A pretrained image aspect ratio classifier that sorts an image into one of 10 categories — the aspect ratio of the image.. Use the image aspect ratio 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": "Cinematic",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 10 image aspect ratio 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.
Businesses can utilize the image aspect ratio identifier to ensure that images uploaded to their social media platforms maintain the optimal dimensions for visibility and engagement. By automatically flagging images that do not conform to best practices, brands can enhance user experience and improve the effectiveness of their campaigns.
Online retailers can leverage this function to validate product image aspect ratios, ensuring that all product images are uniformly sized for a cohesive look. This practice can boost customer trust and increase sales by promoting a professional appearance across product listings.
Advertising agencies can use the aspect ratio identifier to ensure that creative assets meet the specifications for various ad platforms, which often have strict requirements for image dimensions. This reduces the likelihood of campaign delays or rejections due to incorrect image formats.
Web developers can implement the image aspect ratio identifier to automatically check that images uploaded to a website are proportionate and fit the designated layout. This ensures aesthetic harmony across all visual elements, critical for maintaining brand identity and user experience.
Organizations with extensive image libraries can use this technology to assess and categorize their image collections based on aspect ratios. This aids in efficient archival processes and facilitates quick retrieval of images that meet specific dimensions for future projects.
Developers of graphic design and content creation software can integrate the aspect ratio identifier to help users select the right dimensions for their projects. This assists users in creating visually appealing content that adheres to industry standards without the need for manual adjustments.
Companies offering AI-based image enhancement services can incorporate the aspect ratio identifier to automatically adjust or crop images to fit desired formats without distorting the content. This enhances the quality of images used in various applications, from digital marketing to media production.
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 image aspect ratio 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.