A pretrained the color of a shirt classifier that sorts an image into one of 10 categories — what color the shirt is. Use the the color of a shirt 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 16 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 shirt 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.
The false image classification function can automatically identify and categorize shirts based on color for online retail platforms. This allows retailers to streamline their inventory management, making it easier for customers to find products by filtering search results by color.
Fashion brands can utilize the function to analyze social media and online images to identify prevalent shirt colors among influencers and consumers. This data can be leveraged to inform design choices and marketing strategies.
Retailers can implement this function on their websites or apps to enhance personalization. By identifying the color of a shirt preferred by a user, they can recommend similar products and improve customer engagement.
Retail stores can use the classification function to optimize their in-store visual merchandising strategies. By understanding which shirt colors are trending, they can arrange displays that are more visually appealing and likely to attract buyers.
The function can assist in predicting future inventory needs based on color trends identified in sales data. By analyzing which colors sell best, businesses can adjust their stock levels appropriately to meet customer demand.
Brands can use the color identification capabilities to create targeted marketing campaigns. By understanding which colors resonate with different demographics, they can tailor their promotions and advertisements to maximize engagement.
Manufacturers can implement the classification function to ensure that shirts being produced match the specified color standards. This helps maintain brand consistency and quality assurance, reducing returns and enhancing 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 shirt 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.