A pretrained ad size classifier that sorts an image into one of 10 categories — what the most effective ad size is. Use the ad size 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": "1200X1200",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 10 ad size 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 used to identify the size of advertisements within various marketing materials. By analyzing these sizes, marketers can optimize their ad layouts to enhance visibility and engagement, ensuring that ad placements are appropriate for the target audience.
Businesses can leverage the size identification function to conduct A/B testing on different ad sizes. By comparing conversion rates and audience engagement metrics across varying ad dimensions, companies can determine which sizes yield the best results.
This function can assist advertising agencies in managing their ad inventory efficiently. By categorizing the sizes of stored ads, agencies can quickly access and allocate the right ad dimensions for specific campaigns or platforms.
Businesses can utilize the ad size identifier to analyze competitors’ marketing strategies. By identifying the ad sizes that competitors frequently employ, companies can gain insights into industry standards and innovate their own advertising approaches.
Organizations can implement this function to ensure that ads meet specific size regulations set by advertising platforms or industry standards. This helps maintain compliance and avoid potential penalties due to incorrect ad sizing.
Marketers can integrate the size identification function into their reporting tools to generate detailed reports on ad performance based on size. This enables data-driven decision-making and highlights trends in user interaction related to specific ad dimensions.
By identifying and analyzing the sizes of ads that perform best with their target demographics, businesses can tailor their marketing strategies. This can lead to the creation of highly personalized advertising campaigns that resonate more effectively with potential customers.
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 ad size 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.