Tag images with labels
Convert images of screens to structured elements
Analyze images to generate captions, detect objects, or perform OCR
Generate saliency maps from RGB and depth images
Enhance faces in old or AI-generated photos
Mark anime facial landmarks
Run 3D human pose estimation with images
Art Institute of Chicago Gallery
Restore blurred or small images with prompt
Estimate depth from images
Restore and enhance images
Recognize text and formulas in images
Generate clickable coordinates on a screenshot
DeepDanbooru is an AI-powered tool designed to tag images with labels automatically. It leverages deep learning to analyze and categorize visual content, making it a valuable resource for organizing and searching image collections. DeepDanbooru is particularly popular for its ability to efficiently process large batches of images and apply relevant tags based on their content.
• AI-Powered Tagging: Automatically identifies and applies tags to images based on their content.
• Customizable Models: Supports the use of pre-trained models or custom-trained models for specific tagging needs.
• Integration with Danbooru: Designed to work seamlessly with Danbooru databases, allowing for easy migration and use of existing metadata.
• Efficiency: Can process multiple images quickly, making it suitable for large-scale tagging operations.
• Developer-Friendly: Includes an API for integration into custom workflows or applications.
What is the difference between DeepDanbooru and Danbooru?
DeepDanbooru is an AI-powered tagging tool built to work alongside Danbooru databases, while Danbooru itself is a booru-style imageboard and tagging platform.
Can DeepDanbooru process multiple images at once?
Yes, DeepDanbooru is designed to handle bulk image processing, making it efficient for large collections.
How accurate is DeepDanbooru's tagging?
The accuracy of DeepDanbooru depends on the quality of the AI model used. While it can achieve high accuracy, some tags may require manual review and adjustment to ensure correctness.