HyperAI超神经

Document Layout Analysis On Publaynet Val

评估指标

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List

Overall

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Title

评测结果

各个模型在此基准测试上的表现结果

							Paper Title
DETR	0.975	0.964	0.957	0.981	0.947	0.918	Bridging the Performance Gap between DETR and R-CNN for Graphical Object Detection in Document Images
VGT	0.971	0.968	0.962	0.981	0.950	0.939	Vision Grid Transformer for Document Layout Analysis
LayoutLMv3-B	0.970	0.955	0.951	0.979	0.945	0.906	LayoutLMv3: Pre-training for Document AI with Unified Text and Image Masking
CDeC-Net	-	-	-	0.978	-	-	CDeC-Net: Composite Deformable Cascade Network for Table Detection in Document Images
DiT-L	0.972	0.960	0.949	0.978	0.944	0.893	DiT: Self-supervised Pre-training for Document Image Transformer
DoPTA-HR	0.970	0.957	0.949	0.977	0.944	0.895	DoPTA: Improving Document Layout Analysis using Patch-Text Alignment
ResNext-101-32×8d	0.968	0.940	0.935	0.976	0.930	0.862	Vision Grid Transformer for Document Layout Analysis
TRDLU	0.966	0.975	0.959	0.976	0.958	0.921	Transformer-based Approach for Document Understanding
VSR	0.964	0.947	0.957	0.974	0.967	0.931	VSR: A Unified Framework for Document Layout Analysis combining Vision, Semantics and Relations
UDoc	0.964	0.937	0.939	0.973	0.939	0.885	Unified Pretraining Framework for Document Understanding
BEiT-B	0.957	0.924	0.931	0.973	0.934	0.866	BEiT: BERT Pre-Training of Image Transformers
DeiT-B	0.957	0.921	0.932	0.972	0.934	0.874	Training data-efficient image transformers & distillation through attention
Mask RCNN	0.949	0.886	0.910	0.960	0.916	0.840	PubLayNet: largest dataset ever for document layout analysis
Faster RCNN	0.937	0.883	0.902	0.954	0.910	0.826	PubLayNet: largest dataset ever for document layout analysis
GLAM	0.206	0.862	0.722	0.868	0.878	0.800	A Graphical Approach to Document Layout Analysis

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HyperAI超神经

Document Layout Analysis On Publaynet Val

评估指标

Figure

List

Overall

Table

Text

Title

评测结果

各个模型在此基准测试上的表现结果

							Paper Title
DETR	0.975	0.964	0.957	0.981	0.947	0.918	Bridging the Performance Gap between DETR and R-CNN for Graphical Object Detection in Document Images
VGT	0.971	0.968	0.962	0.981	0.950	0.939	Vision Grid Transformer for Document Layout Analysis
LayoutLMv3-B	0.970	0.955	0.951	0.979	0.945	0.906	LayoutLMv3: Pre-training for Document AI with Unified Text and Image Masking
CDeC-Net	-	-	-	0.978	-	-	CDeC-Net: Composite Deformable Cascade Network for Table Detection in Document Images
DiT-L	0.972	0.960	0.949	0.978	0.944	0.893	DiT: Self-supervised Pre-training for Document Image Transformer
DoPTA-HR	0.970	0.957	0.949	0.977	0.944	0.895	DoPTA: Improving Document Layout Analysis using Patch-Text Alignment
ResNext-101-32×8d	0.968	0.940	0.935	0.976	0.930	0.862	Vision Grid Transformer for Document Layout Analysis
TRDLU	0.966	0.975	0.959	0.976	0.958	0.921	Transformer-based Approach for Document Understanding
VSR	0.964	0.947	0.957	0.974	0.967	0.931	VSR: A Unified Framework for Document Layout Analysis combining Vision, Semantics and Relations
UDoc	0.964	0.937	0.939	0.973	0.939	0.885	Unified Pretraining Framework for Document Understanding
BEiT-B	0.957	0.924	0.931	0.973	0.934	0.866	BEiT: BERT Pre-Training of Image Transformers
DeiT-B	0.957	0.921	0.932	0.972	0.934	0.874	Training data-efficient image transformers & distillation through attention
Mask RCNN	0.949	0.886	0.910	0.960	0.916	0.840	PubLayNet: largest dataset ever for document layout analysis
Faster RCNN	0.937	0.883	0.902	0.954	0.910	0.826	PubLayNet: largest dataset ever for document layout analysis
GLAM	0.206	0.862	0.722	0.868	0.878	0.800	A Graphical Approach to Document Layout Analysis

0 of 15 row(s) selected.