DETR Document Table Detection Model
| Property | Value |
|---|---|
| Parameter Count | 41.6M |
| License | Apache 2.0 |
| Base Model | facebook/detr-resnet-50 |
| Paper | End-to-End Object Detection with Transformers |
| Training Data | ICDAR2019 Table Dataset |
What is detr-doc-table-detection?
detr-doc-table-detection is a specialized deep learning model designed for detecting both bordered and borderless tables in document images. Built upon Facebook's DETR-ResNet-50 architecture, this model represents a significant advancement in document analysis and table extraction technologies. With over 363,000 downloads, it has proven to be a valuable tool in the document processing ecosystem.
Implementation Details
The model leverages the DETR (DEtection TRansformer) architecture combined with a ResNet-50 backbone, utilizing transformer encoders and decoders for efficient table detection. It processes images using PyTorch and supports F32 tensor operations.
- Built on the DETR architecture for end-to-end object detection
- Implements ResNet-50 as the backbone network
- Utilizes transformer-based detection mechanism
- Supports both bordered and borderless table detection
Core Capabilities
- Accurate detection of tables regardless of border presence
- End-to-end processing without post-processing steps
- High-confidence detection with customizable thresholds
- Efficient processing of document images
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its ability to detect both bordered and borderless tables in documents, which is traditionally a challenging task in document processing. It uses the innovative DETR architecture, eliminating the need for complex post-processing steps common in traditional object detection systems.
Q: What are the recommended use cases?
The model is ideal for document processing pipelines, particularly in scenarios involving automated data extraction from business documents, academic papers, and financial reports. It's especially useful in digitization projects where table extraction is a crucial component.
