A pretrained if image is cropped classifier that sorts an image into one of 2 categories. Use the if image is cropped 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": "Cropped",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 2 if image is cropped 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 use case involves leveraging the image cropping identifier in photography applications to automatically determine whether an uploaded photo has been cropped. This feature can help photographers manage their content, ensuring that only original images or unedited versions are showcased, thereby maintaining the integrity of their portfolio.
E-commerce platforms can use the image cropping identifier to validate product images before they are published. By identifying cropped images, the platform can flag potentially misleading visuals that may not accurately represent the product, improving customer trust and satisfaction.
Social media apps can implement the identifier to detect cropped images in user uploads. By processing images to find those that have been cropped, moderation teams can ensure that content adheres to community standards, preventing the spread of altered images that may mislead or offend users.
Media management systems can employ this identifier to track crop history for images. By logging instances of cropping, organizations can maintain a record of content alterations, enabling them to revert to original images when necessary and track changes over time.
Art analysis software can utilize the cropping identifier to evaluate whether images of artwork have been cropped inappropriately. This can assist curators and art historians in ensuring that critical elements of the artwork are analyzed while preventing misinterpretation due to cropping.
In automated graphic design software, the crop detection feature can be employed to suggest cropping adjustments. By identifying cropped images, the tool can provide recommendations for optimizing visual elements, helping users create cohesive and visually appealing content.
In digital forensics, the ability to identify cropped images is crucial for maintaining evidence integrity. Investigators can use this function to analyze image authenticity, determining whether an image has been altered, enhancing the credibility of forensic investigations in legal contexts.
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 if image is cropped 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.