Model overview
> **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.
Key features
特点摘要待依据来源补齐,不凭模型名称推断能力。
Limitations & considerations
Sources are insufficient to establish specific limitations. Verify task performance, latency, data policies, licensing and version lifecycle.
Source prices & cost analysis
Prices vary by channel, region and configuration. Channel quotes are listed independently and are not treated as a single official provider price.
| Source / channel | Input | Output | Billing unit | Collected at |
|---|---|---|---|---|
| No verified prices yet. Check the source channel. | ||||
Estimate monthly base token costs
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Public benchmarks & run configuration
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。
View full indexed rankings →Frequently asked questions
Do open weights permit free commercial use?
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Sources & traceability
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。