What can detr-doc-table-detection do?

document table detection via transformer-based object localization, multi-format model export and deployment packaging, huggingface hub-integrated model discovery and versioning, resnet-50 backbone feature extraction with transformer refinement, icdar2019 dataset-specialized table detection with domain adaptation

detr-doc-table-detection

ModelFree

object-detection model by undefined. 2,57,361 downloads.

Open Source

/ 100

5 capabilities

Capabilities5 decomposed

document table detection via transformer-based object localization

Medium confidence

Detects and localizes tables within document images using DETR (Detection Transformer), a transformer-based object detection architecture that replaces traditional CNN-based detectors with a set-based prediction approach. The model processes document images through a ResNet-50 backbone for feature extraction, then applies transformer encoder-decoder layers to directly predict table bounding boxes and class labels without hand-crafted NMS or anchor generation, enabling end-to-end differentiable detection optimized for document layout understanding.

Solves for

I need to automatically locate and extract table regions from scanned documents or PDFs for downstream OCR or data extractionI want to build a document processing pipeline that identifies where tables are positioned in multi-page documentsI need to segment document images into table and non-table regions for selective processingI'm building a document intelligence system that must handle mixed layouts with text, images, and tabular data

Best for

document processing teams automating table extraction from invoices, reports, and research papers

developers building document intelligence platforms requiring table localization as a preprocessing step

organizations processing ICDAR2019-style document datasets with mixed table layouts

Requires

PyTorch 1.9+ or ONNX Runtime 1.10+ for model inference

transformers library 4.5.0+ for model loading and preprocessing

Python 3.7+

Limitations

Trained exclusively on ICDAR2019 dataset — may have reduced accuracy on document types, table styles, or layouts not represented in training data

Requires GPU or significant CPU resources for real-time inference on high-resolution document images; CPU inference adds 500ms+ latency per image

No built-in handling for rotated, skewed, or severely degraded document images — preprocessing normalization required

What makes it unique

Uses DETR's transformer-based set prediction approach instead of traditional anchor-based detectors (Faster R-CNN, YOLO), eliminating hand-crafted NMS and enabling direct end-to-end optimization for document table detection; fine-tuned specifically on ICDAR2019 document dataset rather than generic object detection datasets like COCO

vs alternatives

Achieves higher precision on document tables than generic YOLO/Faster R-CNN models because it's domain-specialized on document layouts and uses transformer attention to reason about table structure globally rather than locally, though it trades inference speed for accuracy compared to lightweight YOLO variants

multi-format model export and deployment packaging

Medium confidence

Provides pre-converted model artifacts in PyTorch, ONNX, and SafeTensors formats, enabling deployment across heterogeneous inference environments without requiring manual conversion pipelines. The model is packaged with HuggingFace Hub integration, allowing single-line loading via transformers library and direct compatibility with ONNX Runtime, TensorRT, and edge deployment frameworks, eliminating format conversion bottlenecks in production workflows.

Solves for

I need to deploy this table detection model to both cloud GPU servers and edge devices with different runtime requirementsI want to use ONNX Runtime for faster inference on CPU or specialized hardware without PyTorch overheadI need to integrate this model into existing pipelines that expect SafeTensors format for security and performanceI'm building a multi-platform application and need the model in formats compatible with web, mobile, and server runtimes

Best for

MLOps teams managing multi-environment deployments (cloud, edge, on-premise)

developers building production systems requiring model format flexibility and zero-conversion overhead

organizations with security policies favoring SafeTensors format for model integrity verification

Requires

transformers 4.5.0+ for PyTorch format loading

ONNX Runtime 1.10+ for ONNX inference

safetensors 0.3.0+ for SafeTensors format support

Limitations

ONNX export may have minor numerical precision differences from PyTorch due to operator implementation variations; requires validation on target hardware

SafeTensors format support depends on downstream framework versions; older transformers versions may not support direct SafeTensors loading

No pre-built binaries for specialized runtimes (TensorRT, CoreML, NCNN); ONNX conversion is intermediate step requiring additional tooling

What makes it unique

Provides simultaneous multi-format availability (PyTorch + ONNX + SafeTensors) in a single HuggingFace Hub repository with zero-friction loading via transformers library, eliminating the need for custom conversion scripts or format-specific wrapper code that most open-source models require

vs alternatives

Faster deployment iteration than models requiring manual ONNX conversion (saving 30+ minutes per format change) and safer than single-format models because format flexibility enables fallback to alternative runtimes if one fails in production

huggingface hub-integrated model discovery and versioning

Medium confidence

Integrates with HuggingFace Model Hub infrastructure, providing automatic model versioning, revision tracking, and one-line loading via transformers library without manual weight downloads or path management. The model is registered with Hub endpoints compatibility, enabling direct inference via HuggingFace Inference API and automatic caching of model weights locally, with built-in support for model cards, dataset attribution (ICDAR2019), and Apache 2.0 license metadata for compliance tracking.

Solves for

I want to load a pre-trained table detection model with a single line of code without managing file paths or downloadsI need to track which version of the model I'm using in production and easily roll back to previous versions if neededI want to use this model via HuggingFace Inference API without running inference infrastructure myselfI need to verify the model's training dataset, license, and attribution before using it in my application

Best for

rapid prototyping teams wanting instant model access without infrastructure setup

developers building applications with strict compliance requirements (license tracking, dataset attribution)

teams using HuggingFace ecosystem tools (Transformers, Datasets, Accelerate) as their primary ML stack

Requires

transformers 4.5.0+

huggingface-hub 0.4.0+ for model downloading and caching

Internet connection for initial model download

Limitations

Requires internet connectivity for initial model download and Hub metadata fetching; offline inference requires pre-cached weights

HuggingFace Inference API has rate limits and latency SLAs that may not meet real-time requirements for high-throughput document processing

Model versioning is tied to HuggingFace Hub; if the repository is deleted or made private, dependent applications break

What makes it unique

Provides integrated Hub-native versioning and metadata tracking with automatic weight caching and Inference API compatibility, eliminating the need for custom model registry, version control, or download management that developers typically implement separately

vs alternatives

Faster time-to-inference than downloading models from GitHub releases or custom servers (automatic caching + CDN distribution) and more transparent than proprietary model APIs because dataset attribution, license, and model card are publicly visible and version-controlled

resnet-50 backbone feature extraction with transformer refinement

Medium confidence

Extracts visual features from document images using a pre-trained ResNet-50 CNN backbone (trained on ImageNet), which captures low-level document structure (edges, text regions, table grids) through hierarchical convolutional layers. These features are then refined through DETR's transformer encoder-decoder stack, which applies multi-head self-attention to reason about spatial relationships between document elements and predict table locations, enabling both local feature precision and global document layout understanding.

Solves for

I need robust feature extraction that captures both fine-grained document details (table borders, cell boundaries) and high-level layout structureI want to leverage pre-trained ImageNet knowledge to improve detection accuracy on document images without retraining from scratchI need to understand which document regions the model attends to when making table detection decisions for interpretabilityI'm building a system that must handle diverse document types (scanned, digital-born, mixed quality) with consistent feature quality

Best for

document processing teams handling diverse document sources requiring robust feature extraction

developers building interpretable document AI systems needing attention visualization

teams with limited labeled data who can benefit from ImageNet pre-training transfer learning

Requires

PyTorch 1.9+ with torchvision for ResNet-50 backbone loading

transformers 4.5.0+ for DETR architecture

Input images must be normalized to ImageNet statistics (mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])

Limitations

ResNet-50 backbone adds ~200MB model size; lighter backbones (ResNet-18, MobileNet) not provided in this artifact

Transformer refinement adds computational overhead (~2-3x slower than ResNet-50 alone); not suitable for real-time mobile inference without quantization

Feature extraction is optimized for 800x1066 resolution; significant upsampling/downsampling of input images may degrade feature quality

What makes it unique

Combines ImageNet-pretrained ResNet-50 CNN backbone with DETR transformer encoder-decoder, enabling both transfer learning from general vision tasks and document-specific spatial reasoning via attention, rather than using either CNN-only (Faster R-CNN) or transformer-only (ViT) approaches

vs alternatives

More accurate than ResNet-50 alone for document tables because transformer attention captures long-range dependencies between table elements, and more efficient than pure vision transformers because ResNet-50 backbone provides strong inductive bias for local feature extraction, reducing transformer compute requirements

icdar2019 dataset-specialized table detection with domain adaptation

Medium confidence

Fine-tuned specifically on the ICDAR2019 document analysis competition dataset, which contains diverse document layouts, table styles, and quality variations representative of real-world document processing scenarios. The model has learned document-specific patterns (table borders, cell structures, header rows, multi-column layouts) that generic object detectors lack, enabling higher precision on document tables while potentially requiring domain adaptation for out-of-distribution document types not represented in ICDAR2019.

Solves for

I need a table detector optimized for document images similar to ICDAR2019 competition datasets (invoices, research papers, forms)I want to understand how well this model will perform on my specific document type before investing in custom trainingI need to detect tables in documents with diverse layouts, quality levels, and table styles without building a custom datasetI'm building a document processing system and want to leverage domain-specialized models rather than generic object detectors

Best for

teams processing document types similar to ICDAR2019 (invoices, research papers, technical documents, forms)

organizations with limited labeled data who can use this pre-trained model as a starting point for fine-tuning

developers building document intelligence systems requiring high table detection precision on standard document layouts

Requires

Document images similar in style/quality to ICDAR2019 dataset for optimal performance

Validation dataset from target domain to measure performance degradation and determine if fine-tuning is needed

Optional: labeled data from target domain if domain adaptation fine-tuning is required

Limitations

Accuracy may degrade significantly on document types not represented in ICDAR2019 (e.g., handwritten documents, non-English layouts, specialized medical/legal formats)

No information provided on ICDAR2019 train/val/test split; unclear if model was evaluated on held-out test set or if performance metrics are available

Domain shift from ICDAR2019 to production documents (different scanners, quality, table styles) requires validation and potentially fine-tuning

What makes it unique

Fine-tuned exclusively on ICDAR2019 document competition dataset rather than generic COCO or Open Images, encoding document-specific patterns (table borders, cell structures, header recognition) that generic detectors lack, with explicit dataset attribution for reproducibility and compliance

vs alternatives

Higher precision on document tables than generic DETR-COCO or YOLO models because it's optimized for document layouts, but requires domain validation before deployment on out-of-distribution document types, whereas generic models have broader applicability at the cost of lower document-specific accuracy

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

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Best For

✓document processing teams automating table extraction from invoices, reports, and research papers
✓developers building document intelligence platforms requiring table localization as a preprocessing step
✓organizations processing ICDAR2019-style document datasets with mixed table layouts
✓teams needing lightweight, deployable table detection without cloud API dependencies
✓MLOps teams managing multi-environment deployments (cloud, edge, on-premise)
✓developers building production systems requiring model format flexibility and zero-conversion overhead
✓organizations with security policies favoring SafeTensors format for model integrity verification
✓teams optimizing inference latency across heterogeneous hardware (GPUs, CPUs, TPUs, mobile accelerators)

Known Limitations

⚠Trained exclusively on ICDAR2019 dataset — may have reduced accuracy on document types, table styles, or layouts not represented in training data
⚠Requires GPU or significant CPU resources for real-time inference on high-resolution document images; CPU inference adds 500ms+ latency per image
⚠No built-in handling for rotated, skewed, or severely degraded document images — preprocessing normalization required
⚠Outputs only bounding box coordinates and class labels; does not perform table structure parsing, cell extraction, or content OCR
⚠Fixed input resolution (typically 800x1066 for DETR) may require image resizing, potentially losing fine-grained table details in high-resolution documents
⚠ONNX export may have minor numerical precision differences from PyTorch due to operator implementation variations; requires validation on target hardware

Requirements

PyTorch 1.9+ or ONNX Runtime 1.10+ for model inferencetransformers library 4.5.0+ for model loading and preprocessingPython 3.7+Pillow or OpenCV for image loading and preprocessingGPU with CUDA 11.0+ recommended for production inference; CPU inference supported but slowtransformers 4.5.0+ for PyTorch format loadingONNX Runtime 1.10+ for ONNX inferencesafetensors 0.3.0+ for SafeTensors format support

Input / Output

Accepts: image/jpeg, image/png, image/tiff, numpy arrays (H×W×3 format), PIL Image objects, model weights in PyTorch (.pt, .pth), model weights in ONNX (.onnx), model weights in SafeTensors (.safetensors), model identifier string (e.g., 'TahaDouaji/detr-doc-table-detection'), revision/branch specification (e.g., 'main', 'v1.0', specific commit hash), image/jpeg, image/png, image/tiff (document images), numpy arrays (H×W×3, uint8 or float32), document images (scanned PDFs, digital documents) similar to ICDAR2019 dataset, images with table-containing documents in English or similar languages

Produces: structured data: bounding boxes (x_min, y_min, x_max, y_max format), class labels (table vs non-table or multi-class table types), confidence scores per detection, JSON serializable detection dictionaries compatible with COCO format, loaded model objects compatible with transformers pipeline API, ONNX inference sessions ready for onnxruntime.InferenceSession, SafeTensors-backed model state dicts for memory-efficient loading, transformers.AutoModelForObjectDetection instance, model metadata (training dataset, license, model card HTML), model revision history and version information, feature maps from ResNet-50 backbone (C×H'×W' tensors), transformer encoder output (sequence of refined feature vectors), attention weights from transformer multi-head attention (for visualization), final detection predictions (bounding boxes + class logits), table bounding box predictions optimized for ICDAR2019-style document layouts, confidence scores reflecting model's certainty on ICDAR2019-trained patterns

UnfragileRank

Adoption60%(40% weight)

Quality21%(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

5 capabilities

Visit detr-doc-table-detection→

Model Details

huggingface

Provider

transformers

Architecture

257,361

Downloads

Tasks

object-detection

About

TahaDouaji/detr-doc-table-detection — a object-detection model on HuggingFace with 2,57,361 downloads

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Data Sources

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Capabilities5 decomposed

document table detection via transformer-based object localization

Medium confidence

Solves for

Best for

document processing teams automating table extraction from invoices, reports, and research papers

developers building document intelligence platforms requiring table localization as a preprocessing step

organizations processing ICDAR2019-style document datasets with mixed table layouts

Requires

PyTorch 1.9+ or ONNX Runtime 1.10+ for model inference

transformers library 4.5.0+ for model loading and preprocessing

Python 3.7+

Limitations

Trained exclusively on ICDAR2019 dataset — may have reduced accuracy on document types, table styles, or layouts not represented in training data

Requires GPU or significant CPU resources for real-time inference on high-resolution document images; CPU inference adds 500ms+ latency per image

No built-in handling for rotated, skewed, or severely degraded document images — preprocessing normalization required

What makes it unique

vs alternatives

multi-format model export and deployment packaging

Medium confidence

Solves for

Best for

MLOps teams managing multi-environment deployments (cloud, edge, on-premise)

developers building production systems requiring model format flexibility and zero-conversion overhead

organizations with security policies favoring SafeTensors format for model integrity verification

Requires

transformers 4.5.0+ for PyTorch format loading

ONNX Runtime 1.10+ for ONNX inference

safetensors 0.3.0+ for SafeTensors format support

Limitations

ONNX export may have minor numerical precision differences from PyTorch due to operator implementation variations; requires validation on target hardware

SafeTensors format support depends on downstream framework versions; older transformers versions may not support direct SafeTensors loading

No pre-built binaries for specialized runtimes (TensorRT, CoreML, NCNN); ONNX conversion is intermediate step requiring additional tooling

What makes it unique

vs alternatives

huggingface hub-integrated model discovery and versioning

Medium confidence

Solves for

Best for

rapid prototyping teams wanting instant model access without infrastructure setup

developers building applications with strict compliance requirements (license tracking, dataset attribution)

teams using HuggingFace ecosystem tools (Transformers, Datasets, Accelerate) as their primary ML stack

Requires

transformers 4.5.0+

huggingface-hub 0.4.0+ for model downloading and caching

Internet connection for initial model download

Limitations

Requires internet connectivity for initial model download and Hub metadata fetching; offline inference requires pre-cached weights

HuggingFace Inference API has rate limits and latency SLAs that may not meet real-time requirements for high-throughput document processing

Model versioning is tied to HuggingFace Hub; if the repository is deleted or made private, dependent applications break

What makes it unique

vs alternatives

resnet-50 backbone feature extraction with transformer refinement

Medium confidence

Solves for

Best for

document processing teams handling diverse document sources requiring robust feature extraction

developers building interpretable document AI systems needing attention visualization

teams with limited labeled data who can benefit from ImageNet pre-training transfer learning

Requires

PyTorch 1.9+ with torchvision for ResNet-50 backbone loading

transformers 4.5.0+ for DETR architecture

Input images must be normalized to ImageNet statistics (mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])

Limitations

ResNet-50 backbone adds ~200MB model size; lighter backbones (ResNet-18, MobileNet) not provided in this artifact

Transformer refinement adds computational overhead (~2-3x slower than ResNet-50 alone); not suitable for real-time mobile inference without quantization

Feature extraction is optimized for 800x1066 resolution; significant upsampling/downsampling of input images may degrade feature quality

What makes it unique

vs alternatives

icdar2019 dataset-specialized table detection with domain adaptation

Medium confidence

Solves for

Best for

teams processing document types similar to ICDAR2019 (invoices, research papers, technical documents, forms)

organizations with limited labeled data who can use this pre-trained model as a starting point for fine-tuning

developers building document intelligence systems requiring high table detection precision on standard document layouts

Requires

Document images similar in style/quality to ICDAR2019 dataset for optimal performance

Validation dataset from target domain to measure performance degradation and determine if fine-tuning is needed

Optional: labeled data from target domain if domain adaptation fine-tuning is required

Limitations

Accuracy may degrade significantly on document types not represented in ICDAR2019 (e.g., handwritten documents, non-English layouts, specialized medical/legal formats)

No information provided on ICDAR2019 train/val/test split; unclear if model was evaluated on held-out test set or if performance metrics are available

Domain shift from ICDAR2019 to production documents (different scanners, quality, table styles) requires validation and potentially fine-tuning

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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detr-doc-table-detection

Capabilities5 decomposed

document table detection via transformer-based object localization

multi-format model export and deployment packaging

huggingface hub-integrated model discovery and versioning

resnet-50 backbone feature extraction with transformer refinement

icdar2019 dataset-specialized table detection with domain adaptation

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Best For

Known Limitations

Requirements

Input / Output

UnfragileRank

Model Details

About

Categories

Alternatives to detr-doc-table-detection

Are you the builder of detr-doc-table-detection?

Get the weekly brief

Data Sources

detr-doc-table-detection

Capabilities5 decomposed

document table detection via transformer-based object localization

multi-format model export and deployment packaging

huggingface hub-integrated model discovery and versioning

resnet-50 backbone feature extraction with transformer refinement

icdar2019 dataset-specialized table detection with domain adaptation

Related Artifactssharing capabilities

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Datasaur

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Best For

Known Limitations

Requirements

Input / Output

UnfragileRank

Model Details

About

Categories

Alternatives to detr-doc-table-detection

Are you the builder of detr-doc-table-detection?

Get the weekly brief

Data Sources