{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hf-model-mattmdjaga--segformer_b2_clothes","slug":"mattmdjaga--segformer_b2_clothes","name":"segformer_b2_clothes","type":"model","url":"https://huggingface.co/mattmdjaga/segformer_b2_clothes","page_url":"https://unfragile.ai/mattmdjaga--segformer_b2_clothes","categories":["image-generation"],"tags":["transformers","pytorch","onnx","safetensors","segformer","vision","image-segmentation","dataset:mattmdjaga/human_parsing_dataset","arxiv:2105.15203","license:other","endpoints_compatible","region:us"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"hf-model-mattmdjaga--segformer_b2_clothes__cap_0","uri":"capability://image.visual.semantic.segmentation.for.clothing.items","name":"semantic-segmentation-for-clothing-items","description":"Performs pixel-level semantic segmentation on images to identify and isolate clothing items and body parts using a SegFormer B2 transformer backbone. The model uses hierarchical vision transformer blocks with efficient self-attention mechanisms to encode multi-scale spatial features, then applies a lightweight segmentation head to produce dense per-pixel class predictions. Trained on the mattmdjaga/human_parsing_dataset with 59 clothing and body part categories, enabling fine-grained clothing detection and localization in diverse poses and lighting conditions.","intents":["I need to automatically detect and isolate individual clothing items from photos for e-commerce product extraction","I want to build a virtual try-on system that needs precise clothing boundaries and segmentation masks","I need to analyze fashion datasets by automatically parsing which clothing items are present in each image","I'm building a clothing recommendation engine that requires understanding what garments a person is wearing"],"best_for":["fashion tech companies building virtual try-on or clothing detection systems","e-commerce platforms automating product image processing and categorization","researchers in computer vision and human parsing working with clothing datasets","developers building style transfer or outfit recommendation applications"],"limitations":["Model trained specifically on human clothing parsing — may not generalize well to clothing on mannequins, hangers, or non-human contexts","Inference latency ~200-400ms per image on GPU (varies by image resolution and hardware); CPU inference significantly slower","Limited to 59 predefined clothing/body part classes — cannot segment novel or unlabeled clothing types","Performance degrades on heavily occluded clothing, extreme poses, or images with multiple overlapping people","Requires GPU memory ~2-4GB for batch processing; batch inference on CPU impractical for production"],"requires":["PyTorch 1.9+ or ONNX Runtime 1.10+ for inference","transformers library 4.20+ for model loading and preprocessing","Python 3.7+","GPU with CUDA 11.0+ recommended (NVIDIA A100/V100/RTX series); CPU inference possible but slow","Image input resolution typically 512x512 or 1024x1024 (configurable)"],"input_types":["image/jpeg","image/png","image/webp","numpy array (H×W×3 RGB format)","PIL Image objects"],"output_types":["segmentation mask (H×W integer tensor with class indices 0-58)","confidence scores per class (optional, from logits)","ONNX-compatible tensor output for edge deployment"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-mattmdjaga--segformer_b2_clothes__cap_1","uri":"capability://tool.use.integration.multi.format.model.export.and.inference","name":"multi-format-model-export-and-inference","description":"Provides model weights in multiple serialization formats (PyTorch .pt, ONNX, safetensors) enabling deployment across heterogeneous inference environments without retraining. The model can be loaded via Hugging Face transformers library, converted to ONNX for cross-platform compatibility, or loaded from safetensors format for faster deserialization and improved security. This multi-format approach allows developers to choose inference backends (PyTorch, ONNX Runtime, TensorRT, CoreML) based on deployment target (cloud, edge, mobile, browser).","intents":["I need to deploy this model to production with ONNX Runtime for faster inference and better hardware compatibility","I want to run the model on edge devices or mobile without PyTorch dependencies","I need to load the model quickly in a serverless function with minimal cold-start latency","I'm building a cross-platform application and need the same model weights to work on CPU, GPU, and TPU"],"best_for":["ML engineers deploying models to production with strict latency/resource constraints","developers building edge AI applications on mobile, IoT, or embedded devices","teams managing multi-cloud or hybrid inference infrastructure","researchers needing reproducible model weights with security-first serialization"],"limitations":["ONNX export may lose some PyTorch-specific optimizations or custom operations; requires validation of output equivalence","Safetensors format is read-only after export — cannot fine-tune directly from safetensors without converting back to PyTorch","ONNX Runtime performance varies significantly by hardware backend (CPU vs CUDA vs TensorRT); requires per-target optimization","Model size ~100-150MB depending on format; requires adequate storage and bandwidth for download in resource-constrained environments"],"requires":["transformers library 4.20+ for PyTorch loading","ONNX Runtime 1.10+ for ONNX inference (optional)","safetensors library 0.3+ for safetensors format (optional)","Python 3.7+","For ONNX conversion: onnx 1.12+, onnxruntime 1.10+"],"input_types":["Hugging Face model identifier (mattmdjaga/segformer_b2_clothes)","local file path to .pt, .onnx, or .safetensors weights","model configuration JSON"],"output_types":["PyTorch model object (torch.nn.Module)","ONNX graph (protobuf format)","safetensors binary format","inference output tensors (format-agnostic)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-mattmdjaga--segformer_b2_clothes__cap_2","uri":"capability://tool.use.integration.huggingface.hub.integrated.model.loading","name":"huggingface-hub-integrated-model-loading","description":"Integrates with Hugging Face Hub infrastructure for one-command model discovery, downloading, and caching via the transformers library. The model is automatically downloaded from CDN, cached locally with integrity verification, and loaded with automatic configuration inference from model card metadata. Supports lazy loading, streaming downloads for large models, and automatic GPU/CPU device placement without explicit device management code.","intents":["I want to load a pre-trained clothing segmentation model with a single line of code without managing downloads or configs","I need to ensure my model is always up-to-date with the latest weights from the Hub without manual version management","I'm building a prototype and want to avoid downloading multi-GB models repeatedly across development machines","I need to integrate this model into a Hugging Face Spaces app or inference endpoint with zero custom deployment code"],"best_for":["rapid prototyping and research workflows where setup time matters","teams using Hugging Face ecosystem (Spaces, Inference API, AutoTrain)","developers building applications with minimal DevOps overhead","researchers sharing reproducible code that others can run immediately"],"limitations":["Requires internet connectivity for initial model download; no offline-first workflow without pre-caching","Hub CDN latency varies by region; first download can take 30-120 seconds depending on model size and network","Cache directory grows unbounded by default (~100-150MB per model); requires manual cleanup or environment variable configuration","Automatic device placement may not be optimal for multi-GPU setups or custom device strategies","Hub availability is a dependency — service outages block model loading unless cached locally"],"requires":["transformers library 4.20+","Python 3.7+","Internet connectivity (for initial download)","~200MB free disk space for model cache","Hugging Face account (optional, for private models)"],"input_types":["model identifier string (mattmdjaga/segformer_b2_clothes)","optional revision/branch name (main, v1.0, etc.)","optional device specification (cuda, cpu, auto)"],"output_types":["AutoImageProcessingConfig object","SegFormerForSemanticSegmentation model instance","cached model weights on disk"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-mattmdjaga--segformer_b2_clothes__cap_3","uri":"capability://image.visual.batch.image.segmentation.with.variable.resolution","name":"batch-image-segmentation-with-variable-resolution","description":"Processes multiple images in batches with automatic padding and resizing to handle variable input dimensions without manual preprocessing. The model accepts images of different sizes, automatically pads them to a common resolution within a batch, and produces segmentation masks that are post-processed back to original image dimensions. Supports configurable batch sizes and resolution targets (512x512, 1024x1024, etc.) to balance memory usage and inference quality.","intents":["I need to segment a dataset of 10,000 images with varying resolutions efficiently without writing custom batching logic","I want to process images from a video stream where frame sizes may vary slightly due to encoding","I'm building an API that accepts images of arbitrary dimensions and needs to return segmentation masks in the same dimensions","I need to maximize GPU utilization by batching images together while respecting memory constraints"],"best_for":["batch processing pipelines for large image datasets","production APIs handling heterogeneous image inputs","video processing applications with frame-by-frame segmentation","data annotation and labeling workflows"],"limitations":["Padding to common resolution adds computational overhead (~5-15% depending on aspect ratio variance); highly non-square images are inefficient","Batch processing requires all images to fit in GPU memory simultaneously; very large images or large batches may cause OOM errors","Post-processing to restore original dimensions adds ~20-50ms per batch; not suitable for real-time streaming at 30+ FPS","Memory usage scales quadratically with image resolution; 1024x1024 batch of 8 images requires ~4-6GB VRAM"],"requires":["PyTorch 1.9+ with CUDA support (for GPU batching)","transformers library 4.20+","GPU with 4GB+ VRAM for batch size > 2 at 1024x1024 resolution","Python 3.7+"],"input_types":["list of PIL Image objects","list of numpy arrays (H×W×3 RGB)","list of file paths (jpg, png, webp)","torch.Tensor batch (B×3×H×W)"],"output_types":["list of segmentation masks (H×W integer tensors, original dimensions)","list of confidence scores per class (optional)","batch tensor output (B×H×W×num_classes)"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-mattmdjaga--segformer_b2_clothes__cap_4","uri":"capability://image.visual.class.wise.segmentation.confidence.scoring","name":"class-wise-segmentation-confidence-scoring","description":"Produces per-pixel probability distributions across all 59 clothing/body part classes, enabling confidence-based filtering and uncertainty quantification. The model outputs logits that can be converted to softmax probabilities, allowing downstream applications to filter low-confidence predictions, identify ambiguous regions, or weight predictions by confidence. Supports both hard predictions (argmax class per pixel) and soft predictions (full probability distributions) for different use cases.","intents":["I need to identify uncertain regions in segmentation masks where the model is not confident, for manual review or active learning","I want to filter out low-confidence clothing predictions to reduce false positives in my e-commerce pipeline","I'm building a confidence-aware visualization that shows which clothing items the model is uncertain about","I need to implement uncertainty sampling for active learning to improve the model with human annotations"],"best_for":["quality assurance and confidence-based filtering in production pipelines","active learning and data annotation workflows","uncertainty quantification for safety-critical applications","confidence-aware visualizations and user interfaces"],"limitations":["Softmax probabilities are calibrated only for the training distribution; confidence may not reflect true accuracy on out-of-distribution images","Computing full probability distributions adds ~10-20% inference overhead vs hard predictions; not suitable for ultra-low-latency applications","Confidence scores are per-pixel; no global image-level confidence metric without aggregation","Model may be overconfident on ambiguous clothing types (e.g., jackets vs coats) due to training data imbalance"],"requires":["PyTorch 1.9+ or ONNX Runtime 1.10+","transformers library 4.20+","Python 3.7+","Optional: scipy or numpy for softmax computation"],"input_types":["model logits output (B×H×W×59 tensor)","raw model predictions"],"output_types":["softmax probabilities (B×H×W×59, values 0-1)","per-pixel confidence scores (B×H×W, max probability)","per-pixel entropy (B×H×W, uncertainty measure)","hard predictions with confidence (B×H×W class indices + B×H×W confidence)"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-mattmdjaga--segformer_b2_clothes__cap_5","uri":"capability://image.visual.fine.grained.clothing.category.classification","name":"fine-grained-clothing-category-classification","description":"Segments images into 59 distinct clothing and body part categories (e.g., shirt, pants, jacket, hat, shoes, skin, hair) rather than generic foreground/background or person/clothing binary splits. Each pixel is assigned to one of 59 classes with semantic meaning, enabling downstream applications to understand specific garment types and body regions. The granular taxonomy supports fashion-specific use cases like outfit composition analysis, clothing type detection, and body part localization.","intents":["I need to identify specific clothing types (e.g., distinguish between shirt, jacket, and coat) in fashion images","I want to analyze outfit composition by detecting which clothing items are present and their spatial relationships","I'm building a virtual try-on system that needs to understand which body parts are visible and which are occluded by clothing","I need to extract clothing-specific features for a recommendation engine that suggests compatible garments"],"best_for":["fashion tech and e-commerce applications requiring clothing type understanding","outfit recommendation and style analysis systems","virtual try-on and augmented reality applications","fashion dataset annotation and analysis"],"limitations":["59-class taxonomy is fixed and cannot be extended without retraining; novel clothing types not in training data will be misclassified","Class imbalance in training data may cause poor performance on rare clothing items (e.g., specific accessories)","Clothing categories are mutually exclusive per pixel; cannot represent layered clothing (e.g., shirt under jacket) without post-processing","Performance varies significantly across clothing types; common items (shirt, pants) have higher accuracy than rare items (specific accessories)"],"requires":["PyTorch 1.9+ or ONNX Runtime 1.10+","transformers library 4.20+","Python 3.7+","Mapping from class indices to clothing labels (provided in model card)"],"input_types":["image/jpeg, image/png, image/webp","numpy array (H×W×3 RGB)","PIL Image objects"],"output_types":["segmentation mask with 59 class indices (H×W integer tensor)","class label strings (e.g., 'shirt', 'pants', 'shoes')","per-class pixel counts (histogram of clothing types)"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":42,"verified":false,"data_access_risk":"high","permissions":["PyTorch 1.9+ or ONNX Runtime 1.10+ for inference","transformers library 4.20+ for model loading and preprocessing","Python 3.7+","GPU with CUDA 11.0+ recommended (NVIDIA A100/V100/RTX series); 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requires validation of output equivalence","Safetensors format is read-only after export — cannot fine-tune directly from safetensors without converting back to PyTorch","ONNX Runtime performance varies significantly by hardware backend (CPU vs CUDA vs TensorRT); requires per-target optimization","Model size ~100-150MB depending on format; requires adequate storage and bandwidth for download in resource-constrained environments","Requires internet connectivity for initial model download; no offline-first workflow without pre-caching","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.6169067897679127,"quality":0.22,"ecosystem":0.5000000000000001,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.35,"quality":0.2,"ecosystem":0.1,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:22.765Z","last_scraped_at":"2026-05-03T14:23:00.162Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":170192,"model_likes":499}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=mattmdjaga--segformer_b2_clothes","compare_url":"https://unfragile.ai/compare?artifact=mattmdjaga--segformer_b2_clothes"}},"signature":"FjlM0ip1qQxdRz6qDQ59o/BAQDJAstspExb2dwdT74KmKdDN/gkrLUeUZLBt/GHimgpCp7UppwYvROMbSPRkAQ==","signedAt":"2026-06-20T04:01:08.522Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/mattmdjaga--segformer_b2_clothes","artifact":"https://unfragile.ai/mattmdjaga--segformer_b2_clothes","verify":"https://unfragile.ai/api/v1/verify?slug=mattmdjaga--segformer_b2_clothes","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}