NSFW Image Detection Model
| Property | Value |
|---|---|
| Parameter Count | 85.8M |
| License | Apache 2.0 |
| Architecture | Vision Transformer (ViT) |
| Paper | ViT Paper |
| Accuracy | 98.04% |
What is nsfw_image_detection?
The nsfw_image_detection model is a specialized Vision Transformer (ViT) fine-tuned for detecting Not Safe For Work (NSFW) content in images. Built on the google/vit-base-patch16-224-in21k architecture, this model processes 224x224 pixel images to classify them as either "normal" or "nsfw" with high accuracy.
Implementation Details
The model utilizes a transformer-based architecture fine-tuned with carefully selected hyperparameters, including a batch size of 16 and a learning rate of 5e-5. Training was conducted on a proprietary dataset of 80,000 images, ensuring robust performance in real-world applications.
- Pre-trained on ImageNet-21k dataset
- Fine-tuned using PyTorch framework
- Implements patch-based image processing (16x16 patches)
- Utilizes F32 tensor type for computations
Core Capabilities
- Binary classification of images (normal/nsfw)
- High accuracy rate of 98.04% on evaluation set
- Processing speed of 52.46 samples per second
- Efficient inference with modern transformer architecture
Frequently Asked Questions
Q: What makes this model unique?
This model combines the powerful ViT architecture with specialized fine-tuning for NSFW detection, achieving exceptional accuracy while maintaining processing efficiency. The large training dataset and careful hyperparameter optimization make it particularly robust for content moderation tasks.
Q: What are the recommended use cases?
The model is specifically designed for content moderation systems, social media platforms, and any application requiring automatic filtering of inappropriate content. It's particularly suitable for high-throughput systems requiring reliable NSFW detection.
