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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.","intents":["Filter user-generated content in moderation pipelines to flag potentially explicit images","Automatically tag or quarantine NSFW content in image galleries or social platforms","Validate image datasets for training purposes to remove inappropriate content","Implement 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","Developers needing lightweight, open-source NSFW detection without cloud dependencies"],"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"],"requires":["Python 3.7+ with transformers library (HuggingFace)","PyTorch or ONNX Runtime for inference","PIL/Pillow for image preprocessing","4GB+ RAM for model loading (quantized weights ~350MB)","Optional: GPU with CUDA 11.0+ for accelerated inference"],"input_types":["image/jpeg","image/png","image/webp","image/bmp","PIL Image objects","numpy arrays (H×W×3 format)"],"output_types":["logits (raw model outputs, 2 values)","probabilities (softmax-normalized, 0-1 range)","class labels (NSFW/SFW string)","confidence scores"],"categories":["image-visual","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-adamcodd--vit-base-nsfw-detector__cap_1","uri":"capability://image.visual.cross.platform.model.inference.with.transformers.js.browser.support","name":"cross-platform model inference with transformers.js browser support","description":"Enables NSFW detection directly in web browsers and Node.js environments through transformers.js, a JavaScript port of the HuggingFace transformers library. 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Supports both CPU inference (via WASM) and GPU acceleration (via WebGL), with automatic fallback mechanisms for unsupported environments.","intents":["Run NSFW detection in-browser without sending images to a backend server","Build privacy-preserving content moderation directly in client applications","Reduce backend infrastructure costs by offloading inference to client devices","Implement 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","Developers building Electron or Node.js desktop applications"],"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","WebGL acceleration limited to browsers with WebGL 2.0 support — fallback to WASM on older browsers","No built-in image preprocessing — developers must handle resizing, normalization, and format conversion"],"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","WebGL 2.0 support for GPU acceleration (optional but recommended)","CORS-enabled image sources if loading from external URLs"],"input_types":["image URLs (CORS-enabled)","File objects from HTML input elements","Canvas elements","Blob objects","Base64-encoded image strings"],"output_types":["JSON object with class label and confidence scores","Promise-based async results","Streaming predictions for batch processing"],"categories":["image-visual","tool-use-integration","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-adamcodd--vit-base-nsfw-detector__cap_2","uri":"capability://data.processing.analysis.quantized.model.weight.distribution.and.format.conversion","name":"quantized model weight distribution and format conversion","description":"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).","intents":["Deploy NSFW detection on resource-constrained devices (mobile, edge, IoT)","Reduce model download time and storage footprint in production systems","Enable 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","Security-conscious teams requiring sandboxed model loading"],"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","No mixed-precision quantization — entire model uses uniform bit-width (typically int8)","Format conversion is one-way — cannot reliably recover full-precision weights from quantized versions"],"requires":["ONNX Runtime 1.13+ (for ONNX inference)","safetensors library (pip install safetensors)","PyTorch 1.9+ (for safetensors loading in PyTorch)","TensorRT 8.0+ (optional, for NVIDIA GPU optimization)","CoreML Tools (optional, for iOS deployment)"],"input_types":["ONNX model files (.onnx)","safetensors weight files (.safetensors)","PyTorch state dicts (.pt, .pth)","HuggingFace model identifiers (auto-download)"],"output_types":["Loaded model objects (framework-specific)","Inference sessions (ONNX Runtime)","Quantized weight tensors","Metadata JSON (model config, tokenizer info)"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-adamcodd--vit-base-nsfw-detector__cap_3","uri":"capability://data.processing.analysis.batch.image.processing.with.configurable.preprocessing","name":"batch image processing with configurable preprocessing","description":"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.","intents":["Scan entire image datasets or user uploads for NSFW content in bulk","Generate moderation reports with per-image confidence scores and flagged items","Integrate into data pipeline ETL processes for dataset cleaning","Monitor 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","Researchers evaluating model performance on image corpora"],"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","Preprocessing assumes standard RGB images — handling of edge cases (grayscale, RGBA, corrupted files) requires custom error handling","No caching of preprocessing results — repeated inference on same images recomputes normalization"],"requires":["Python 3.7+","transformers library (pip install transformers)","torch or tensorflow backend","PIL/Pillow for image loading","numpy for array operations","8GB+ RAM for batch processing (32+ images)"],"input_types":["file paths (local or remote URLs)","PIL Image objects","numpy arrays (H×W×3 uint8 or float32)","image bytes/streams","CSV/JSON manifests with image paths"],"output_types":["JSON array with per-image predictions","CSV with image path, label, confidence columns","Pandas DataFrame for analysis","Structured logs with timestamps and metadata"],"categories":["data-processing-analysis","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-adamcodd--vit-base-nsfw-detector__cap_4","uri":"capability://code.generation.editing.fine.tuning.and.transfer.learning.capability","name":"fine-tuning and transfer learning capability","description":"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.","intents":["Adapt the model to domain-specific NSFW definitions (e.g., anime, medical, artistic content)","Improve accuracy on custom datasets with organization-specific content policies","Create specialized detectors for niche platforms or use cases","Reduce 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","Organizations with sufficient GPU resources for fine-tuning (8GB+ VRAM)"],"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 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