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The model processes input images through a CNN backbone (ResNet-50) to extract visual features, then applies transformer encoder-decoder layers to identify bounding boxes and confidence scores for table objects. It outputs normalized coordinates (x, y, width, height) for each detected table region, enabling downstream extraction pipelines to isolate and process tables independently from surrounding document content.","intents":["I need to automatically identify where tables are located in scanned documents or PDFs so I can extract them separately","I want to preprocess document images to segment table regions before running OCR or table structure recognition","I need to build a document processing pipeline that can distinguish tables from text and other visual elements"],"best_for":["document processing teams building end-to-end table extraction pipelines","enterprises digitizing paper documents with mixed content (text + tables)","researchers working on document understanding and table extraction benchmarks"],"limitations":["Trained on English-language documents; performance may degrade on non-Latin scripts or heavily stylized tables","Requires minimum image resolution (~224px) for reliable detection; very small or rotated tables may be missed","No multi-page document handling; processes single images independently without cross-page context","Detection confidence varies with table complexity; simple grid tables perform better than nested or irregular layouts"],"requires":["PyTorch 1.9+ or TensorFlow 2.x with transformers library","Python 3.7+","PIL/Pillow for image preprocessing","GPU recommended for batch processing (CPU inference ~500ms per image)"],"input_types":["image (JPEG, PNG, BMP)","numpy array (H, W, 3 format)","PIL Image objects"],"output_types":["structured JSON with bounding boxes (x, y, width, height in normalized coordinates)","confidence scores per detection","class labels (table)"],"categories":["image-visual","document-processing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-microsoft--table-transformer-detection__cap_1","uri":"capability://image.visual.batch.table.detection.with.confidence.filtering","name":"batch table detection with confidence filtering","description":"Processes multiple document images in parallel batches through the detection model with configurable confidence thresholds and non-maximum suppression (NMS) to filter overlapping detections. The implementation leverages PyTorch's batching capabilities to amortize model loading overhead and GPU memory usage across multiple images, returning deduplicated table regions with confidence scores above a user-specified threshold. This enables efficient processing of document collections without reloading the model between images.","intents":["I need to process 100+ document images and extract all table regions efficiently without reloading the model each time","I want to filter out low-confidence table detections to reduce false positives in my extraction pipeline","I need to apply consistent detection thresholds across a batch of documents for quality control"],"best_for":["data engineering teams processing large document corpora","production systems requiring high-throughput table detection","quality assurance workflows that need confidence-based filtering"],"limitations":["Batch size limited by available GPU memory; typical max 32-64 images per batch on 8GB VRAM","NMS post-processing adds ~50-100ms overhead per batch regardless of image count","Confidence thresholds are global; cannot apply per-document or per-region thresholds","No built-in handling for images of vastly different sizes; padding/resizing may affect small tables"],"requires":["PyTorch with CUDA support (or CPU fallback with significant slowdown)","transformers library 4.10+","torchvision for NMS utilities"],"input_types":["list of PIL Image objects","list of numpy arrays","directory path with image files"],"output_types":["list of detection results per image","filtered bounding boxes with confidence scores","metadata (image filename, processing time)"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-microsoft--table-transformer-detection__cap_2","uri":"capability://image.visual.transfer.learning.fine.tuning.for.domain.specific.tables","name":"transfer learning fine-tuning for domain-specific tables","description":"Enables fine-tuning the pre-trained table detection model on custom document datasets using the transformers library's Trainer API or native PyTorch training loops. The model's weights are initialized from Microsoft's pre-trained checkpoint, allowing rapid adaptation to domain-specific table layouts (e.g., financial statements, medical records, scientific papers) with minimal labeled data. Supports gradient accumulation, mixed-precision training, and distributed training across multiple GPUs to reduce training time and memory requirements.","intents":["I have 500 labeled examples of tables in my specific domain and want to fine-tune the model for better accuracy","I need to adapt the model to detect tables with unusual layouts or formatting that differ from the training data","I want to reduce false positives on non-table objects common in my document type"],"best_for":["teams with domain-specific document collections (legal, medical, financial)","organizations needing to improve detection accuracy without training from scratch","researchers experimenting with table detection on specialized datasets"],"limitations":["Requires 100+ labeled examples for meaningful improvement; fewer examples risk overfitting","Fine-tuning on GPU takes 2-4 hours for 500 examples; CPU training is impractical","No built-in data augmentation specific to table detection; requires manual augmentation pipeline","Transfer learning assumes source domain (general documents) is reasonably similar to target domain; extreme domain shift may require more data"],"requires":["PyTorch 1.9+","transformers 4.10+","CUDA-capable GPU with 8GB+ VRAM","labeled dataset in COCO or custom format with bounding box annotations"],"input_types":["image files (JPEG, PNG)","COCO-format JSON annotations","custom annotation format (convertible to COCO)"],"output_types":["fine-tuned model checkpoint (PyTorch .pt or safetensors format)","training metrics (loss curves, mAP scores)","evaluation results on validation set"],"categories":["image-visual","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-microsoft--table-transformer-detection__cap_3","uri":"capability://tool.use.integration.integration.with.document.processing.pipelines.via.huggingface.inference.api","name":"integration with document processing pipelines via huggingface inference api","description":"Exposes the table detection model through HuggingFace's managed Inference API endpoints, enabling serverless integration into document processing workflows without managing model deployment infrastructure. Requests are sent as HTTP POST calls with base64-encoded images, and responses return JSON with detected table bounding boxes. The API handles model versioning, auto-scaling, and GPU allocation transparently, with optional caching for repeated requests on identical images.","intents":["I want to call table detection from my web application without running a local model server","I need a REST API endpoint for table detection that scales automatically with request volume","I want to avoid managing GPU infrastructure and just pay per inference"],"best_for":["web applications and SaaS platforms requiring on-demand table detection","teams without GPU infrastructure or DevOps expertise","prototypes and MVPs needing quick integration without deployment overhead"],"limitations":["API latency ~500ms-2s per request due to network overhead and cold starts; unsuitable for real-time applications","Pricing based on inference calls; high-volume batch processing may be more expensive than self-hosted","Rate limits apply (typically 100 requests/minute for free tier); requires paid tier for production workloads","No local caching or offline fallback; requires internet connectivity"],"requires":["HuggingFace API token (free or paid)","HTTP client library (requests, curl, fetch)","Base64 encoding capability for image serialization"],"input_types":["base64-encoded image strings","image URLs","binary image data"],"output_types":["JSON with bounding boxes and confidence scores","HTTP status codes and error messages"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-microsoft--table-transformer-detection__cap_4","uri":"capability://image.visual.onnx.model.export.for.edge.deployment.and.inference.optimization","name":"onnx model export for edge deployment and inference optimization","description":"Exports the PyTorch table detection model to ONNX (Open Neural Network Exchange) format, enabling deployment on edge devices, mobile platforms, and optimized inference runtimes (TensorRT, CoreML, ONNX Runtime). The export process quantizes weights to INT8 or FP16 precision, reducing model size by 4-8x and inference latency by 2-3x compared to full-precision PyTorch. ONNX Runtime provides cross-platform inference with minimal dependencies, suitable for embedded document processing systems.","intents":["I need to run table detection on mobile devices or edge servers with limited compute","I want to reduce model size from 300MB to <50MB for deployment in resource-constrained environments","I need faster inference (sub-100ms) for real-time document scanning applications"],"best_for":["mobile and edge device developers","embedded systems requiring offline document processing","teams optimizing inference latency and power consumption"],"limitations":["ONNX export requires careful handling of dynamic shapes; some transformer operations may not export cleanly","Quantization (INT8) may reduce accuracy by 1-3% depending on calibration data quality","ONNX Runtime support varies by platform; some advanced transformer operations require fallback to CPU","Debugging quantized models is harder; requires calibration dataset and profiling tools"],"requires":["PyTorch 1.9+","onnx and onnx-simplifier libraries","ONNX Runtime 1.10+ for inference","Quantization calibration dataset (100-500 representative images)"],"input_types":["PyTorch model checkpoint","calibration images for quantization"],"output_types":["ONNX model file (.onnx)","quantized ONNX model (INT8 or FP16)","model metadata and input/output schemas"],"categories":["image-visual","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-microsoft--table-transformer-detection__cap_5","uri":"capability://image.visual.multi.scale.table.detection.with.resolution.adaptation","name":"multi-scale table detection with resolution adaptation","description":"Automatically adapts input image resolution and applies multi-scale inference to detect tables across a range of sizes within a single document. The model processes images at multiple scales (0.5x, 1.0x, 1.5x original resolution) and merges detections using NMS, enabling detection of both large tables spanning full pages and small tables embedded in dense text. Resolution adaptation normalizes input images to optimal inference size (typically 800x800 pixels) while preserving aspect ratio, preventing information loss from aggressive resizing.","intents":["I need to detect both large and small tables in the same document without missing either","I want to handle documents with varying DPI and resolution without manual preprocessing","I need to improve recall on small tables that are often missed by single-scale detection"],"best_for":["document processing systems handling heterogeneous document sources","OCR pipelines requiring robust table detection across resolution variations","quality assurance workflows where missing small tables is unacceptable"],"limitations":["Multi-scale inference increases latency by 2-3x compared to single-scale; ~1-2 seconds per image","Memory usage scales with number of scales; 3-scale inference requires ~3x GPU memory","NMS post-processing becomes more complex with overlapping detections across scales; may produce duplicate boxes","Very small tables (<50 pixels) still difficult to detect reliably even with multi-scale approach"],"requires":["PyTorch 1.9+","transformers library","torchvision for NMS and image utilities","GPU with 8GB+ VRAM for multi-scale batch processing"],"input_types":["image files at any resolution (100x100 to 4000x4000 pixels)","numpy arrays","PIL Image objects"],"output_types":["merged bounding boxes from all scales","confidence scores with scale information","scale-specific detections (optional)"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":52,"verified":false,"data_access_risk":"high","permissions":["PyTorch 1.9+ or TensorFlow 2.x with transformers library","Python 3.7+","PIL/Pillow for image preprocessing","GPU recommended for batch processing (CPU inference ~500ms per image)","PyTorch with CUDA support (or CPU fallback with significant slowdown)","transformers library 4.10+","torchvision for NMS utilities","PyTorch 1.9+","transformers 4.10+","CUDA-capable GPU with 8GB+ VRAM"],"failure_modes":["Trained on English-language documents; performance may degrade on non-Latin scripts or heavily stylized tables","Requires minimum image resolution (~224px) for reliable detection; very small or rotated tables may be missed","No multi-page document handling; processes single images independently without cross-page context","Detection confidence varies with table complexity; simple grid tables perform better than nested or irregular layouts","Batch size limited by available GPU memory; typical max 32-64 images per batch on 8GB VRAM","NMS post-processing adds ~50-100ms overhead per batch regardless of image count","Confidence thresholds are global; cannot apply per-document or per-region thresholds","No built-in handling for images of vastly different sizes; padding/resizing may affect small tables","Requires 100+ labeled examples for meaningful improvement; fewer examples risk overfitting","Fine-tuning on GPU takes 2-4 hours for 500 examples; CPU training is impractical","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.8209824121430481,"quality":0.37,"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":"2026-05-03T14:22:58.551Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":3394499,"model_likes":417}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=microsoft--table-transformer-detection","compare_url":"https://unfragile.ai/compare?artifact=microsoft--table-transformer-detection"}},"signature":"gQEIvwppP/pGYixkNQSfi/YYXLk5CfHoMb1/S7N/b4WSk1WLVJ36gBwkaaoclW50b20245djgqWHZiPERsz9Bw==","signedAt":"2026-06-22T04:03:42.176Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/microsoft--table-transformer-detection","artifact":"https://unfragile.ai/microsoft--table-transformer-detection","verify":"https://unfragile.ai/api/v1/verify?slug=microsoft--table-transformer-detection","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"}}