Identify photo highlight retention using AI

Below is a free classifier to identify photo highlight retention. Just upload your image, and our AI will predict what highlights are most relevant in the photo - in just seconds.

photo highlight retention identifier

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Get started

    import nyckel
    
    credentials = nyckel.Credentials("YOUR_CLIENT_ID", "YOUR_CLIENT_SECRET")
    nyckel.invoke("photo-highlight-retention", "your_image_url", credentials)
                

    fetch('https://www.nyckel.com/v1/functions/photo-highlight-retention/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/photo-highlight-retention/invoke
                

How this classifier works

To start, upload your image. Our AI tool will then predict what highlights are most relevant in the photo.

This pretrained image model uses a Nyckel-created dataset and has 21 labels, including Balanced, Blown Out, Clean, Color Accurate, Color Shifted, Contrasty, Detailed, Dynamic Range, Flat and High Contrast.

We'll also show a confidence score (the higher the number, the more confident the AI model is around what highlights are most relevant in the photo).

Whether you're just curious or building photo highlight retention detection into your application, we hope our classifier proves helpful.

Need to identify photo highlight retention at scale?

Get API or Zapier access to this classifier for free. It's perfect for:



  • Social Media Optimization: The 'photo highlight retention' identifier can be used by social media platforms to enhance user engagement by identifying and promoting images that are likely to retain attention. By analyzing photos for their potential to attract likes and shares, platforms can suggest the best content to users, thereby increasing overall interaction rates.

  • Marketing Campaign Enhancement: Companies can leverage this function to tailor their marketing strategies by identifying visuals that resonate most with their target audience. By retaining highlights from images that yield higher conversion rates, businesses can refine ad design and content for maximum impact.

  • E-commerce Visual Analysis: Online retailers can utilize the identifier to assess product images that capture customer interest more effectively. By focusing on image highlights that improve click-through rates, retailers can optimize their product presentations, leading to higher sales and customer satisfaction.

  • User-Generated Content Selection: Brands can employ this function to sift through user-generated content for marketing purposes. By identifying photos that retain the most appeal, they can curate effective promotional material that reflects genuine consumer experiences and enhances brand credibility.

  • Exhibition Curation: Museums and galleries can use the photo highlight retention identifier to select the most impactful images for exhibitions. By understanding which artworks or artifacts captivate viewers' attention, curators can design layouts that enhance visitor engagement and overall experience.

  • Educational Material Development: Educators can apply this function to develop more compelling teaching materials by identifying images that retain students’ attention. This analysis can lead to improved lesson plans and presentations that foster better learning outcomes through visually engaging content.

  • Image-Based AI Training: The identifier can be used in training artificial intelligence models that focus on image understanding. By selecting images that demonstrate strong highlight retention, developers can improve the dataset quality, leading to more accurate AI interpretations and classifications in tasks such as object detection and scene recognition.

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