{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hf-model-marqo--nsfw-image-detection-384","slug":"marqo--nsfw-image-detection-384","name":"nsfw-image-detection-384","type":"model","url":"https://huggingface.co/Marqo/nsfw-image-detection-384","page_url":"https://unfragile.ai/marqo--nsfw-image-detection-384","categories":["image-generation"],"tags":["timm","safetensors","image-classification","license:apache-2.0","region:us"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"hf-model-marqo--nsfw-image-detection-384__cap_0","uri":"capability://image.visual.nsfw.content.classification.via.vision.transformer.embeddings","name":"nsfw content classification via vision transformer embeddings","description":"Classifies images as safe or unsafe for work using a timm-based vision transformer backbone (384-dimensional embedding space) fine-tuned on NSFW/SFW datasets. The model encodes images into a learned embedding space where unsafe content clusters distinctly from safe content, enabling binary or multi-class classification through a trained classification head. Uses safetensors format for efficient model serialization and loading.","intents":["Filter user-generated content in moderation pipelines before publishing","Automatically tag or quarantine potentially unsafe images in bulk uploads","Build content safety checks into image hosting or social platforms","Screen images in e-commerce or community platforms for policy violations"],"best_for":["Content moderation teams building automated safety systems","Platform engineers implementing real-time image filtering","Developers building community-driven applications with UGC","Teams needing open-source alternatives to proprietary moderation APIs"],"limitations":["Binary or limited-class classification only — does not distinguish between types of unsafe content (violence, explicit, etc.)","384-dimensional embedding space may not capture nuanced edge cases or cultural context variations","Inference latency depends on hardware; GPU acceleration recommended for production throughput","Model trained on specific NSFW/SFW datasets — performance may degrade on out-of-distribution image styles (e.g., artwork, anime, medical imagery)","No built-in confidence thresholding or uncertainty quantification — requires external calibration for production deployment"],"requires":["Python 3.7+","PyTorch 1.9+ or compatible deep learning framework","timm library (PyTorch Image Models) for vision transformer backbone","transformers library for model loading and inference","Hugging Face Hub access (for model download via huggingface_hub)","Sufficient VRAM for 384-dim embedding inference (~2-4GB for batch processing)"],"input_types":["JPEG images","PNG images","WebP images","Tensor arrays (pre-loaded in memory)"],"output_types":["Binary classification label (safe/unsafe)","Classification probabilities (softmax scores per class)","384-dimensional embedding vector (for downstream similarity search or clustering)"],"categories":["image-visual","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-marqo--nsfw-image-detection-384__cap_1","uri":"capability://image.visual.batch.image.safety.screening.with.embedding.extraction","name":"batch image safety screening with embedding extraction","description":"Processes multiple images in parallel, extracting both classification predictions and 384-dimensional embeddings for each image in a single forward pass. Supports batching via PyTorch DataLoader or manual batch stacking, enabling efficient throughput for large-scale content moderation workflows. Embeddings can be persisted to vector databases for downstream similarity-based filtering or clustering of unsafe content patterns.","intents":["Screen thousands of user uploads in a single batch job for moderation","Extract embeddings for all images in a dataset to build a safety-aware vector index","Identify clusters of similar unsafe content for pattern analysis","Build a content similarity search system to find related unsafe images"],"best_for":["Data engineers building batch moderation pipelines","ML teams analyzing content safety patterns at scale","Developers integrating safety checks into ETL workflows","Researchers studying NSFW content distribution and clustering"],"limitations":["Batch processing requires loading all images into memory — memory constraints limit batch size on consumer hardware","No streaming or incremental inference — entire batch must complete before results are available","Embeddings are fixed 384-dimensional vectors — cannot be dynamically resized for different downstream tasks","No built-in deduplication — duplicate images in batch will be processed redundantly"],"requires":["Python 3.7+","PyTorch with CUDA support (for GPU acceleration) or CPU fallback","Sufficient RAM for batch size × image resolution (e.g., 32 images × 384×384 ≈ 2GB)","Optional: vector database client (Pinecone, Weaviate, Milvus) for embedding storage"],"input_types":["Batch of JPEG/PNG/WebP images","Image file paths (loaded via PIL or OpenCV)","Pre-loaded image tensors (shape: [batch_size, 3, 384, 384])"],"output_types":["Classification labels per image (batch_size,)","Classification probabilities (batch_size, num_classes)","Embedding matrix (batch_size, 384)"],"categories":["image-visual","data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-marqo--nsfw-image-detection-384__cap_2","uri":"capability://image.visual.real.time.image.safety.inference.with.low.latency.prediction","name":"real-time image safety inference with low-latency prediction","description":"Performs single-image NSFW classification with minimal latency suitable for synchronous request-response workflows (e.g., API endpoints, chat applications). Uses optimized inference paths via ONNX export or TorchScript compilation to reduce overhead. Can be deployed as a microservice or embedded in application servers for immediate safety feedback on user uploads.","intents":["Reject unsafe images immediately during upload in a web application","Provide real-time safety feedback to users before they post content","Block unsafe images in chat or messaging applications before delivery","Implement safety gates in API endpoints with <100ms latency requirements"],"best_for":["Full-stack developers building user-facing content platforms","Backend engineers implementing API safety gates","DevOps teams deploying inference microservices","Teams with strict latency budgets (<200ms per request)"],"limitations":["Single-image inference only — no batch optimization benefits","Latency varies with hardware (CPU: 500ms–2s, GPU: 50–200ms) — requires GPU for production SLAs","No caching of results — identical images processed redundantly","Model size (~384MB for full transformer) requires sufficient disk/memory on edge devices"],"requires":["Python 3.7+ or compiled inference runtime (ONNX Runtime, TensorRT)","GPU with CUDA 11.0+ (recommended for <200ms latency) or CPU fallback","Web framework (FastAPI, Flask, Django) for API wrapping","Optional: ONNX Runtime or TorchScript for optimized inference"],"input_types":["Single JPEG/PNG/WebP image","Image URL (fetched and decoded on-the-fly)","Base64-encoded image string","Pre-loaded PIL Image or torch.Tensor"],"output_types":["Binary classification label (safe/unsafe)","Classification probability (confidence score 0–1)","Optional: 384-dimensional embedding for downstream analysis"],"categories":["image-visual","safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-marqo--nsfw-image-detection-384__cap_3","uri":"capability://image.visual.transfer.learning.fine.tuning.for.domain.specific.nsfw.detection","name":"transfer learning fine-tuning for domain-specific nsfw detection","description":"Leverages the pre-trained vision transformer backbone and 384-dimensional embedding space as a feature extractor for custom NSFW classification tasks. Enables fine-tuning on domain-specific datasets (e.g., medical imagery, artwork, anime) by replacing or retraining the classification head while freezing or partially unfreezing the backbone. Uses standard PyTorch training loops with cross-entropy loss and gradient descent optimization.","intents":["Adapt the model to detect unsafe content specific to your platform or industry","Reduce false positives on artwork, medical images, or other edge cases","Train on proprietary datasets without sharing data with external APIs","Build multiple specialized classifiers (e.g., violence vs. explicit) from shared embeddings"],"best_for":["ML engineers with labeled domain-specific datasets (100+ examples)","Teams needing custom safety policies beyond generic NSFW/SFW","Organizations with privacy constraints preventing cloud-based APIs","Researchers studying content moderation in specialized domains"],"limitations":["Requires labeled training data — no zero-shot or few-shot learning without additional techniques","Fine-tuning on small datasets (<500 examples) risks overfitting — requires regularization and validation splits","Backbone weights are frozen by default — full model fine-tuning requires significant compute and data","No built-in hyperparameter search — requires manual tuning of learning rate, batch size, and regularization","Transfer learning assumes source domain (generic NSFW) is similar to target domain — may fail for highly specialized content"],"requires":["Python 3.7+","PyTorch 1.9+","Labeled dataset with 100+ images per class (minimum)","GPU with 8GB+ VRAM for fine-tuning (16GB+ recommended)","Training infrastructure (Jupyter, Weights & Biases, or MLflow for experiment tracking)"],"input_types":["Labeled image dataset (directory structure or CSV manifest)","Pre-computed embeddings (for linear probe fine-tuning)","Augmented images (via torchvision.transforms or albumentations)"],"output_types":["Fine-tuned model checkpoint (PyTorch .pt or safetensors format)","Training metrics (loss, accuracy, F1 score)","Validation predictions and confusion matrices"],"categories":["image-visual","data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-marqo--nsfw-image-detection-384__cap_4","uri":"capability://image.visual.embedding.space.similarity.search.for.unsafe.content.clustering","name":"embedding-space similarity search for unsafe content clustering","description":"Extracts 384-dimensional embeddings for images and enables vector similarity search to find visually similar unsafe content. Embeddings can be indexed in vector databases (Pinecone, Weaviate, Milvus) or used with approximate nearest neighbor (ANN) algorithms (FAISS, Annoy) for fast retrieval. Enables clustering of unsafe content patterns without re-running classification on every image.","intents":["Find all images similar to a flagged unsafe image in a large dataset","Cluster unsafe content to identify coordinated abuse or spam campaigns","Build a content deduplication system to avoid processing duplicate unsafe images","Analyze patterns in unsafe content to improve moderation policies"],"best_for":["Content moderation teams analyzing abuse patterns","Platform engineers building deduplication systems","Researchers studying content distribution and clustering","Teams managing large image repositories with safety concerns"],"limitations":["Similarity search is approximate — may miss visually similar images if embedding space is not well-calibrated","Requires pre-computing and storing embeddings for all images — significant storage overhead (384 floats × num_images ≈ 1.5KB per image)","Vector database setup and maintenance adds operational complexity","Similarity thresholds must be manually tuned — no automatic calibration","Embeddings are fixed 384-dimensional — cannot be dynamically adjusted for different similarity metrics"],"requires":["Python 3.7+","Vector database (Pinecone, Weaviate, Milvus) or local ANN library (FAISS, Annoy)","Sufficient storage for embeddings (1.5KB per image × dataset size)","Optional: Approximate nearest neighbor library (FAISS, Annoy, ScaNN) for local indexing"],"input_types":["Query image (converted to 384-dim embedding)","Pre-computed embedding vectors (batch or single)","Embedding indices in vector database"],"output_types":["List of similar image IDs with similarity scores","Clustering assignments (via k-means or DBSCAN on embeddings)","Similarity matrix (pairwise distances between embeddings)"],"categories":["image-visual","search-retrieval","data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":50,"verified":false,"data_access_risk":"high","permissions":["Python 3.7+","PyTorch 1.9+ or compatible deep learning framework","timm library (PyTorch Image Models) for vision transformer backbone","transformers library for model loading and inference","Hugging Face Hub access (for model download via huggingface_hub)","Sufficient VRAM for 384-dim embedding inference (~2-4GB for batch processing)","PyTorch with CUDA support (for GPU acceleration) or CPU fallback","Sufficient RAM for batch size × image resolution (e.g., 32 images × 384×384 ≈ 2GB)","Optional: vector database client (Pinecone, Weaviate, Milvus) for embedding storage","Python 3.7+ or compiled inference runtime (ONNX Runtime, TensorRT)"],"failure_modes":["Binary or limited-class classification only — does not distinguish between types of unsafe content (violence, explicit, etc.)","384-dimensional embedding space may not capture nuanced edge cases or cultural context variations","Inference latency depends on hardware; 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