table transformer detection

Introduction

The Table Transformer is a DETR-based model fine-tuned for table detection, trained on the PubTables1M dataset. It was introduced in the paper "PubTables-1M: Towards Comprehensive Table Extraction From Unstructured Documents" by Smock et al. This model is designed to detect tables within documents, leveraging a Transformer-based architecture.

Architecture

The Table Transformer utilizes the DETR (DEtection TRansformers) architecture, which is a Transformer-based object detection model. It employs a "normalize before" setting, meaning that layer normalization is applied prior to self- and cross-attention layers. This architectural choice aligns with DETR's method for efficient and effective object detection.

Training

The model has been fine-tuned using the PubTables1M dataset, which is designed for comprehensive table extraction from unstructured documents. The training process includes optimizing the DETR architecture specifically for table detection tasks.

Guide: Running Locally

To run the Table Transformer model locally, follow these steps:

1. Setup Environment: Ensure you have Python and PyTorch installed.
2. Clone the Repository: Download the model repository from Hugging Face.
3. Install Required Libraries: Use pip to install necessary libraries, such as transformers and torch.
4. Load the Model: Use the Hugging Face Transformers library to load the Table Transformer model.
5. Inference: Utilize the model to detect tables in your document files.

For optimal performance, consider using cloud GPUs from providers like AWS, Google Cloud, or Azure, which offer scalable and efficient computing resources.

License

The Table Transformer model is released under the MIT License, allowing for broad usage and modification with minimal restrictions.