layoutlmv3 base finetuned publaynet

Introduction

The layoutlmv3-base-finetuned-publaynet model is a fine-tuned version of the microsoft/layoutlmv3-base, specifically trained on the PubLayNet dataset. This model is designed to excel in document layout analysis, achieving a mean Average Precision (mAP) of 95.1 on the PubLayNet validation set.

Architecture

This model leverages the LayoutLMv3 architecture, which integrates both text and image data for document understanding. The architecture is pre-trained using a unified text and image masking strategy to enhance the model's document analysis capabilities.

Training

The model was fine-tuned on the PubLayNet dataset, which is a large-scale dataset designed for document layout analysis. The training process involved optimizing the model to improve its precision in identifying and understanding various document elements.

Guide: Running Locally

1. Install Prerequisites: Ensure you have Python installed, along with necessary libraries such as transformers and torch.
2. Clone the Repository: Download the model files from the Hugging Face repository.
3. Load the Model: Use the transformers library to load the model and tokenizer.
4. Inference: Prepare your input data, and run inference using the loaded model to analyze document layouts.

For optimal performance, especially with large datasets or complex documents, consider using cloud GPUs from providers like AWS, Google Cloud, or Azure.

License

The model and its associated content are licensed under the Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0). This license allows for sharing and adaptation, provided it's not for commercial purposes and appropriate credit is given. The source code includes portions based on the Hugging Face transformers project and adheres to the Microsoft Open Source Code of Conduct.