TF-ID

Tool for recognizing tables and figures in academic literature

CommonProductProductivityAcademic literatureObject detection

TF-ID is an object detection model series created by Yifei Hu for extracting tables and figures from academic papers. These models are fine-tuned based on the microsoft/Florence-2 checkpoint, offering versions with or without title text. Their aim is to enhance the accessibility and processing efficiency of information in academic literature.

Extract tables and figures from academic papers
Provide versions with and without title text
Fine-tuned from the microsoft/Florence-2 model checkpoint
Supports custom model training
Open-source model weights and annotated datasets
Detailed training and usage guides provided

TF-ID is primarily designed for researchers and scholars who need to process a large volume of academic papers
especially those who require automated extraction of tables and figures from literature. It saves time in manually searching and organizing data
thereby improving research efficiency.

Researchers use TF-ID to automatically extract experimental result tables from academic papers.
Scholars utilize the TF-ID model to analyze chart data from historical literature.
Educational institutions adopt TF-ID to assist students in quickly obtaining statistical information from literature.

Clone the TF-ID GitHub repository locally.
Download and prepare the required datasets and annotation files.
Place the annotated files and image files in the specified directory as required.
Use the provided scripts to convert the dataset into the required format.
Launch model training using the Accelerate tool.

Visit

TF-ID Visit Over Time

Monthly Visits

494758773

Bounce Rate

37.69%

Page per Visit

5.7

Visit Duration

00:06:29

TF-ID Visit Trend

TF-ID Visit Geography

TF-ID Traffic Sources

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