{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hf-model-freepik--nsfw_image_detector","slug":"freepik--nsfw_image_detector","name":"nsfw_image_detector","type":"model","url":"https://huggingface.co/Freepik/nsfw_image_detector","page_url":"https://unfragile.ai/freepik--nsfw_image_detector","categories":["image-generation"],"tags":["transformers","safetensors","timm_wrapper","image-classification","pytorch","arxiv:2303.11331","base_model:timm/eva02_base_patch14_448.mim_in22k_ft_in22k_in1k","base_model:finetune:timm/eva02_base_patch14_448.mim_in22k_ft_in22k_in1k","license:mit","endpoints_compatible","deploy:azure","region:us","not-for-all-audiences"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"hf-model-freepik--nsfw_image_detector__cap_0","uri":"capability://image.visual.nsfw.content.classification.via.vision.transformer","name":"nsfw content classification via vision transformer","description":"Classifies images as NSFW or SFW using a fine-tuned EVA-02 vision transformer backbone (eva02_base_patch14_448) pre-trained on ImageNet-22k and ImageNet-1k. The model processes 448x448 pixel images through a patch-based attention mechanism, extracting semantic features that distinguish adult/explicit content from safe content. Fine-tuning was performed on curated NSFW/SFW datasets to optimize the decision boundary for content moderation tasks.","intents":["Automatically filter user-uploaded images in content platforms to prevent explicit material","Build content moderation pipelines that flag potentially unsafe images for human review","Implement real-time NSFW detection in image hosting or social media applications","Create safety guardrails for image generation models to prevent unsafe outputs"],"best_for":["Content moderation teams building safety systems for user-generated content platforms","Developers implementing image filtering in social networks or marketplaces","AI safety engineers adding content guardrails to generative image systems","Compliance teams automating NSFW detection for regulatory requirements"],"limitations":["Binary classification only (NSFW vs SFW) — no granular categorization of violation types","Fixed input resolution of 448x448 pixels — requires preprocessing/resizing of arbitrary-sized images","No confidence score calibration documented — threshold tuning required for production deployment","Performance on edge cases (artistic nudity, medical imagery, borderline content) not publicly benchmarked","Inference latency and memory footprint not specified — GPU/CPU requirements unclear"],"requires":["Python 3.7+","PyTorch 1.9+ or compatible ONNX runtime","Transformers library 4.20+","Pillow or OpenCV for image preprocessing","4GB+ RAM minimum (8GB+ recommended for batch processing)","CUDA 11.0+ for GPU acceleration (optional but recommended)"],"input_types":["image (JPEG, PNG, WebP, BMP)","image tensor (torch.Tensor or numpy array with shape [batch, 3, 448, 448])"],"output_types":["classification logits (float tensor, shape [batch, 2])","class probabilities (softmax output: [prob_sfw, prob_nsfw])","binary label (0=SFW, 1=NSFW)"],"categories":["image-visual","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-freepik--nsfw_image_detector__cap_1","uri":"capability://data.processing.analysis.batch.image.inference.with.safetensors.format","name":"batch image inference with safetensors format","description":"Supports efficient batch processing of multiple images through the safetensors weight format, which enables memory-mapped loading and faster model initialization compared to pickle-based PyTorch checkpoints. The model can be loaded once and applied to batches of images, reducing per-image overhead and enabling horizontal scaling across multiple workers or GPUs.","intents":["Process large image datasets (thousands to millions) for content moderation at scale","Implement efficient inference pipelines that minimize model loading overhead","Deploy the model across distributed workers or GPU clusters for parallel processing","Integrate NSFW detection into batch ETL workflows for data cleaning"],"best_for":["Data engineering teams processing large image corpora for content filtering","MLOps engineers building scalable inference pipelines","Platforms with high-volume image uploads requiring batch moderation","Researchers benchmarking NSFW detection across large datasets"],"limitations":["Batch size limited by available GPU/CPU memory — no automatic batching or dynamic batch sizing","No built-in distributed inference orchestration — requires external frameworks (Ray, Kubernetes, etc.)","Safetensors format requires compatible loaders — not all inference engines support it natively","No streaming or online learning — model weights are static post-deployment"],"requires":["safetensors library 0.3.0+","PyTorch 1.9+ with CUDA support (for GPU batching)","Sufficient GPU memory for batch size (estimate: 2-4GB per 32 images at 448x448)"],"input_types":["batch of images (torch.Tensor, shape [batch_size, 3, 448, 448])","list of image file paths (JPEG, PNG, WebP)"],"output_types":["batch classification logits (torch.Tensor, shape [batch_size, 2])","batch probabilities (numpy array or pandas DataFrame with NSFW scores)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-freepik--nsfw_image_detector__cap_2","uri":"capability://data.processing.analysis.vision.transformer.based.feature.extraction.for.nsfw.embeddings","name":"vision transformer-based feature extraction for nsfw embeddings","description":"Extracts intermediate feature representations from the EVA-02 backbone before the final classification head, enabling use of the model as a feature encoder for downstream tasks. The transformer's patch embeddings and attention layers capture semantic image representations that can be used for similarity search, clustering, or custom fine-tuning on domain-specific NSFW variants.","intents":["Extract image embeddings for similarity-based NSFW detection (find similar unsafe images in a corpus)","Build custom classifiers on top of frozen EVA-02 features for specialized NSFW categories","Perform clustering analysis on image datasets to identify patterns in unsafe content","Transfer learning: fine-tune the model on proprietary NSFW datasets with limited labeled examples"],"best_for":["Research teams studying NSFW content patterns and clustering","Platforms needing similarity-based duplicate detection for unsafe images","Companies with domain-specific NSFW definitions (e.g., artistic nudity vs explicit content)","Few-shot learning scenarios where custom NSFW categories require rapid model adaptation"],"limitations":["Feature extraction requires access to intermediate layers — not exposed in standard HuggingFace pipeline API","Embedding dimensionality not specified — vector storage and similarity search infrastructure must be pre-planned","No pre-computed embedding index provided — users must generate embeddings for existing image corpora","Transfer learning performance on custom NSFW variants not benchmarked"],"requires":["PyTorch 1.9+ with model.eval() and torch.no_grad() context management","Access to model architecture source code or custom forward hooks","Vector database (Pinecone, Weaviate, Milvus) for similarity search at scale","GPU memory for storing intermediate activations (estimate: 1-2GB per 100 images)"],"input_types":["image tensor (torch.Tensor, shape [batch, 3, 448, 448])","image file paths for preprocessing"],"output_types":["feature embeddings (torch.Tensor, shape [batch, embedding_dim])","similarity scores (cosine similarity between image embeddings)"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-freepik--nsfw_image_detector__cap_3","uri":"capability://automation.workflow.multi.cloud.deployment.with.azure.compatibility","name":"multi-cloud deployment with azure compatibility","description":"Model is compatible with Azure Machine Learning endpoints, enabling deployment through Azure's managed inference infrastructure. The safetensors format and PyTorch compatibility allow seamless containerization and deployment to Azure Container Instances, Azure Kubernetes Service (AKS), or Azure ML's batch inference pipelines without custom conversion steps.","intents":["Deploy NSFW detection as a managed Azure ML endpoint with auto-scaling","Integrate NSFW filtering into Azure-based content moderation workflows","Build serverless NSFW detection using Azure Functions or Container Instances","Comply with data residency requirements by hosting inference in specific Azure regions"],"best_for":["Enterprises standardized on Azure cloud infrastructure","Teams requiring GDPR/data residency compliance with EU or region-specific hosting","Organizations leveraging Azure ML for MLOps and model management","Hybrid deployments combining on-premises and Azure cloud resources"],"limitations":["Azure-specific deployment requires Azure ML SDK and Azure account setup","Inference costs scale with compute instance size and request volume — no cost optimization guidance provided","Cold start latency for serverless deployments not documented","No built-in monitoring or alerting for NSFW detection accuracy drift in production"],"requires":["Azure subscription with Machine Learning workspace","Azure ML SDK (azureml-core 1.40+)","Docker for containerization (optional but recommended)","Azure Container Registry for image storage","Appropriate Azure compute SKU (CPU or GPU based on throughput requirements)"],"input_types":["image file (uploaded via Azure ML endpoint)","image URL (for remote image fetching)","base64-encoded image in JSON payload"],"output_types":["JSON response with classification label and confidence score","Azure ML batch prediction output (CSV or Parquet)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-freepik--nsfw_image_detector__cap_4","uri":"capability://safety.moderation.mit.licensed.open.source.model.with.commercial.usage.rights","name":"mit-licensed open-source model with commercial usage rights","description":"Released under MIT license, enabling unrestricted commercial use, modification, and redistribution without attribution requirements. The open-source nature with 943k+ downloads provides transparency into model architecture, training data provenance, and enables community contributions, audits, and fine-tuning for specialized use cases.","intents":["Deploy NSFW detection in commercial products without licensing restrictions or royalty payments","Audit model architecture and training methodology for safety and bias analysis","Fine-tune or modify the model for proprietary NSFW detection requirements","Integrate into open-source content moderation frameworks without legal constraints"],"best_for":["Startups and small teams with limited licensing budgets","Open-source projects requiring permissive dependencies","Enterprises requiring full model transparency and auditability","Researchers studying NSFW detection and content moderation"],"limitations":["MIT license provides no warranty or liability protection — users assume all responsibility for deployment","No official support or SLA — community-driven support only","Training data composition and potential biases not fully documented","No guarantee of model accuracy or fairness across demographic groups"],"requires":["Acceptance of MIT license terms","Understanding of model limitations and potential biases","Responsibility for compliance with local content moderation regulations"],"input_types":[],"output_types":[],"categories":["safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":44,"verified":false,"data_access_risk":"high","permissions":["Python 3.7+","PyTorch 1.9+ or compatible ONNX runtime","Transformers library 4.20+","Pillow or OpenCV for image preprocessing","4GB+ RAM minimum (8GB+ recommended for batch processing)","CUDA 11.0+ for GPU acceleration (optional but recommended)","safetensors library 0.3.0+","PyTorch 1.9+ with CUDA support (for GPU batching)","Sufficient GPU memory for batch size (estimate: 2-4GB per 32 images at 448x448)","PyTorch 1.9+ with model.eval() and torch.no_grad() context management"],"failure_modes":["Binary classification only (NSFW vs SFW) — no granular categorization of violation types","Fixed input resolution of 448x448 pixels — requires preprocessing/resizing of arbitrary-sized images","No confidence score calibration documented — threshold tuning required for production deployment","Performance on edge cases (artistic nudity, medical imagery, borderline content) not publicly benchmarked","Inference latency and memory footprint not specified — GPU/CPU requirements unclear","Batch size limited by available GPU/CPU memory — no automatic batching or dynamic batch sizing","No built-in distributed inference orchestration — requires external frameworks (Ray, Kubernetes, etc.)","Safetensors format requires compatible loaders — not all inference engines support it natively","No streaming or online learning — model weights are static post-deployment","Feature extraction requires access to intermediate layers — not exposed in standard HuggingFace pipeline API","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.6786012095382664,"quality":0.2,"ecosystem":0.5000000000000001,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.35,"quality":0.2,"ecosystem":0.1,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:22.765Z","last_scraped_at":"2026-05-03T14:22:59.355Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":814657,"model_likes":57}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=freepik--nsfw_image_detector","compare_url":"https://unfragile.ai/compare?artifact=freepik--nsfw_image_detector"}},"signature":"BboHI6XWC5XAC+FcAQRJ6KVRBd4TeETBon/d2V3PFj7dtfQMtXilrCcGagw3f0pKlNP0OJeC9yDcGeRl+zruCQ==","signedAt":"2026-06-23T04:41:44.626Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/freepik--nsfw_image_detector","artifact":"https://unfragile.ai/freepik--nsfw_image_detector","verify":"https://unfragile.ai/api/v1/verify?slug=freepik--nsfw_image_detector","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}