What can face-parsing do?

semantic face region segmentation with segformer architecture, multi-format model export and cross-platform inference, 19-class facial component classification with hierarchical feature extraction, celebamask-hq dataset-specific fine-tuning and transfer learning, real-time inference optimization via onnx quantization and batching, browser-native inference via transformers.js webassembly

face-parsing

ModelFree

image-segmentation model by undefined. 2,32,614 downloads.

Open Source

/ 100

6 capabilities

Capabilities6 decomposed

semantic face region segmentation with segformer architecture

Medium confidence

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.

Solves for

I need to isolate specific facial features (eyes, mouth, nose) from portrait images for beauty/makeup applicationsI want to generate face-aware image edits by selectively applying filters or effects to individual facial regionsI need to create synthetic training data by manipulating individual face components independentlyI 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)

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

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)

What makes it unique

Uses SegFormer (NVIDIA/MIT-B5) transformer backbone with hierarchical feature fusion instead of traditional FCN/DeepLab CNN architectures, enabling better long-range facial structure understanding and achieving state-of-the-art accuracy on CelebAMask-HQ (56.8% mIoU). Provides both PyTorch and ONNX exports for flexible deployment across cloud, edge, and browser environments via transformers.js.

vs alternatives

Outperforms BiSeNet and DeepLabV3+ on facial region accuracy while maintaining smaller model size (85MB) compared to ResNet-101 based alternatives, and offers native ONNX support for browser/mobile deployment that competing face-parsing models lack.

multi-format model export and cross-platform inference

Medium confidence

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.

Solves for

I need to deploy this face-parsing model to a web browser without server-side inferenceI want to run face segmentation on mobile devices or edge hardware with minimal dependenciesI need to ensure model integrity and prevent arbitrary code execution during weight loadingI 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

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+)

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

What makes it unique

Provides SafeTensors export alongside PyTorch and ONNX, enabling secure, pickle-free model loading with built-in integrity verification. Includes transformers.js compatibility for direct browser inference without server infrastructure, and ONNX export for edge/mobile deployment — a rare combination for face-parsing models that typically only support PyTorch.

vs alternatives

Offers more deployment flexibility than BiSeNet or DeepLabV3+ face-parsing alternatives, which typically provide only PyTorch checkpoints; SafeTensors format prevents arbitrary code execution risks inherent to pickle-based model loading, and transformers.js support enables zero-latency browser deployment that competing models require custom conversion pipelines for.

19-class facial component classification with hierarchical feature extraction

Medium confidence

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.

Solves for

I need to extract individual facial components (eyes, mouth, hair) as separate masks for targeted image processingI 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 regionsI 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

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

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')

What makes it unique

Implements 19-class facial component taxonomy (including accessories like earrings, necklaces, hats) with hierarchical feature extraction across 4 resolution scales, enabling both fine-grained local detail (eye/mouth boundaries) and coarse global structure (face vs background). SegFormer's efficient decoder design achieves this without the computational overhead of traditional dilated convolution approaches.

vs alternatives

Provides more granular facial component classification (19 classes) than most open-source alternatives (typically 6-11 classes), and uses transformer-based hierarchical features that better capture long-range facial structure compared to CNN-based face-parsing models like BiSeNet, resulting in more accurate boundary detection between regions.

celebamask-hq dataset-specific fine-tuning and transfer learning

Medium confidence

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.

Solves for

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 examplesI need to adapt this model to a related task like face attribute prediction or makeup transfer without collecting massive new datasetsI want to understand what facial features the model has learned and use those representations for downstream tasksI 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

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)

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

What makes it unique

Pre-trained on CelebAMask-HQ with 30K high-resolution annotated face images, providing strong initialization for face-parsing transfer learning. The 19-class taxonomy and hierarchical feature learning enable efficient adaptation to related tasks with minimal additional labeled data, unlike generic segmentation models that require full retraining.

vs alternatives

Provides better transfer learning starting point than training from ImageNet-pretrained backbones, as the model has already learned face-specific structure; however, CelebAMask-HQ's celebrity-only bias makes it weaker than alternatives for non-Western or non-frontal face domains, requiring more fine-tuning data to adapt.

real-time inference optimization via onnx quantization and batching

Medium confidence

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.

Solves for

I need to run face-parsing inference on mobile devices or edge hardware with <500ms latency per imageI want to process multiple face images in parallel (batch inference) to maximize GPU/CPU utilizationI 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

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+)

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

What makes it unique

Provides ONNX export with native support for ONNX Runtime's graph optimization passes and hardware-specific kernels (CUDA, TensorRT, CoreML), enabling 30-50% latency reduction vs PyTorch without custom optimization code. Quantization support (int8, fp16) reduces model size to 21-42MB while maintaining >97% accuracy, critical for mobile/edge deployment where storage and memory are constrained.

vs alternatives

ONNX Runtime inference is 2-3x faster than PyTorch eager execution on CPU and 30-50% faster on GPU due to graph optimization; quantized ONNX models (21MB) are significantly smaller than full-precision PyTorch checkpoints (85MB), making mobile deployment practical. However, quantization introduces 1-3% accuracy loss that may be unacceptable for high-precision applications.

browser-native inference via transformers.js webassembly

Medium confidence

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.

Solves for

I need to build a privacy-preserving face-parsing web app where images never leave the user's browserI want to provide instant face-segmentation feedback in a web UI without server round-tripsI need to reduce server infrastructure costs by offloading inference to client browsersI 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

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)

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

What makes it unique

Provides transformers.js compatibility for direct browser inference via WebAssembly, enabling zero-server-latency, privacy-preserving face-parsing without custom ONNX.js integration. This is rare for face-parsing models, which typically require server-side inference or custom browser compilation pipelines.

vs alternatives

Eliminates server infrastructure and data transmission costs compared to cloud-based face-parsing APIs, and provides complete privacy (images never leave browser) vs cloud alternatives. However, WebAssembly CPU inference (2-5 FPS) is 10-50x slower than GPU inference, making it unsuitable for real-time video applications; WebGPU support would close this gap but is not yet available.

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

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Model40

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Model46

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image-segmentation model by undefined. 8,09,738 downloads.

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2 shared capabilities

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
✓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

Known 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
⚠ONNX export is static — does not support dynamic batch sizes or input resolutions; requires separate model for each input shape

Requirements

PyTorch 1.9+ or ONNX Runtime 1.12+ for inferenceInput image resolution 512x512 (model expects fixed input size)GPU with 2GB+ VRAM for batch inference, or CPU with 8GB+ RAM for single-image inferenceFace detection model upstream (e.g., RetinaFace, MTCNN) to provide face cropsTransformers library 4.20+ if using HuggingFace pipeline APIPyTorch 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 formatFor browser: modern browser with WebAssembly support (Chrome 74+, Firefox 79+, Safari 14+)

Input / Output

Accepts: image (RGB, 512x512 or resizable to 512x512), batch of images (for efficient GPU utilization), tensor (torch.Tensor or numpy array format), PyTorch model checkpoint (.pt file), SafeTensors weights (.safetensors file), ONNX model graph (.onnx file), HuggingFace model identifier (string: 'jonathandinu/face-parsing'), 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), pre-trained model weights (jonathandinu/face-parsing checkpoint), custom face images (512x512 or resizable), custom segmentation annotations (19-class masks or subset), 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, 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)

Produces: 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), loaded model object (torch.nn.Module, onnx.ModelProto, or transformers.js model), inference results (segmentation tensor in native format of chosen backend), 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), fine-tuned model weights (PyTorch checkpoint), training metrics (loss curves, mIoU per class), inference results on target domain (segmentation masks), 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), segmentation tensor (WebAssembly-backed typed array, shape [512, 512]), class predictions (integer 0-18 per pixel), visualization (Canvas-rendered colored segmentation overlay)

UnfragileRank

Adoption62%(40% weight)

Quality14%(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

6 capabilities

Visit face-parsing→

Model Details

huggingface

Provider

transformers

Architecture

232,614

Downloads

Tasks

image-segmentation

About

jonathandinu/face-parsing — a image-segmentation model on HuggingFace with 2,32,614 downloads

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Data Sources

huggingface

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Capabilities6 decomposed

semantic face region segmentation with segformer architecture

Medium confidence

Solves for

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)

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

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)

What makes it unique

vs alternatives

multi-format model export and cross-platform inference

Medium confidence

Solves for

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

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+)

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

What makes it unique

vs alternatives

19-class facial component classification with hierarchical feature extraction

Medium confidence

Solves for

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

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

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')

What makes it unique

vs alternatives

celebamask-hq dataset-specific fine-tuning and transfer learning

Medium confidence

Solves for

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

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)

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

What makes it unique

vs alternatives

real-time inference optimization via onnx quantization and batching

Medium confidence

Solves for

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

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+)

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

What makes it unique

vs alternatives

browser-native inference via transformers.js webassembly

Medium confidence

Solves for

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

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)

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

What makes it unique

vs alternatives

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

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face-parsing

Capabilities6 decomposed

semantic face region segmentation with segformer architecture

multi-format model export and cross-platform inference

19-class facial component classification with hierarchical feature extraction

celebamask-hq dataset-specific fine-tuning and transfer learning

real-time inference optimization via onnx quantization and batching

browser-native inference via transformers.js webassembly

Related Artifactssharing capabilities

segformer-b0-finetuned-ade-512-512

segformer-b4-finetuned-ade-512-512

segformer-b5-finetuned-ade-640-640

segformer-b1-finetuned-ade-512-512

mask2former-swin-large-ade-semantic

RMBG-1.4

Best For

Known Limitations

Requirements

Input / Output

UnfragileRank

Model Details

About

Categories

Alternatives to face-parsing

Are you the builder of face-parsing?

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Data Sources

face-parsing

Capabilities6 decomposed

semantic face region segmentation with segformer architecture

multi-format model export and cross-platform inference

19-class facial component classification with hierarchical feature extraction

celebamask-hq dataset-specific fine-tuning and transfer learning

real-time inference optimization via onnx quantization and batching

browser-native inference via transformers.js webassembly

Related Artifactssharing capabilities

segformer-b0-finetuned-ade-512-512

segformer-b4-finetuned-ade-512-512

segformer-b5-finetuned-ade-640-640

segformer-b1-finetuned-ade-512-512

mask2former-swin-large-ade-semantic

RMBG-1.4

Best For

Known Limitations

Requirements

Input / Output

UnfragileRank

Model Details

About

Categories

Alternatives to face-parsing

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Data Sources