Pretrained computer vision classifier

Identify photo shadow detail with one API call.

A pretrained photo shadow detail classifier that sorts an image into one of 10 categories — the quality of shadow details in photographs. Use the photo shadow detail API immediately, no training required, then adapt it to your own data when you need more.

Pretrained · Nyckel-trained 10 labels out of the box Image input

Try the photo shadow detail classifier

Drop in a photo and get the prediction back. No signup, no setup.

What this photo shadow detail classifier recognizes

A sample of the 16 labels this pretrained classifier chooses between.

Angular Shadow
Bright Shadow
Colored Shadow
Dark Shadow
Diffused Shadow
Even Shadow
Hard Shadow
Large Shadow
Long Shadow
Not Defined

Need a label that isn't here? Clone the classifier into your Nyckel console and edit the label set to fit your data.

Call the photo shadow detail API

Get your own copy of this classifier behind your own endpoint — callable from any HTTP client:

API quick start
curl -X POST "https://www.nyckel.com/v1/functions/YOUR_FUNCTION_ID/invoke" \
  -H "Authorization: Bearer YOUR_ACCESS_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"data": "https://example.com/photo.jpg"}'

Example response

{
  "labelName": "Angular Shadow",
  "labelId": "label_...",
  "confidence": 0.92
}

Under the hood

Model type
Nyckel-trained

Trained on a Nyckel-curated dataset covering 10 photo shadow detail categories, served on Nyckel's own infrastructure — your image stays on Nyckel.

Input
Image

Send an image URL or file to the invoke endpoint; the response is a label with a confidence score.

Make it yours
Adaptable

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.

More than a demo: this page is one of thousands of pretrained functions on Nyckel, an ML classification platform. You can invoke classifiers by API, review predictions, correct labels, collect samples from production traffic, and promote any pretrained function to a private custom model — without changing your integration.

Where teams use photo shadow detail classification

Interior Design Assessment

The photo shadow detail identifier can help interior designers evaluate the effectiveness of lighting in photos of staged homes. By analyzing shadows, designers can gain insights into how light interacts with various elements, allowing them to make informed adjustments to improve the overall aesthetic.

E-commerce Product Photography

E-commerce platforms can leverage the shadow detail identifier to process product images more effectively. By ensuring consistent and appealing shadow patterns, platforms can enhance product visibility and attractiveness, leading to better customer engagement and increased sales.

Art Value Appraisal

Art appraisers can utilize this function to assess the quality of photographs representing artwork. By examining the shadow details, they can determine if the images accurately represent the colors and textures of the original pieces, ensuring more accurate appraisals.

Marketing Campaign Analysis

Marketing teams can employ the shadow detail identifier to evaluate images used in campaigns. By analyzing the play of shadows, they can assess visual appeal and adjust photographic strategies to enhance brand messaging and audience impact.

Real Estate Marketing

Real estate agents can use this function to optimize property listings by identifying the quality of shadow details in property photos. Enhancing shadow clarity can attract more potential buyers, as it showcases the property's features more vividly.

Fashion Photography Enhancement

Fashion brands can use the shadow detail identifier to ensure that clothing is represented authentically in promotional images. Properly captured shadows can highlight fabric texture and movement, allowing customers to have a clearer idea of product quality.

Social Media Content Optimization

Content creators can utilize this identifier to improve the visual quality of their social media posts. By analyzing the shadow details in their images, they can enhance composition, leading to higher engagement rates and more visually appealing profiles.

Common questions

What's the difference between a zero-shot and a Nyckel-trained classifier?

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.

How do I know whether this will work for my application?

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.

What happens when it makes a mistake?

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.

Do I need training data to get started?

No. This photo shadow detail 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.

What does it cost to try?

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.

Ready to classify photo shadow detail at scale?

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.