{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hf-model-valentinafevu--yolos-fashionpedia","slug":"valentinafevu--yolos-fashionpedia","name":"yolos-fashionpedia","type":"model","url":"https://huggingface.co/valentinafevu/yolos-fashionpedia","page_url":"https://unfragile.ai/valentinafevu--yolos-fashionpedia","categories":["image-generation"],"tags":["transformers","pytorch","yolos","object-detection","YOLOS","Object detection","en","dataset:detection-datasets/fashionpedia","license:mit","endpoints_compatible","deploy:azure","region:us"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"hf-model-valentinafevu--yolos-fashionpedia__cap_0","uri":"capability://image.visual.fashion.item.object.detection.with.vision.transformer.backbone","name":"fashion-item object detection with vision transformer backbone","description":"Detects and localizes fashion items in images using YOLOS (You Only Look at Sequences), a vision transformer-based object detection architecture that treats image patches as sequences rather than using convolutional feature pyramids. The model is fine-tuned on the Fashionpedia dataset containing 46k+ annotated fashion product images across 27 clothing categories, enabling detection of apparel, accessories, and footwear with bounding box coordinates and class labels.","intents":["I need to automatically identify and locate clothing items in product photos for e-commerce catalog organization","I want to detect fashion items in user-uploaded images for a style recommendation system","I need to extract bounding boxes of garments from fashion runway or street-style photos for analysis","I want to build a fashion inventory management system that recognizes items in warehouse photos"],"best_for":["e-commerce platforms building automated product tagging and categorization pipelines","fashion tech startups developing style recommendation or virtual try-on systems","content creators and fashion brands analyzing fashion item presence in media","researchers studying fashion datasets and clothing detection benchmarks"],"limitations":["Optimized for fashion items specifically — performance degrades on non-apparel objects in images","Requires clear, well-lit images; struggles with extreme occlusion, motion blur, or heavily stylized artwork","Vision transformer architecture has higher computational cost (~2-3x slower inference than lightweight CNNs) and requires more GPU memory","Limited to 27 fashion categories from Fashionpedia — may not detect niche or emerging fashion items outside training distribution","No temporal consistency across video frames — each frame processed independently without motion tracking"],"requires":["PyTorch 1.9+","Transformers library 4.20+","Python 3.7+","GPU with 4GB+ VRAM for batch inference (CPU inference possible but slow)","PIL/Pillow for image preprocessing"],"input_types":["image/jpeg","image/png","image/webp","numpy arrays (H, W, 3)","PIL Image objects"],"output_types":["structured data: list of detections with {class_id, class_name, confidence_score, bbox_coordinates}","bounding box format: [x_min, y_min, x_max, y_max] or [x_center, y_center, width, height]"],"categories":["image-visual","object-detection"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-valentinafevu--yolos-fashionpedia__cap_1","uri":"capability://image.visual.multi.category.fashion.item.classification.with.confidence.scoring","name":"multi-category fashion item classification with confidence scoring","description":"Classifies detected fashion items into one of 27 predefined categories (e.g., shirt, pants, dress, jacket, shoes, accessories) with per-detection confidence scores indicating model certainty. The classification head is integrated into the YOLOS detection pipeline, outputting both bounding box predictions and category logits for each detected object in a single forward pass.","intents":["I need to automatically tag fashion items by type (e.g., 'shirt', 'pants', 'shoes') for inventory management","I want to filter detections by confidence threshold to reduce false positives in production systems","I need to understand which fashion categories the model is most/least confident about for a given image","I want to build a confidence-based filtering pipeline that only processes high-confidence detections downstream"],"best_for":["e-commerce backends automating product categorization with quality control thresholds","fashion analytics platforms analyzing item distribution and trends across image datasets","quality assurance teams validating detection accuracy before deploying to production"],"limitations":["Classification is limited to 27 Fashionpedia categories — cannot detect items outside this taxonomy","Confidence scores are model calibration-dependent; raw softmax outputs may not reflect true probability of correctness","No hierarchical classification — cannot distinguish between sub-categories (e.g., 'long-sleeve shirt' vs 'short-sleeve shirt')","Confidence scores can be overconfident on out-of-distribution images, requiring post-hoc calibration for production use"],"requires":["PyTorch 1.9+","Transformers library 4.20+","Knowledge of the 27 Fashionpedia category labels for interpretation"],"input_types":["image/jpeg","image/png","PIL Image objects"],"output_types":["structured data: {class_id: int, class_name: str, confidence: float (0-1)}","logits: raw model outputs before softmax for custom post-processing"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-valentinafevu--yolos-fashionpedia__cap_2","uri":"capability://data.processing.analysis.batch.image.processing.with.configurable.inference.parameters","name":"batch image processing with configurable inference parameters","description":"Processes multiple images in batches through the YOLOS model with configurable inference parameters including confidence thresholds, NMS (non-maximum suppression) IoU thresholds, and maximum detections per image. Leverages PyTorch's batch processing and GPU acceleration to parallelize inference across images, with support for variable image sizes through dynamic padding or resizing.","intents":["I need to process thousands of product images efficiently for a catalog ingestion pipeline","I want to tune detection sensitivity (confidence threshold) and NMS aggressiveness for my specific use case","I need to limit the number of detections per image to control downstream processing cost","I want to process images of varying sizes without manual preprocessing"],"best_for":["data engineering teams building ETL pipelines for large-scale image datasets","production systems requiring configurable detection sensitivity for different domains","batch processing jobs running on cloud infrastructure with GPU acceleration"],"limitations":["Batch size is limited by GPU memory — typical batch size 8-32 depending on image resolution and GPU VRAM","Variable image sizes in a batch require padding/resizing, which can introduce artifacts or reduce detection accuracy on very small/large images","NMS is applied per-image independently — no cross-image deduplication for overlapping detections across image boundaries","No built-in distributed inference — requires external frameworks (Ray, Dask) for multi-GPU or multi-machine scaling"],"requires":["PyTorch 1.9+","GPU with 4GB+ VRAM for reasonable batch sizes","Transformers library 4.20+","Optional: Ray or Dask for distributed batch processing"],"input_types":["list of PIL Image objects","list of numpy arrays","list of image file paths"],"output_types":["list of detection results per image: [{class_id, class_name, confidence, bbox}, ...]","structured data: batch-level metadata including processing time, memory usage"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-valentinafevu--yolos-fashionpedia__cap_3","uri":"capability://data.processing.analysis.bounding.box.coordinate.output.with.multiple.format.support","name":"bounding box coordinate output with multiple format support","description":"Outputs detected object bounding boxes in multiple coordinate formats (xyxy, xywh, normalized, pixel coordinates) with flexible serialization to JSON, COCO format, or custom formats. The model natively outputs normalized coordinates [0-1] which are converted to pixel coordinates based on input image dimensions, enabling seamless integration with downstream annotation tools and visualization libraries.","intents":["I need bounding box coordinates in COCO format for dataset annotation and benchmarking","I want to visualize detections on images using matplotlib or OpenCV with pixel coordinates","I need to convert detections to VOC XML format for training other models","I want to store detections in a standardized format for data versioning and comparison"],"best_for":["computer vision engineers integrating detections with existing annotation pipelines","researchers comparing detection results across different models using standard formats","data scientists building training datasets from model predictions"],"limitations":["Coordinate format conversion requires manual implementation — no built-in converters for all formats","Normalized coordinates lose precision when converted to pixel coordinates for very small images (<100px)","No automatic coordinate transformation for rotated bounding boxes — only axis-aligned rectangles supported","Coordinate precision depends on model output quantization and floating-point rounding"],"requires":["Knowledge of target coordinate format (xyxy, xywh, normalized, etc.)","Image dimensions for converting normalized to pixel coordinates"],"input_types":["model output: normalized bounding box coordinates"],"output_types":["xyxy format: [x_min, y_min, x_max, y_max]","xywh format: [x_center, y_center, width, height]","COCO format: {image_id, category_id, bbox, area, iscrowd}","JSON: serializable detection dictionaries"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-valentinafevu--yolos-fashionpedia__cap_4","uri":"capability://tool.use.integration.huggingface.hub.integration.with.one.line.model.loading","name":"huggingface hub integration with one-line model loading","description":"Integrates with HuggingFace Hub for model distribution, versioning, and one-line loading via the transformers library's AutoModel API. The model is versioned on Hub with model card documentation, inference examples, and automatic compatibility checks. Users load the model with a single line of code: `AutoModelForObjectDetection.from_pretrained('valentinafevu/yolos-fashionpedia')`, which handles downloading, caching, and device placement.","intents":["I want to quickly prototype a fashion detection system without downloading model files manually","I need to ensure I'm using the latest model version with automatic updates from Hub","I want to compare this model with other fashion detection models available on Hub","I need to deploy this model to production with version pinning and reproducibility"],"best_for":["researchers and practitioners prototyping quickly with minimal setup","teams deploying models to production with version control and reproducibility","developers integrating multiple HuggingFace models into a single pipeline"],"limitations":["Requires internet connection for initial model download (unless cached locally)","Model caching directory can grow large (~500MB+ per model) — requires disk space management","Hub API rate limits apply for frequent model downloads or updates","No built-in model versioning for custom fine-tuning — requires manual Hub repository management"],"requires":["transformers library 4.20+","huggingface_hub library","Internet connection for initial download","HuggingFace account (optional, for private models)"],"input_types":["model identifier string: 'valentinafevu/yolos-fashionpedia'","optional: revision/branch name for version pinning"],"output_types":["loaded PyTorch model object ready for inference","model configuration and tokenizer/processor"],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-valentinafevu--yolos-fashionpedia__cap_5","uri":"capability://automation.workflow.azure.deployment.compatibility.with.containerized.inference","name":"azure deployment compatibility with containerized inference","description":"Model is compatible with Azure ML endpoints and containerized deployment through Docker, enabling serverless inference scaling on Azure infrastructure. The model can be packaged with inference code into a container image and deployed as an Azure ML endpoint with automatic scaling based on request volume. Supports both batch and real-time inference modes through Azure's managed inference services.","intents":["I want to deploy this model as a scalable REST API on Azure without managing infrastructure","I need to containerize the model for deployment across multiple cloud providers","I want to set up auto-scaling inference endpoints that handle variable traffic","I need to integrate this model into an Azure ML pipeline for production workflows"],"best_for":["teams already invested in Azure ecosystem seeking managed inference","enterprises requiring cloud-native deployment with auto-scaling","data scientists deploying models without DevOps expertise"],"limitations":["Azure-specific deployment requires Azure account and familiarity with Azure ML services","Containerization adds ~500MB overhead for base image + dependencies","Cold start latency for serverless endpoints can be 10-30 seconds on first request","Pricing depends on compute instance type and request volume — can be expensive for high-traffic applications","No built-in monitoring or logging — requires Azure Monitor integration for production observability"],"requires":["Azure account with ML workspace","Docker for containerization","Azure CLI or SDK for deployment","Sufficient Azure quota for compute instances"],"input_types":["image files via REST API","batch image datasets for batch inference jobs"],"output_types":["JSON response with detections","batch results stored in Azure Blob Storage"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-valentinafevu--yolos-fashionpedia__cap_6","uri":"capability://tool.use.integration.mit.licensed.open.source.model.with.commercial.usage.rights","name":"mit-licensed open-source model with commercial usage rights","description":"Released under MIT license, enabling unrestricted commercial use, modification, and redistribution without attribution requirements. The model weights, architecture, and training code are open-source, allowing users to fine-tune, quantize, or integrate into proprietary systems without licensing restrictions or royalty obligations.","intents":["I want to use this model in a commercial product without licensing concerns","I need to fine-tune this model on proprietary data for my company","I want to redistribute this model as part of my software product","I need to modify the model architecture for my specific use case"],"best_for":["commercial companies building fashion tech products","startups integrating detection into proprietary systems","researchers publishing modified versions of the model"],"limitations":["MIT license requires including license text in distributions — not a true 'no strings attached' license","No warranty or liability guarantees — users assume all risk for model failures in production","No official support or SLA — community-driven support only","Fashionpedia dataset has separate licensing — users must comply with dataset license for fine-tuning"],"requires":["Understanding of MIT license terms","Compliance with Fashionpedia dataset license if fine-tuning"],"input_types":["model weights and architecture"],"output_types":["modified model weights","fine-tuned checkpoints"],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":45,"verified":false,"data_access_risk":"high","permissions":["PyTorch 1.9+","Transformers library 4.20+","Python 3.7+","GPU with 4GB+ VRAM for batch inference (CPU inference possible but slow)","PIL/Pillow for image preprocessing","Knowledge of the 27 Fashionpedia category labels for interpretation","GPU with 4GB+ VRAM for reasonable batch sizes","Optional: Ray or Dask for distributed batch processing","Knowledge of target coordinate format (xyxy, xywh, normalized, etc.)","Image dimensions for converting normalized to pixel coordinates"],"failure_modes":["Optimized for fashion items specifically — performance degrades on non-apparel objects in images","Requires clear, well-lit images; struggles with extreme occlusion, motion blur, or heavily stylized artwork","Vision transformer architecture has higher computational cost (~2-3x slower inference than lightweight CNNs) and requires more GPU memory","Limited to 27 fashion categories from Fashionpedia — may not detect niche or emerging fashion items outside training distribution","No temporal consistency across video frames — each frame processed independently without motion tracking","Classification is limited to 27 Fashionpedia categories — cannot detect items outside this taxonomy","Confidence scores are model calibration-dependent; raw softmax outputs may not reflect true probability of correctness","No hierarchical classification — cannot distinguish between sub-categories (e.g., 'long-sleeve shirt' vs 'short-sleeve shirt')","Confidence scores can be overconfident on out-of-distribution images, requiring post-hoc calibration for production use","Batch size is limited by GPU memory — typical batch size 8-32 depending on image resolution and GPU VRAM","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.6775315046119605,"quality":0.24,"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":"2026-05-24T12:16:22.766Z","last_scraped_at":"2026-05-03T14:22:58.551Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":599201,"model_likes":145}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=valentinafevu--yolos-fashionpedia","compare_url":"https://unfragile.ai/compare?artifact=valentinafevu--yolos-fashionpedia"}},"signature":"JEQVVaNs735O/ae2SpGZBGa3STKnuJ3MJBavlp7nZ6qn8BhgLtsbpaBTbb1y5ypK38xjnrfwygmHc6Q3rFvqBg==","signedAt":"2026-06-20T01:08:05.212Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/valentinafevu--yolos-fashionpedia","artifact":"https://unfragile.ai/valentinafevu--yolos-fashionpedia","verify":"https://unfragile.ai/api/v1/verify?slug=valentinafevu--yolos-fashionpedia","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"}}