nsfw_image_detection

Classifies images into NSFW (not safe for work) or SFW (safe for work) categories using a Vision Transformer (ViT) backbone fine-tuned on image classification tasks. The model processes images through a transformer-based architecture that learns spatial and semantic features across the entire image, then outputs binary classification logits. Inference can be performed locally via PyTorch or remotely via HuggingFace Inference API endpoints, supporting batch processing of multiple images.

Supports inference through HuggingFace Inference API endpoints compatible with Azure deployment and multi-region hosting, enabling serverless image classification without local GPU infrastructure. The model can be queried via REST API with automatic batching, request queuing, and horizontal scaling across distributed endpoints. Supports both synchronous single-image requests and asynchronous batch processing for high-throughput scenarios.

Exposes intermediate ViT embeddings and attention maps from the transformer backbone, enabling feature-level analysis beyond binary classification. The model's internal representations can be extracted at various layers (patch embeddings, transformer blocks, class token) for downstream tasks like similarity search, clustering, or custom fine-tuning. Attention weights reveal which image regions the model focuses on for NSFW decisions, supporting interpretability and debugging.

Loads model weights using the SafeTensors format instead of traditional PyTorch pickle files, providing faster deserialization, reduced memory footprint during loading, and protection against arbitrary code execution vulnerabilities. The SafeTensors format is a standardized binary serialization that skips Python's pickle machinery, enabling safe parallel loading and compatibility across frameworks (PyTorch, TensorFlow, JAX). Model weights are memory-mapped for efficient loading on resource-constrained devices.