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nsfw_image_detector

ModelFree

image-classification model by undefined. 9,43,400 downloads.

Open Source

43

/ 100

5 capabilities

Capabilities5 decomposed

nsfw content classification via vision transformer

Medium confidence

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.

Solves for

Automatically filter user-uploaded images in content platforms to prevent explicit materialBuild content moderation pipelines that flag potentially unsafe images for human reviewImplement real-time NSFW detection in image hosting or social media applicationsCreate 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

Requires

Python 3.7+

PyTorch 1.9+ or compatible ONNX runtime

Transformers library 4.20+

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

What makes it unique

Uses EVA-02 vision transformer architecture (arxiv:2303.11331) with masked image modeling pre-training on ImageNet-22k, providing stronger semantic understanding of image content compared to standard ResNet or ViT baselines. The patch-based attention mechanism enables fine-grained analysis of image regions, improving detection of subtle NSFW indicators.

vs alternatives

More accurate than rule-based or shallow CNN approaches (e.g., OpenNSFW) due to transformer-based semantic understanding; faster inference than multi-stage ensemble methods while maintaining competitive accuracy on diverse NSFW datasets.

batch image inference with safetensors format

Medium confidence

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.

Solves for

Process large image datasets (thousands to millions) for content moderation at scaleImplement efficient inference pipelines that minimize model loading overheadDeploy the model across distributed workers or GPU clusters for parallel processingIntegrate 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

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)

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

What makes it unique

Leverages safetensors format for memory-mapped weight loading, eliminating pickle deserialization overhead and enabling faster model initialization in batch pipelines. This is particularly advantageous for serverless or containerized deployments where model loading time directly impacts latency.

vs alternatives

Faster model loading and lower memory fragmentation than standard PyTorch .pt checkpoints; compatible with ONNX Runtime and TensorFlow via safetensors converters, enabling cross-framework deployment flexibility.

vision transformer-based feature extraction for nsfw embeddings

Medium confidence

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.

Solves for

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 categoriesPerform clustering analysis on image datasets to identify patterns in unsafe contentTransfer 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)

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

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

What makes it unique

EVA-02 architecture provides rich intermediate representations through multi-head self-attention layers, enabling extraction of hierarchical semantic features (low-level texture to high-level semantic concepts) that are more expressive than single-layer CNN features for NSFW detection tasks.

vs alternatives

Transformer-based embeddings capture global image context and long-range dependencies better than CNN features; enables few-shot fine-tuning with smaller labeled datasets compared to training ResNet-based classifiers from scratch.

multi-cloud deployment with azure compatibility

Medium confidence

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.

Solves for

Deploy NSFW detection as a managed Azure ML endpoint with auto-scalingIntegrate NSFW filtering into Azure-based content moderation workflowsBuild serverless NSFW detection using Azure Functions or Container InstancesComply 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

Requires

Azure subscription with Machine Learning workspace

Azure ML SDK (azureml-core 1.40+)

Docker for containerization (optional but recommended)

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

What makes it unique

Pre-validated for Azure ML endpoints with safetensors format support, eliminating custom conversion or serialization steps. The model card explicitly documents Azure compatibility, reducing deployment friction for Azure-native organizations.

vs alternatives

Faster time-to-production on Azure compared to models requiring custom containerization or format conversion; integrates natively with Azure ML's model registry, versioning, and monitoring infrastructure.

mit-licensed open-source model with commercial usage rights

Medium confidence

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.

Solves for

Deploy NSFW detection in commercial products without licensing restrictions or royalty paymentsAudit model architecture and training methodology for safety and bias analysisFine-tune or modify the model for proprietary NSFW detection requirementsIntegrate 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

Requires

Acceptance of MIT license terms

Understanding of model limitations and potential biases

Responsibility for compliance with local content moderation regulations

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

What makes it unique

MIT license with 943k+ downloads creates a large, active community for auditing, improvement, and specialized fine-tuning. The open-source nature enables transparency into model behavior and potential biases, supporting responsible AI practices.

vs alternatives

No licensing costs or restrictions compared to proprietary NSFW detection APIs (e.g., AWS Rekognition, Google Vision); enables full model customization and on-premises deployment without vendor lock-in.

Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.

Related Artifactssharing capabilities

Artifacts that share capabilities with nsfw_image_detector, ranked by overlap. Discovered automatically through the match graph.

nsfw-image-detection-384

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nsfw content classification via vision transformer embeddingstransfer learning fine-tuning for domain-specific nsfw detectionbatch image safety screening with embedding extractionreal-time image safety inference with low-latency prediction

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CommunityForensics-DeepfakeDet-ViT

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batch image classification with safetensors model loading

1 shared capability

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
✓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

Known 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
⚠Batch size limited by available GPU/CPU memory — no automatic batching or dynamic batch sizing

Requirements

Python 3.7+PyTorch 1.9+ or compatible ONNX runtimeTransformers library 4.20+Pillow or OpenCV for image preprocessing4GB+ 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)

Input / Output

Accepts: image (JPEG, PNG, WebP, BMP), image tensor (torch.Tensor or numpy array with shape [batch, 3, 448, 448]), batch of images (torch.Tensor, shape [batch_size, 3, 448, 448]), list of image file paths (JPEG, PNG, WebP), image tensor (torch.Tensor, shape [batch, 3, 448, 448]), image file paths for preprocessing, image file (uploaded via Azure ML endpoint), image URL (for remote image fetching), base64-encoded image in JSON payload

Produces: classification logits (float tensor, shape [batch, 2]), class probabilities (softmax output: [prob_sfw, prob_nsfw]), binary label (0=SFW, 1=NSFW), batch classification logits (torch.Tensor, shape [batch_size, 2]), batch probabilities (numpy array or pandas DataFrame with NSFW scores), feature embeddings (torch.Tensor, shape [batch, embedding_dim]), similarity scores (cosine similarity between image embeddings), JSON response with classification label and confidence score, Azure ML batch prediction output (CSV or Parquet)

UnfragileRank

Adoption69%(40% weight)

Quality13%(20% weight)

Ecosystem50%(15% weight)

Match Graph10%(20% weight)

Freshness75%(5% weight)

UnfragileRank is computed from adoption signals, documentation quality, ecosystem connectivity, match graph feedback, and freshness. No artifact can pay for a higher rank.

Type: Model

5 capabilities

Visit nsfw_image_detector→

Model Details

huggingface

Provider

transformers

Architecture

943,400

Downloads

Tasks

image-classification

About

Freepik/nsfw_image_detector — a image-classification model on HuggingFace with 9,43,400 downloads

Categories

image-generationtransformerssafetensorstimm_wrapperimage-classificationpytorcharxiv:2303.11331base_model:timm/eva02_base_patch14_448.mim_in22k_ft_in22k_in1kbase_model:finetune:timm/eva02_base_patch14_448.mim_in22k_ft_in22k_in1klicense:mitendpoints_compatibleregion:usnot-for-all-audiencesdeploy:azure

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