MODEL PROFILE · 模型档案

siglip2-x256-explicit-content

prithivMLmods · 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 - **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.

译文排队中,当前展示来源原文。

图像分类license:apache-2.0
上下文窗口来源未披露
输入 → 输出图像 → 待核验
模型权重待核验
档案更新时间2026/11/2 19:16:01
OVERVIEW

模型介绍

> **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 - **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.

主要特点

特点摘要待依据来源补齐,不凭模型名称推断能力。

局限与注意事项

暂无足够来源支持具体缺点结论。请重点验证实际任务表现、时延、数据政策、许可与版本生命周期。

PRICING & COST

来源报价与费用分析

同一模型在不同渠道、地区和配置下可能采用不同价格。下表独立保留各渠道报价,不将渠道价格视为厂商官方统一价格。

来源 / 渠道输入输出计费单位采集时间
暂未取得可核验报价,请查阅来源渠道。

按月估算基础 Token 费用

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预估不包含缓存优惠、阶梯定价、多模态附加费用、网络检索、税费及推理额外输出。

INDEPENDENT BENCHMARKS

公开评测与运行配置

暂无已采集的可核验公开评分。模型未上榜不代表能力较低。

查看完整已收录榜单 →

常见问题

开放权重是否代表可以免费商用?

不一定。应核对具体版本许可证、商用门槛和使用政策。自部署还会产生算力和维护成本。

这些价格是实时结算价格吗?

不是实时结算价格,而是带采集时间的来源报价快照。请到对应渠道核对当前版本、区域、折扣和计费方式。

资料来源与可追溯记录

许可证:apache-2.0。模型介绍与第三方评测均以原来源的具体版本和方法为准。