A pretrained color grading quality classifier that sorts an image into one of 10 categories — the quality of color grading in images.. Use the color grading quality 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 14 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": "Advanced",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 10 color grading quality 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.
In film production, maintaining consistent color grading across shots is crucial for storytelling. This function can automatically identify discrepancies in color grading, allowing cinematographers to make necessary adjustments and ensure a cohesive visual narrative.
Marketing agencies can utilize the color grading quality identifier to evaluate the effectiveness of color schemes in advertisements. By analyzing color grading across various ads, teams can determine which qualities resonate best with target audiences, optimizing future campaigns.
E-commerce platforms can implement this function to assess the color grading of product images. By ensuring product images have optimal color grading, companies can enhance visual appeal, leading to improved customer engagement and increased sales.
Content creators on platforms like Instagram or TikTok can use the color grading quality identifier to evaluate their visuals before posting. By ensuring high-quality color grading, influencers can enhance viewer retention and engagement metrics.
Game developers can apply this function to ensure visual consistency in the color grading of in-game cinematics. By identifying and correcting poor color grading early in the development process, they can enhance the overall gaming experience and immersion.
Educators producing video content for online courses can utilize this identifier to ensure their instructional videos have professional-level color grading. This attention to quality can increase the perceived value of the material and improve student engagement.
Companies managing large libraries of digital media can employ this function to streamline quality checks on their archives. This ensures that all visual assets maintain a consistent and high-quality color grading standard, facilitating easier retrieval and use in various projects.
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 grading quality 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.