A pretrained the color of a jacket classifier that sorts an image into one of 10 categories — what color the jacket is. Use the the color of a jacket 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 32 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 jacket 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 help retailers automatically classify and sort jackets by color, facilitating better inventory tracking and management. By integrating the classification system into their inventory software, retailers can ensure that they maintain an optimal stock of each jacket color.
Online retail platforms can leverage this technology to categorize jackets based on color. This would enhance searchability for customers looking for specific colors, ultimately improving user experience and increasing sales conversions.
Businesses can utilize color classification to analyze customer preferences and trends among different jacket colors. This insight can inform targeted marketing campaigns, improving engagement rates by showcasing preferred styles and colors to specific consumer segments.
Fashion apps can incorporate this function to provide personalized jacket recommendations based on users' past purchases and color preferences. By showing users jackets in their preferred colors, the app can enhance user satisfaction and drive repeat purchases.
Fashion designers and brands can use the color classification function to analyze market trends in jacket colors over time. This data can inform design decisions and help brands anticipate shifts in consumer preferences, leading to more successful product launches.
Manufacturers can implement this function to automate the quality control process by ensuring that jackets are produced in the correct colors as specified. This would reduce errors, improve consistency across batches, and maintain brand standards.
Brick-and-mortar stores can apply this function to analyze which jacket colors are displayed in-store and how they affect customer engagement. Insights gained can help store managers create visually appealing displays that attract customers and enhance overall shopping experience.
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 jacket 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.