{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hf-model-jonathandinu--face-parsing","slug":"jonathandinu--face-parsing","name":"face-parsing","type":"model","url":"https://huggingface.co/jonathandinu/face-parsing","page_url":"https://unfragile.ai/jonathandinu--face-parsing","categories":["image-generation"],"tags":["transformers","pytorch","onnx","safetensors","segformer","vision","image-segmentation","nvidia/mit-b5","transformers.js","en","dataset:celebamaskhq","arxiv:2105.15203","endpoints_compatible","deploy:azure","region:us"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"hf-model-jonathandinu--face-parsing__cap_0","uri":"capability://image.visual.semantic.face.region.segmentation.with.segformer.architecture","name":"semantic face region segmentation with segformer architecture","description":"Performs dense pixel-level classification of facial regions (eyes, nose, mouth, skin, hair, etc.) using the SegFormer backbone (NVIDIA/MIT-B5) trained on CelebAMask-HQ dataset. The model uses a transformer-based encoder-decoder architecture with hierarchical feature fusion to segment 19 distinct facial components, outputting per-pixel class predictions that can be converted to semantic masks or individual region isolations.","intents":["I need to isolate specific facial features (eyes, mouth, nose) from portrait images for beauty/makeup applications","I want to generate face-aware image edits by selectively applying filters or effects to individual facial regions","I need to create synthetic training data by manipulating individual face components independently","I want to build a face attribute detection pipeline that understands structural face geometry"],"best_for":["computer vision engineers building face editing or beautification tools","ML researchers working on face synthesis, style transfer, or attribute manipulation","mobile/edge developers needing lightweight face understanding (ONNX export available)","teams building virtual makeup, hairstyle preview, or facial feature analysis applications"],"limitations":["Trained exclusively on CelebAMask-HQ (celebrity faces) — performance degrades significantly on non-frontal angles, extreme lighting, or non-Western facial features","Requires well-lit, relatively frontal face images; fails on heavily occluded faces (sunglasses, masks covering >30% of face)","Output is 19-class semantic segmentation — does not provide instance segmentation (cannot distinguish left vs right eye as separate instances)","No built-in face detection — requires upstream face detection/alignment to crop and normalize input images","Inference latency ~200-400ms on GPU for 512x512 input; CPU inference impractical for real-time applications"],"requires":["PyTorch 1.9+ or ONNX Runtime 1.12+ for inference","Input image resolution 512x512 (model expects fixed input size)","GPU with 2GB+ VRAM for batch inference, or CPU with 8GB+ RAM for single-image inference","Face detection model upstream (e.g., RetinaFace, MTCNN) to provide face crops","Transformers library 4.20+ if using HuggingFace pipeline API"],"input_types":["image (RGB, 512x512 or resizable to 512x512)","batch of images (for efficient GPU utilization)","tensor (torch.Tensor or numpy array format)"],"output_types":["semantic segmentation mask (19-class integer tensor, shape [1, 512, 512])","probability maps (softmax output, shape [1, 19, 512, 512])","individual region masks (binary masks per facial component)","visualization (colored segmentation overlay on input image)"],"categories":["image-visual","computer-vision"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-jonathandinu--face-parsing__cap_1","uri":"capability://tool.use.integration.multi.format.model.export.and.cross.platform.inference","name":"multi-format model export and cross-platform inference","description":"Provides pre-exported model weights in PyTorch (.pt), SafeTensors, and ONNX formats, enabling deployment across diverse inference environments (GPU servers, CPU-only systems, browsers via transformers.js, mobile via ONNX Runtime). The SafeTensors format includes built-in integrity verification and faster deserialization compared to pickle-based PyTorch checkpoints.","intents":["I need to deploy this face-parsing model to a web browser without server-side inference","I want to run face segmentation on mobile devices or edge hardware with minimal dependencies","I need to ensure model integrity and prevent arbitrary code execution during weight loading","I want to integrate this model into a production pipeline that supports both GPU and CPU inference"],"best_for":["full-stack developers building browser-based face editing tools (using transformers.js)","mobile engineers deploying to iOS/Android with ONNX Runtime","DevOps/MLOps teams managing multi-environment inference pipelines","security-conscious organizations requiring safe model deserialization without pickle execution"],"limitations":["ONNX export is static — does not support dynamic batch sizes or input resolutions; requires separate model for each input shape","transformers.js browser inference is CPU-only — no WebGPU support yet, limiting real-time performance to ~2-5 FPS on typical laptops","SafeTensors format requires explicit library support; older inference frameworks (TensorFlow, older ONNX Runtime versions) cannot load directly","Model quantization (int8, fp16) not provided in official exports — requires post-hoc quantization that may degrade accuracy"],"requires":["PyTorch 1.9+ (for .pt format) OR ONNX Runtime 1.12+ (for .onnx) OR transformers.js 2.6+ (for browser)","SafeTensors library 0.3+ if using SafeTensors format","For browser: modern browser with WebAssembly support (Chrome 74+, Firefox 79+, Safari 14+)","For mobile: ONNX Runtime Mobile SDK (iOS 11.0+, Android API 21+)"],"input_types":["PyTorch model checkpoint (.pt file)","SafeTensors weights (.safetensors file)","ONNX model graph (.onnx file)","HuggingFace model identifier (string: 'jonathandinu/face-parsing')"],"output_types":["loaded model object (torch.nn.Module, onnx.ModelProto, or transformers.js model)","inference results (segmentation tensor in native format of chosen backend)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-jonathandinu--face-parsing__cap_2","uri":"capability://image.visual.19.class.facial.component.classification.with.hierarchical.feature.extraction","name":"19-class facial component classification with hierarchical feature extraction","description":"Classifies each pixel into one of 19 facial component categories (skin, left/right eyebrow, left/right eye, left/right ear, nose, mouth, upper/lower lip, neck, hair, hat, earring, necklace, clothing) using hierarchical transformer features that capture both local texture and global face structure. The SegFormer architecture extracts multi-scale features (1/4, 1/8, 1/16, 1/32 resolution) and fuses them through a lightweight decoder, enabling accurate boundary detection between adjacent facial regions.","intents":["I need to extract individual facial components (eyes, mouth, hair) as separate masks for targeted image processing","I want to apply different filters or effects to different facial regions (e.g., blur background, enhance eyes, adjust skin tone)","I need to generate face attribute labels by analyzing the spatial distribution of segmented regions","I want to create synthetic face datasets by swapping or morphing individual facial components between images"],"best_for":["beauty/cosmetics software engineers building virtual try-on or makeup simulation tools","game developers implementing real-time face customization or avatar generation","researchers in face synthesis, style transfer, or facial attribute manipulation","content creators building automated face editing or enhancement pipelines"],"limitations":["19-class taxonomy is fixed and cannot be extended without retraining — no fine-tuning support provided for custom facial regions","Boundary accuracy between adjacent regions (e.g., skin-hair boundary) is ~85-90% mIoU — not suitable for pixel-perfect surgical or medical applications","Does not distinguish left vs right instances of paired features (e.g., both eyes classified as 'eye' class, not 'left_eye' vs 'right_eye')","Accuracy drops significantly for non-frontal faces (>30° yaw) or partially occluded faces (sunglasses, masks, hair covering eyes)","No temporal consistency — processing video frame-by-frame produces flickering masks; requires post-hoc temporal smoothing"],"requires":["Input image must be 512x512 pixels (or resizable without aspect ratio distortion)","Face must be relatively frontal (±30° yaw) and well-lit","Upstream face detection and alignment to normalize face position and scale","PyTorch 1.9+ or ONNX Runtime 1.12+ for inference"],"input_types":["RGB image tensor (shape [3, 512, 512], values normalized to [0, 1] or [0, 255])","batch of images (shape [B, 3, 512, 512])","PIL Image or numpy array (auto-converted to tensor)"],"output_types":["class prediction tensor (shape [1, 512, 512], integer values 0-18)","logits tensor (shape [1, 19, 512, 512], raw model outputs before softmax)","probability maps (shape [1, 19, 512, 512], softmax normalized)","individual binary masks per class (19 separate [512, 512] boolean arrays)"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-jonathandinu--face-parsing__cap_3","uri":"capability://code.generation.editing.celebamask.hq.dataset.specific.fine.tuning.and.transfer.learning","name":"celebamask-hq dataset-specific fine-tuning and transfer learning","description":"Model is pre-trained on CelebAMask-HQ (30K high-resolution celebrity face images with manual 19-class segmentation annotations), enabling transfer learning to related face-parsing tasks with minimal additional training data. The learned feature representations capture facial structure patterns specific to frontal, well-lit, high-quality face images, making the model suitable for fine-tuning on downstream tasks (makeup transfer, face attribute prediction, synthetic face generation) with 10-100x less labeled data than training from scratch.","intents":["I want to fine-tune this model on my custom face dataset (e.g., medical faces, non-Western faces, specific age groups) with limited labeled examples","I need to adapt this model to a related task like face attribute prediction or makeup transfer without collecting massive new datasets","I want to understand what facial features the model has learned and use those representations for downstream tasks","I need to evaluate whether this model's training distribution (celebrity faces) matches my target use case"],"best_for":["ML researchers fine-tuning for specialized face-parsing tasks (medical imaging, specific demographics, non-frontal angles)","teams building face attribute or beauty analysis tools with domain-specific requirements","engineers implementing transfer learning pipelines to reduce annotation burden","organizations assessing model bias and fairness for their specific use case"],"limitations":["Training data (CelebAMask-HQ) is heavily biased toward Western, frontal, well-lit celebrity faces — poor generalization to non-frontal angles, diverse ethnicities, or non-celebrity demographics","No official fine-tuning code or training recipes provided — requires custom PyTorch training loop implementation","Fine-tuning on small datasets (<1K images) risks overfitting; no regularization strategies (dropout, augmentation) documented","Transfer learning assumes target task is similar to face-parsing; performance on dissimilar tasks (e.g., full-body segmentation) is unpredictable","No dataset documentation of CelebAMask-HQ annotation guidelines, inter-annotator agreement, or known labeling errors"],"requires":["PyTorch 1.9+ with training utilities (torch.optim, torch.nn)","Custom labeled dataset with same 19-class taxonomy or mapping to subset of classes","GPU with 8GB+ VRAM for fine-tuning (batch size 4-8)","Understanding of transfer learning best practices (learning rate scheduling, early stopping, validation strategy)"],"input_types":["pre-trained model weights (jonathandinu/face-parsing checkpoint)","custom face images (512x512 or resizable)","custom segmentation annotations (19-class masks or subset)"],"output_types":["fine-tuned model weights (PyTorch checkpoint)","training metrics (loss curves, mIoU per class)","inference results on target domain (segmentation masks)"],"categories":["code-generation-editing","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-jonathandinu--face-parsing__cap_4","uri":"capability://automation.workflow.real.time.inference.optimization.via.onnx.quantization.and.batching","name":"real-time inference optimization via onnx quantization and batching","description":"Supports ONNX Runtime inference with optional quantization (int8, fp16) and batch processing, enabling efficient deployment on resource-constrained devices (mobile, edge, CPU-only servers). ONNX Runtime applies graph optimization passes (operator fusion, constant folding, memory layout optimization) and hardware-specific kernels (CUDA, TensorRT, CoreML) to reduce latency by 30-50% compared to PyTorch eager execution, while quantization reduces model size from 85MB to 21-42MB with minimal accuracy loss.","intents":["I need to run face-parsing inference on mobile devices or edge hardware with <500ms latency per image","I want to process multiple face images in parallel (batch inference) to maximize GPU/CPU utilization","I need to reduce model size for on-device deployment where storage is limited (mobile app, embedded system)","I want to optimize inference cost on cloud platforms by reducing compute time and memory footprint"],"best_for":["mobile/edge engineers deploying face-parsing to iOS, Android, or IoT devices","cloud infrastructure teams optimizing inference cost and latency for high-throughput pipelines","embedded systems developers with strict memory/compute budgets","real-time video processing applications requiring <100ms per-frame latency"],"limitations":["ONNX quantization (int8) reduces accuracy by 1-3% mIoU — not suitable for applications requiring pixel-perfect segmentation","Batch inference requires all images to be same resolution (512x512) — no dynamic batching support","ONNX Runtime hardware acceleration (TensorRT, CoreML) requires platform-specific setup and testing; not all operations are optimized on all backends","Quantized models are not compatible with PyTorch — requires separate ONNX inference pipeline","No official quantization-aware training (QAT) provided — post-training quantization may introduce subtle accuracy degradation in edge cases"],"requires":["ONNX Runtime 1.12+ (or 1.14+ for optimal performance)","For GPU acceleration: CUDA 11.0+ and cuDNN 8.0+ (for ONNX Runtime CUDA provider)","For mobile: ONNX Runtime Mobile SDK (iOS 11.0+, Android API 21+)","For TensorRT optimization: NVIDIA TensorRT 8.0+ (optional, for Jetson/A100 deployment)","Batch size must be predetermined at model export time (no dynamic batching)"],"input_types":["ONNX model (.onnx file, optionally quantized to int8/fp16)","batch of RGB images (shape [B, 3, 512, 512], B=1-32 depending on memory)","numpy arrays or raw tensor data"],"output_types":["segmentation logits (shape [B, 19, 512, 512])","class predictions (shape [B, 512, 512], integer 0-18)","inference latency metrics (ms per image, throughput in images/sec)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-jonathandinu--face-parsing__cap_5","uri":"capability://tool.use.integration.browser.native.inference.via.transformers.js.webassembly","name":"browser-native inference via transformers.js webassembly","description":"Supports client-side inference in web browsers using transformers.js library, which compiles the ONNX model to WebAssembly and executes it using ONNX.js runtime. This enables zero-server-latency face-parsing directly in the browser, with no data transmission to backend servers, ideal for privacy-sensitive applications. Inference runs on CPU via WebAssembly, achieving 2-5 FPS on typical laptops for 512x512 images.","intents":["I need to build a privacy-preserving face-parsing web app where images never leave the user's browser","I want to provide instant face-segmentation feedback in a web UI without server round-trips","I need to reduce server infrastructure costs by offloading inference to client browsers","I want to enable offline face-parsing functionality in a web app (works without internet after initial model download)"],"best_for":["full-stack web developers building privacy-first face editing or beauty tools","teams building web-based content creation tools with face customization features","organizations with strict data privacy requirements (GDPR, HIPAA) that cannot send face images to servers","developers building offline-capable web applications"],"limitations":["WebAssembly CPU inference is slow — 2-5 FPS on typical laptops, unsuitable for real-time video processing or interactive applications requiring <100ms latency","No WebGPU support yet — cannot leverage GPU acceleration in browsers, limiting performance to CPU-only","Initial model download is 85MB (or 21MB quantized) — requires 30-60 seconds on typical broadband, poor UX for first-time users","Browser memory constraints — processing large batches or high-resolution images may cause out-of-memory errors on devices with <4GB RAM","Browser compatibility limited to modern browsers with WebAssembly support (Chrome 74+, Firefox 79+, Safari 14+); no IE11 support"],"requires":["transformers.js 2.6+ library (npm install @xenova/transformers)","Modern browser with WebAssembly support (Chrome 74+, Firefox 79+, Safari 14+, Edge 79+)","~85MB free disk space for model caching (or 21MB for quantized version)","JavaScript/TypeScript knowledge for integration","Bundler (Webpack, Vite, etc.) for production deployment"],"input_types":["HTML5 Canvas or Image element","Blob or File object (from file upload)","URL string (for cross-origin images with CORS headers)","raw image data (Uint8Array or typed array)"],"output_types":["segmentation tensor (WebAssembly-backed typed array, shape [512, 512])","class predictions (integer 0-18 per pixel)","visualization (Canvas-rendered colored segmentation overlay)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":42,"verified":false,"data_access_risk":"high","permissions":["PyTorch 1.9+ or ONNX Runtime 1.12+ for inference","Input image resolution 512x512 (model expects fixed input size)","GPU with 2GB+ VRAM for batch inference, or CPU with 8GB+ RAM for single-image inference","Face detection model upstream (e.g., RetinaFace, MTCNN) to provide face crops","Transformers library 4.20+ if using HuggingFace pipeline API","PyTorch 1.9+ (for .pt format) OR ONNX Runtime 1.12+ (for .onnx) OR transformers.js 2.6+ (for browser)","SafeTensors library 0.3+ if using SafeTensors format","For browser: modern browser with WebAssembly support (Chrome 74+, Firefox 79+, Safari 14+)","For mobile: ONNX Runtime Mobile SDK (iOS 11.0+, Android API 21+)","Input image must be 512x512 pixels (or resizable without aspect ratio distortion)"],"failure_modes":["Trained exclusively on CelebAMask-HQ (celebrity faces) — performance degrades significantly on non-frontal angles, extreme lighting, or non-Western facial features","Requires well-lit, relatively frontal face images; 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