{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hf-model-paddlepaddle--pp-doclayoutv3_safetensors","slug":"paddlepaddle--pp-doclayoutv3_safetensors","name":"PP-DocLayoutV3_safetensors","type":"model","url":"https://huggingface.co/PaddlePaddle/PP-DocLayoutV3_safetensors","page_url":"https://unfragile.ai/paddlepaddle--pp-doclayoutv3_safetensors","categories":["image-generation"],"tags":["transformers","safetensors","pp_doclayout_v3","object-detection","PaddleOCR","PaddlePaddle","image-segmentation","ocr","layout","layout_detection","en","zh","multilingual","arxiv:2601.21957","base_model:PaddlePaddle/PP-DocLayoutV3","base_model:finetune:PaddlePaddle/PP-DocLayoutV3","license:apache-2.0","endpoints_compatible","region:us"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"hf-model-paddlepaddle--pp-doclayoutv3_safetensors__cap_0","uri":"capability://image.visual.document.layout.region.detection","name":"document-layout-region-detection","description":"Detects and localizes distinct layout regions (text blocks, tables, figures, headers, footers) within document images using an object-detection backbone trained on diverse document types. 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The preprocessing pipeline handles common document scanning artifacts (skew, low contrast, variable DPI) by applying adaptive histogram equalization and geometric normalization, ensuring consistent input quality across diverse document sources.","intents":["I have scanned documents with varying quality and need to standardize them before layout detection","I want to automatically correct image orientation and contrast issues in document scans","I need to resize documents to optimal dimensions for the model without losing layout information"],"best_for":["document digitization pipelines processing real-world scans with quality variations","teams building robust document processing systems that handle diverse input sources","developers implementing preprocessing for improved layout detection accuracy"],"limitations":["Automatic orientation detection works only for documents with clear text orientation; fails on documents with mixed orientations or complex layouts","Contrast adjustment may over-enhance low-quality scans, introducing artifacts that degrade detection","Resizing to fixed dimensions may distort aspect ratios for non-standard document sizes (e.g., business cards, wide-format documents)","Preprocessing adds ~100-200ms latency per image on CPU"],"requires":["Python 3.7+","OpenCV or PIL/Pillow for image processing","Input images in JPEG/PNG/BMP format"],"input_types":["image file path (string)","numpy array (H×W×3 uint8)","PIL Image object"],"output_types":["normalized image array (H×W×3 uint8)","preprocessing metadata (original size, applied transformations, orientation angle)"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-paddlepaddle--pp-doclayoutv3_safetensors__cap_4","uri":"capability://tool.use.integration.safetensors.format.model.loading","name":"safetensors-format-model-loading","description":"Loads model weights from safetensors format (a safe, fast serialization format) instead of traditional pickle-based PyTorch checkpoints, enabling zero-copy memory mapping and eliminating arbitrary code execution risks. The safetensors loader parses the binary format directly, mapping weights into GPU/CPU memory without intermediate deserialization, reducing model loading time and memory overhead.","intents":["I want to load the model quickly without waiting for pickle deserialization","I need to ensure model integrity and avoid security risks from untrusted checkpoint files","I want to reduce memory footprint during model loading for resource-constrained environments"],"best_for":["teams deploying models in production requiring fast startup times","developers building secure model serving systems with untrusted model sources","resource-constrained environments (edge devices, serverless functions) requiring minimal memory overhead"],"limitations":["Safetensors format requires compatible framework versions (PyTorch 1.13+, PaddlePaddle 2.4+); older versions require conversion","Zero-copy memory mapping only works on systems with sufficient virtual address space; may fall back to standard loading on 32-bit systems","Model modification/fine-tuning requires converting back to framework-native format or using safetensors-compatible tools","Debugging model weights is more difficult with safetensors due to lack of standard Python introspection tools"],"requires":["Python 3.7+","safetensors library (pip install safetensors)","PyTorch 1.13+ or PaddlePaddle 2.4+","Model weights in safetensors format (.safetensors file)"],"input_types":["safetensors file path (string)","HuggingFace model identifier (e.g., 'PaddlePaddle/PP-DocLayoutV3_safetensors')"],"output_types":["loaded model object (PyTorch nn.Module or PaddlePaddle Layer)","model metadata (architecture, parameter count, loading time)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-paddlepaddle--pp-doclayoutv3_safetensors__cap_5","uri":"capability://tool.use.integration.huggingface.model.hub.integration","name":"huggingface-model-hub-integration","description":"Integrates with HuggingFace Model Hub for seamless model discovery, versioning, and deployment through the transformers library and HuggingFace Hub API. 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The model weights are stored in a framework-neutral format that can be loaded into either PyTorch tensors or PaddlePaddle parameters, allowing teams to choose their preferred inference framework based on deployment constraints.","intents":["I want to use the model with PyTorch for research but deploy with PaddlePaddle for production","I need to run the model in environments where only one framework is available","I want to avoid framework-specific model conversion and maintain a single model artifact"],"best_for":["teams with mixed framework deployments (research in PyTorch, production in PaddlePaddle)","organizations standardizing on different frameworks for different use cases","developers building framework-agnostic model serving systems"],"limitations":["Framework-specific optimizations (quantization, pruning) may not transfer between frameworks; requires re-optimization per framework","Inference performance varies significantly between frameworks; PyTorch typically faster on NVIDIA GPUs, PaddlePaddle optimized for mobile/edge","Debugging and profiling requires framework-specific tools; no unified debugging experience","Custom layers or framework-specific features not supported in safetensors format"],"requires":["Python 3.7+","PyTorch 1.13+ OR PaddlePaddle 2.4+ (not both required, but one must be installed)","safetensors library for format compatibility"],"input_types":["safetensors model file","framework selection parameter (pytorch or paddlepaddle)"],"output_types":["framework-native model object (torch.nn.Module or paddle.nn.Layer)","inference results in framework-native tensor format"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-paddlepaddle--pp-doclayoutv3_safetensors__cap_7","uri":"capability://image.visual.document.layout.visualization.debugging","name":"document-layout-visualization-debugging","description":"Generates visual overlays of detected layout regions on original document images for debugging and validation, displaying bounding boxes with region type labels and confidence scores. The visualization pipeline renders detection results directly on images, enabling quick visual inspection of model performance and identification of detection failures without manual annotation.","intents":["I want to visually inspect what regions the model detected to verify accuracy","I need to debug detection failures and understand where the model is making mistakes","I want to create visualizations for model evaluation reports and stakeholder presentations"],"best_for":["developers debugging layout detection models during development","teams creating model evaluation reports with visual evidence","QA engineers validating model performance on test documents"],"limitations":["Visualization quality depends on image resolution; low-resolution images produce cluttered overlays","No built-in filtering for low-confidence detections; visualization can be overwhelming with many false positives","Bounding box rendering is basic (rectangles only); no support for polygon masks or complex region shapes","Visualization adds ~50-100ms per image for rendering; 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