A pretrained line graphs vs scatter plots classifier that sorts an image into one of 2 categories. Use the line graphs vs scatter plots 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 2 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": "Line Graphs",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 2 line graphs vs scatter plots 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 integrated into data visualization software to automatically suggest the most appropriate graph type (line graph or scatter plot) based on the nature of the data provided by the user. By identifying existing graphs and improving their representation, users can enhance the clarity and effectiveness of their presentations.
In business intelligence applications, the binary classifier can analyze datasets and decide whether to generate line graphs or scatter plots for reports. This automation reduces the manual effort involved in determining the best visual representation, allowing analysts to focus more on insights and conclusions.
Educational platforms can utilize this function to teach students about different data visualization techniques. By automatically categorizing provided examples as either line graphs or scatter plots, learners can receive instant feedback and improve their understanding of when to use each type effectively.
Marketing teams can implement this classifier in their analytical dashboards to dynamically adjust visualizations based on the metrics being analyzed. This ensures that stakeholders receive the clearest and most relevant representations of trends, correlations, and potential outliers in marketing data.
E-commerce platforms can leverage this function to assess user interaction data and choose between line graphs and scatter plots for visualizations. By doing so, they can better illustrate trends in user behavior over time or the relationship between different metrics, thereby aiding in strategic decision-making.
Researchers can apply this binary classification function to improve the presentation of their findings by selecting the most suitable graph type for their datasets. This value-added service helps in enhancing the communication of research outcomes, making them more accessible to a broader audience.
Financial software can integrate this function to better visualize time-series data (line graphs) versus transactional data (scatter plots). This helps analysts quickly determine the most effective way to represent financial trends and anomalies, facilitating clearer communication of important financial metrics.
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 line graphs vs scatter plots 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.