A pretrained chart types classifier that sorts an image into one of 2 categories — what type of chart it is. Use the chart types 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 20 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": "Bar Chart",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 2 chart types 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.
Integrating the multilabel image classification function in data visualization tools can help users automatically identify and label different types of charts in documents or presentations. This enhances user experience by improving accessibility and searchability for specific chart types, enabling users to quickly evaluate data representation.
Educational platforms can utilize this function to evaluate students' assignments or projects that include charts. By identifying the various chart types, the system can provide targeted feedback, ensuring students understand when to use specific visualization methods effectively.
Market research firms can leverage this function to analyze visual data in reports. By automatically classifying charts, firms can more easily summarize findings and generate insights on market trends or competitor analysis, leading to more informed decision-making.
Businesses can use this function within automated reporting systems to categorize and label visual data in generated reports. This streamlines the report creation process, ensuring clarity and consistency in how different data visuals are represented, ultimately enhancing stakeholder engagement.
Design teams can implement this function to perform quality checks on data visuals created for marketing or presentations. By ensuring each chart is correctly identified, teams can avoid misrepresentations of data, maintaining a professional standard in visual communications.
Social media platforms and analytics tools can incorporate the classification function to categorize user-generated content featuring charts or infographics. This can help in evaluating engagement patterns and preferences, allowing marketers to tailor content strategies based on chart types and their effectiveness.
Organizations can leverage the multilabel classification function to analyze user interactions with different chart types in their applications or websites. Understanding which types of charts attract more attention can guide design enhancements and improve user engagement with data presentations.
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 chart types 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.