frame manufacturers can employ this function in their quality control processes to ensure that the colors of the frames meet specified standards. By automating the classification of frame colors, manufacturers can reduce human error and enhance product consistency.
Custom framing businesses can utilize this classification function to automate the order processing of frames based on customer specifications. By accurately identifying frame colors, companies can quickly match orders with available inventory, improving fulfillment speed and customer satisfaction.
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 frame 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.