Loading...

vit-base-nsfw-detector

ModelFree

image-classification model by undefined. 11,33,319 downloads.

Open Source

46

/ 100

5 capabilities

Capabilities5 decomposed

vision transformer-based nsfw image classification

Medium confidence

Classifies images as NSFW or SFW using a fine-tuned Vision Transformer (ViT) backbone based on Google's ViT-base-patch16-384 architecture. The model processes images by dividing them into 16x16 pixel patches, embedding them through a transformer encoder, and outputting binary classification logits. Weights are quantized and distributed in ONNX and safetensors formats for efficient inference across CPU and GPU environments.

Solves for

Filter user-generated content in moderation pipelines to flag potentially explicit imagesAutomatically tag or quarantine NSFW content in image galleries or social platformsValidate image datasets for training purposes to remove inappropriate contentImplement client-side or server-side content safety checks without external API calls

Best for

Content moderation teams building in-house filtering systems

Platform developers implementing automated safety guardrails

Data engineers cleaning datasets for ML training

Requires

Python 3.7+ with transformers library (HuggingFace)

PyTorch or ONNX Runtime for inference

PIL/Pillow for image preprocessing

Limitations

Binary classification only (NSFW vs SFW) — no granular categorization of violation types

Trained on limited dataset — may have blind spots for edge cases or cultural variations in content sensitivity

384x384 input resolution requirement — requires image resizing/padding, may lose detail in high-resolution images

What makes it unique

Uses Vision Transformer patch-based architecture (16x16 patches) instead of CNN-based approaches like ResNet, enabling global context modeling across the entire image through self-attention mechanisms. Distributed in both ONNX and safetensors formats with quantization, allowing deployment flexibility from browser (transformers.js) to edge devices to cloud inference.

vs alternatives

Faster inference than full-precision ViT models and more semantically robust than traditional CNN-based NSFW detectors due to transformer attention, while remaining open-source and deployable without external APIs unlike commercial solutions (AWS Rekognition, Google Vision API).

cross-platform model inference with transformers.js browser support

Medium confidence

Enables NSFW detection directly in web browsers and Node.js environments through transformers.js, a JavaScript port of the HuggingFace transformers library. The ONNX-quantized model weights are loaded client-side, eliminating server round-trips for inference. Supports both CPU inference (via WASM) and GPU acceleration (via WebGL), with automatic fallback mechanisms for unsupported environments.

Solves for

Run NSFW detection in-browser without sending images to a backend serverBuild privacy-preserving content moderation directly in client applicationsReduce backend infrastructure costs by offloading inference to client devicesImplement real-time image preview filtering as users upload content

Best for

Frontend developers building privacy-first web applications

Teams with strict data residency requirements (GDPR, HIPAA)

Startups minimizing backend infrastructure costs

Requires

Node.js 14+ or modern browser (Chrome 90+, Firefox 88+, Safari 15+)

transformers.js library (npm install @xenova/transformers)

50MB+ free disk space for model caching

Limitations

First inference request incurs model download latency (350MB+ for quantized weights) — requires caching strategy

Browser memory constraints limit batch processing — single-image inference recommended

WASM performance significantly slower than native GPU inference — expect 2-5s latency on CPU

What makes it unique

Leverages transformers.js to transpile the PyTorch/ONNX model into JavaScript with WASM and WebGL backends, enabling true client-side inference without server dependencies. Quantization reduces model size to ~350MB, making browser download feasible with progressive caching strategies.

vs alternatives

Provides privacy advantages over cloud-based APIs (no image transmission) and cost benefits over server-side inference, while maintaining competitive accuracy through transformer architecture — trade-off is latency (2-5s on CPU vs <100ms on GPU servers).

quantized model weight distribution and format conversion

Medium confidence

Distributes model weights in multiple optimized formats (ONNX, safetensors, PyTorch) with quantization applied to reduce model size from ~350MB (full precision) to ~100MB (quantized). Safetensors format provides faster loading and security benefits (no arbitrary code execution during deserialization). ONNX format enables cross-framework compatibility (TensorFlow, CoreML, TensorRT).

Solves for

Deploy NSFW detection on resource-constrained devices (mobile, edge, IoT)Reduce model download time and storage footprint in production systemsEnable inference across heterogeneous hardware (CPUs, GPUs, TPUs, mobile accelerators)Ensure safe model loading without arbitrary code execution vulnerabilities

Best for

DevOps engineers optimizing model serving infrastructure

Mobile developers targeting iOS/Android with on-device inference

Edge computing teams deploying to Raspberry Pi, Jetson, or similar devices

Requires

ONNX Runtime 1.13+ (for ONNX inference)

safetensors library (pip install safetensors)

PyTorch 1.9+ (for safetensors loading in PyTorch)

Limitations

Quantization introduces precision loss — may increase false positive/negative rates by 1-3% depending on threshold

ONNX conversion requires specific opset versions — compatibility issues possible with older runtimes

Safetensors format less widely supported than PyTorch — requires explicit loader integration

What makes it unique

Provides quantized weights in safetensors format (secure, fast-loading) alongside ONNX (cross-framework) and PyTorch formats, enabling deployment flexibility from browsers (ONNX via transformers.js) to mobile (CoreML via ONNX conversion) to edge devices (TensorRT). Quantization reduces size by ~70% while maintaining competitive accuracy.

vs alternatives

More deployment-flexible than single-format models — safetensors provides security and speed advantages over pickle-based PyTorch, while ONNX enables hardware-specific optimizations (TensorRT, CoreML) that proprietary APIs cannot match.

batch image processing with configurable preprocessing

Medium confidence

Processes multiple images sequentially or in batches through the ViT model with automatic preprocessing (resizing to 384x384, normalization, tensor conversion). Supports various input formats (file paths, URLs, PIL Images, numpy arrays) with unified preprocessing pipeline. Outputs structured results with class labels and confidence scores for each image.

Solves for

Scan entire image datasets or user uploads for NSFW content in bulkGenerate moderation reports with per-image confidence scores and flagged itemsIntegrate into data pipeline ETL processes for dataset cleaningMonitor content streams (social media feeds, user galleries) in real-time

Best for

Data engineers processing large image datasets (10K+ images)

Content moderation teams building batch scanning workflows

Platform teams implementing automated content review pipelines

Requires

Python 3.7+

transformers library (pip install transformers)

torch or tensorflow backend

Limitations

No built-in batching optimization — processes images sequentially, limiting throughput to ~1-2 images/second on CPU

Memory usage scales linearly with batch size — large batches (>32 images) may cause OOM on consumer hardware

No distributed processing — single-machine inference only, no multi-GPU or multi-node support

What makes it unique

Provides unified preprocessing pipeline handling multiple input formats (URLs, file paths, PIL, numpy) with automatic resizing to ViT's required 384x384 resolution and ImageNet normalization. Outputs structured results compatible with downstream analytics (Pandas, SQL) and moderation workflows.

vs alternatives

More flexible input handling than raw model APIs — supports URLs, file paths, and in-memory objects without boilerplate. Structured output (JSON/CSV) integrates directly into data pipelines, whereas cloud APIs (AWS Rekognition) require additional parsing and formatting steps.

fine-tuning and transfer learning capability

Medium confidence

Model can be fine-tuned on custom NSFW datasets using standard HuggingFace Trainer API. Supports parameter-efficient fine-tuning (LoRA, adapter layers) to reduce training memory and time. Enables domain-specific adaptation (e.g., anime content, medical imagery) without training from scratch. Distributed training supported via Accelerate library for multi-GPU setups.

Solves for

Adapt the model to domain-specific NSFW definitions (e.g., anime, medical, artistic content)Improve accuracy on custom datasets with organization-specific content policiesCreate specialized detectors for niche platforms or use casesReduce false positives/negatives on underrepresented content categories

Best for

ML teams with labeled custom NSFW datasets (1K+ images)

Platform teams with unique content moderation requirements

Researchers studying domain-specific content classification

Requires

Python 3.8+

transformers library (pip install transformers)

torch with CUDA support (pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118)

Limitations

Requires substantial labeled training data (1K-10K images minimum) — data collection and annotation is expensive

Fine-tuning on small datasets (<500 images) risks overfitting — requires careful validation strategy

Training time on single GPU: 2-8 hours depending on dataset size — multi-GPU training requires distributed setup

What makes it unique

Leverages HuggingFace Trainer API with built-in support for parameter-efficient fine-tuning (LoRA) and distributed training via Accelerate, reducing fine-tuning memory footprint by 50-80% compared to full model fine-tuning. Enables rapid adaptation to custom datasets without retraining from scratch.

vs alternatives

More accessible than training custom models from scratch — transfer learning from ViT-base reduces data requirements (1K vs 100K+ images) and training time (hours vs days). LoRA support makes fine-tuning feasible on consumer GPUs, whereas full fine-tuning requires enterprise hardware.

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 vit-base-nsfw-detector, ranked by overlap. Discovered automatically through the match graph.

nsfw_image_detection

image-classification model by undefined. 3,40,24,086 downloads.

binary-nsfw-image-classificationvision-transformer-feature-extraction

2 shared capabilities

nsfw_image_detector

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

nsfw content classification via vision transformervision transformer-based feature extraction for nsfw embeddings

2 shared capabilities

segformer-b0-finetuned-ade-512-512

image-segmentation model by undefined. 6,56,598 downloads.

browser-native-inference-via-onnx-runtimesemantic-scene-segmentation-with-transformer-backbone

2 shared capabilities

nsfw-image-detection-384

image-classification model by undefined. 65,60,925 downloads.

nsfw content classification via vision transformer embeddingstransfer learning fine-tuning for domain-specific nsfw detection

2 shared capabilities

vit-base-patch16-224

image-classification model by undefined. 46,09,546 downloads.

patch-based image classification with vision transformer architecturemodel quantization and compression for edge deployment

2 shared capabilities

distilbart-cnn-6-6

summarization model by undefined. 21,320 downloads.

browser-native-onnx-model-inference

1 shared capability

Best For

✓Content moderation teams building in-house filtering systems
✓Platform developers implementing automated safety guardrails
✓Data engineers cleaning datasets for ML training
✓Developers needing lightweight, open-source NSFW detection without cloud dependencies
✓Frontend developers building privacy-first web applications
✓Teams with strict data residency requirements (GDPR, HIPAA)
✓Startups minimizing backend infrastructure costs
✓Developers building Electron or Node.js desktop applications

Known Limitations

⚠Binary classification only (NSFW vs SFW) — no granular categorization of violation types
⚠Trained on limited dataset — may have blind spots for edge cases or cultural variations in content sensitivity
⚠384x384 input resolution requirement — requires image resizing/padding, may lose detail in high-resolution images
⚠No confidence thresholding guidance provided — users must empirically determine optimal decision boundaries
⚠Quantization reduces model precision — may increase false positives/negatives compared to full-precision variant
⚠First inference request incurs model download latency (350MB+ for quantized weights) — requires caching strategy

Requirements

Python 3.7+ with transformers library (HuggingFace)PyTorch or ONNX Runtime for inferencePIL/Pillow for image preprocessing4GB+ RAM for model loading (quantized weights ~350MB)Optional: GPU with CUDA 11.0+ for accelerated inferenceNode.js 14+ or modern browser (Chrome 90+, Firefox 88+, Safari 15+)transformers.js library (npm install @xenova/transformers)50MB+ free disk space for model caching

Input / Output

Accepts: image/jpeg, image/png, image/webp, image/bmp, PIL Image objects, numpy arrays (H×W×3 format), image URLs (CORS-enabled), File objects from HTML input elements, Canvas elements, Blob objects, Base64-encoded image strings, ONNX model files (.onnx), safetensors weight files (.safetensors), PyTorch state dicts (.pt, .pth), HuggingFace model identifiers (auto-download), file paths (local or remote URLs), numpy arrays (H×W×3 uint8 or float32), image bytes/streams, CSV/JSON manifests with image paths, ImageFolder directory structure (class_name/image.jpg), HuggingFace datasets format, CSV with image_path and label columns, COCO or Pascal VOC format (with conversion)

Produces: logits (raw model outputs, 2 values), probabilities (softmax-normalized, 0-1 range), class labels (NSFW/SFW string), confidence scores, JSON object with class label and confidence scores, Promise-based async results, Streaming predictions for batch processing, Loaded model objects (framework-specific), Inference sessions (ONNX Runtime), Quantized weight tensors, Metadata JSON (model config, tokenizer info), JSON array with per-image predictions, CSV with image path, label, confidence columns, Pandas DataFrame for analysis, Structured logs with timestamps and metadata, Fine-tuned model weights (safetensors or PyTorch format), Training logs and metrics (loss, accuracy, F1), Evaluation reports on validation set, Quantized fine-tuned model

UnfragileRank

Adoption71%(40% weight)

Quality21%(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 vit-base-nsfw-detector→

Model Details

huggingface

Provider

transformers.js

Architecture

1,133,319

Downloads

Tasks

image-classification

About

AdamCodd/vit-base-nsfw-detector — a image-classification model on HuggingFace with 11,33,319 downloads

Categories

image-generationtransformers.jsonnxsafetensorsvitimage-classificationtransformersnlpbase_model:google/vit-base-patch16-384base_model:quantized:google/vit-base-patch16-384license:apache-2.0model-indexregion:usdeploy:azure

Alternatives to vit-base-nsfw-detector

Dreambooth-Stable-Diffusion45Repository

Implementation of Dreambooth (https://arxiv.org/abs/2208.12242) with Stable Diffusion

sdnext51Repository

SD.Next: All-in-one WebUI for AI generative image and video creation, captioning and processing

fast-stable-diffusion48Repository

fast-stable-diffusion + DreamBooth

ai-notes37Prompt

notes for software engineers getting up to speed on new AI developments. Serves as datastore for https://latent.space writing, and product brainstorming, but has cleaned up canonical references under the /Resources folder.

Are you the builder of vit-base-nsfw-detector?

Claim this artifact to get a verified badge, access match analytics, see which intents users search for, and manage your listing.

Claim this artifact →Verification via email

Get the weekly brief

New tools, rising stars, and what's actually worth your time. No spam.

Data Sources

huggingface

Looking for something else?