NSFW Image Detection Large
| Property | Value |
|---|---|
| Parameter Count | 87.1M |
| Model Type | FocalNetForImageClassification |
| License | CC-BY-NC-SA-4.0 |
| Tensor Type | F32 |
| Base Model | microsoft/focalnet-base |
What is nsfw-image-detection-large?
The nsfw-image-detection-large is an advanced AI model designed for content moderation, specifically focusing on detecting and classifying potentially inappropriate images. Built on Microsoft's FocalNet architecture, it provides rapid classification into three distinct categories: Safe, Questionable, and Unsafe, with impressive accuracy exceeding 95% on benchmark tasks.
Implementation Details
This model processes images at 512x512 pixels resolution using PyTorch framework. It employs sophisticated image transformations including normalization and tensor conversion, making it particularly effective for large-scale content moderation tasks. The model achieves sub-100ms latency per image on standard GPU hardware.
- Input processing using custom transformations and normalization
- Three-class classification system with confidence scoring
- Optimized for production environments with batch processing capabilities
- Built-in support for high-throughput image analysis
Core Capabilities
- High-speed image classification with 95%+ accuracy
- Real-time content moderation for social media platforms
- Automated filtering for e-commerce product images
- Content verification for educational platforms
- Dating app content moderation
Frequently Asked Questions
Q: What makes this model unique?
This model combines high accuracy with practical implementation features, offering real-world applicability through its three-tier classification system and optimized processing pipeline. Its base on FocalNet architecture provides superior feature extraction capabilities compared to traditional vision models.
Q: What are the recommended use cases?
The model is ideal for content moderation in social media platforms, e-commerce websites, dating applications, and educational platforms where maintaining appropriate content standards is crucial. However, it should be used as part of a broader content moderation strategy rather than as a standalone solution.
