Identify computer generated pattern
using AI
Below is a free classifier to identify computer generated pattern. Just upload your image, and our AI will predict what type of computer-generated pattern it is - in just seconds.
Contact us for API access
Or, use Nyckel to build highly-accurate custom classifiers in just minutes. No PhD required.
Get started
import nyckel
credentials = nyckel.Credentials("YOUR_CLIENT_ID", "YOUR_CLIENT_SECRET")
nyckel.invoke("computer-generated-pattern-identifier", "your_image_url", credentials)
fetch('https://www.nyckel.com/v1/functions/computer-generated-pattern-identifier/invoke', {
method: 'POST',
headers: {
'Authorization': 'Bearer ' + 'YOUR_BEARER_TOKEN',
'Content-Type': 'application/json',
},
body: JSON.stringify(
{"data": "your_image_url"}
)
})
.then(response => response.json())
.then(data => console.log(data));
curl -X POST \
-H "Content-Type: application/json" \
-H "Authorization: Bearer YOUR_BEARER_TOKEN" \
-d '{"data": "your_image_url"}' \
https://www.nyckel.com/v1/functions/computer-generated-pattern-identifier/invoke
How this classifier works
To start, upload your image. Our AI tool will then predict what type of computer-generated pattern it is.
This pretrained image model uses a Nyckel-created dataset and has 20 labels, including Abstract Form, Algorithmic, Cellular, Chaotic Pattern, Digital Mosaic, Fractal, Generative Art, Geometric Design, Lattice Structure and Mandelbrot Set.
We'll also show a confidence score (the higher the number, the more confident the AI model is around what type of computer-generated pattern it is).
Whether you're just curious or building computer generated pattern detection into your application, we hope our classifier proves helpful.
Related Classifiers
Need to identify computer generated pattern at scale?
Get API or Zapier access to this classifier for free. It's perfect for:
- Quality Control in Manufacturing: This function can be utilized in manufacturing environments to distinguish between real product images and computer-generated representations. By ensuring only genuine product images are recognized, companies can maintain quality standards and reduce the risk of defective goods reaching consumers.
- Content Verification for Digital Media: Media companies can employ this technology to verify the authenticity of images used in articles, social media posts, and advertisements. By identifying computer-generated patterns, they can safeguard their credibility and prevent the spread of misinformation.
- Fraud Detection in E-commerce: E-commerce platforms can integrate this function to detect fraudulent listings featuring computer-generated images that do not represent actual products. By preventing such listings, the platform can enhance user trust and improve the overall shopping experience.
- Art Authentication: Galleries and auction houses can use the identifier to authenticate artworks by distinguishing between digitally created pieces and traditional ones. This can help maintain the integrity of art markets by identifying and alerting stakeholders about potential forgeries.
- Augmented Reality Development: Developers of AR applications can implement this function to differentiate between real-world images and computer-generated overlays. Ensuring the accuracy of image classification enhances the effectiveness of AR experiences, benefiting user interactions.
- Social Media Filters Monitoring: Social media platforms can leverage this identifier to monitor and filter out misleading content that may employ overly stylized or computer-generated patterns. This can improve user experience and combat the spread of heavily edited or artificial imagery.
- Academic Research in Image Analysis: Researchers focusing on image analysis and computer vision can utilize this function to study trends in image generation algorithms. By identifying computer-generated patterns, they can contribute to advancements in artificial intelligence and refine classification techniques in their studies.