siglip2 x256 explicit content siglip2 x256 explicit content is a vision language encoder model fine tuned from siglip2 base patch16 256 for multi class image classification . Built on the SiglipForImageClassification architecture, the model is trained to identify and categorize content types in images, especially for explicit, suggestive, or safe media filtering . [!note] SigLIP 2: Multilingual Vision Language Encoders with Improved Semantic Understanding, Localization, and Dense Features https://arxiv.org/pdf/2502.14786 Label Space: 5 Classes The model classifies each image into one of the following content categories: Install Dependencies Inference Code Intended Use This model is intended for applications such as: Content Moderation : Automatically detect NSFW or suggestive content. Parental Controls : Enable AI based filtering for safe media browsing. Dataset Preprocessing : Clean and categorize image datasets for research or deployment. Online Platforms : Help enforce content guidelines for uploads and user generated media.
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