Identify filter intensity using AI

Below is a free classifier to identify filter intensity. Just upload your image, and our AI will predict the intensity of each filter applied to the image. - in just seconds.

filter intensity identifier

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Get started

    import nyckel
    
    credentials = nyckel.Credentials("YOUR_CLIENT_ID", "YOUR_CLIENT_SECRET")
    nyckel.invoke("filter-intensity", "your_image_url", credentials)
                

    fetch('https://www.nyckel.com/v1/functions/filter-intensity/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/filter-intensity/invoke
                

How this classifier works

To start, upload your image. Our AI tool will then predict the intensity of each filter applied to the image..

This pretrained image model uses a Nyckel-created dataset and has 16 labels, including Bold, Bright, Desaturated, Dim, Extreme, Harsh, Heavy, Intense, Light and Moderate.

We'll also show a confidence score (the higher the number, the more confident the AI model is around the intensity of each filter applied to the image.).

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

Need to identify filter intensity at scale?

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



  • Content Moderation: This function can be employed by social media platforms to identify and filter out images that contain misleading or false visual content, helping to maintain the integrity of user-generated content. By flagging images with incorrect intensity levels, these platforms can reduce the spread of misinformation.

  • E-commerce Verification: Online retail platforms can use this identifier to differentiate between accurately represented product images and those that are manipulated or enhanced. By ensuring that only images with authentic filter intensities are displayed, customers can make more informed purchasing decisions.

  • Advertising Compliance: Advertising agencies can implement this function to ensure that promotional images comply with regulatory standards regarding authenticity. By identifying false or misleading filter intensities, they can prevent the distribution of deceptive advertisements that could lead to legal repercussions.

  • News Media Integrity: News organizations can utilize the filter intensity identifier to verify the authenticity of images shared within articles. By confirming that the images align with their intended intensity metrics, these organizations can uphold journalistic integrity and trustworthiness.

  • Art Authentication: Galleries and auction houses can employ this function to assist in the authentication of art pieces. By analyzing the filter intensity used in photographic reproductions, they can help determine whether an image accurately represents the original artwork or has been digitally altered.

  • Digital Forensics: Law enforcement agencies can utilize this identifier during investigations to assess the credibility of digital evidence presented in cases. By analyzing the filter intensities of images, they can establish whether alterations have been made, aiding in the resolution of criminal investigations.

  • AI Training Data Quality: Companies developing AI models can implement this function to enhance the quality of their training data. By filtering images based on their intensity characteristics, they can ensure that their datasets consist of authentic, unaltered images, thus improving model accuracy and performance.

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