U4R
/

StructTable-base

@@ -11,23 +11,22 @@ license: apache-2.0
 <h1>StructEqTable-Deploy: A High-efficiency Open-source Toolkit for Table-to-Latex Transformation</h1>
-[[ Related Paper ]](https://arxiv.org/abs/2406.11633) [[ Website ]](https://unimodal4reasoning.github.io/DocGenome_page/) [[ Dataset (Google Drive)]](https://drive.google.com/drive/folders/1OIhnuQdIjuSSDc_QL2nP4NwugVDgtItD) [[ Dataset (Hugging Face) ]](https://huggingface.co/datasets/U4R/DocGenome/tree/main)
-[[Models 🤗(Hugging Face)]](https://huggingface.co/U4R/StructTable-base/tree/main)
 </div>
 Welcome to the official repository of StructEqTable-Deploy, a solution that converts images of Table into LaTeX, powered by scalable data from [DocGenome benchmark](https://unimodal4reasoning.github.io/DocGenome_page/).
 ## Overview
 Table is an effective way to represent structured data in scientific publications, financial statements, invoices, web pages, and many other scenarios. Extracting tabular data from a visual table image and performing the downstream reasoning tasks according to the extracted data is challenging, mainly due to that tables often present complicated column and row headers with spanning cell operation. To address these challenges, we present TableX, a large-scale multi-modal table benchmark extracted from [DocGenome benchmark](https://unimodal4reasoning.github.io/DocGenome_page/) for table pre-training, comprising more than 2 million high-quality Image-LaTeX pair data covering 156 disciplinary classes. Besides, benefiting from such large-scale data, we train an end-to-end model, StructEqTable, which provides the capability to precisely obtain the corresponding LaTeX description from a visual table image and perform multiple table-related reasoning tasks, including structural extraction and question answering, broadening its application scope and potential.
 ## Changelog
 Tips: Current version of StructEqTable is able to process table images from scientific documents such as arXiv, Scihub papers. Times New Roman And Songti(宋体) are main fonts used in table image, other fonts may decrease the accuracy of the model's output.
 - **[2024/8/22] 🔥 We have released our [latest model](https://huggingface.co/U4R/StructTable-base/tree/v0.2), fine-tuned on the DocGenome dataset. This version features improved inference speed and robustness, achieved through data augmentation and reduced image token num.**
-- [2024/8/08] 🔥 We have released the TensorRT accelerated version, which only takes about 1 second for most images on GPU A100. Please follow the tutorial to install the environment and compile the model weights.
 - [2024/7/30] We have released the first version of StructEqTable.
 ## TODO
@@ -59,6 +58,14 @@ pip install "git+https://github.com/UniModal4Reasoning/StructEqTable-Deploy.git"
 pip install struct-eqtable==0.1.0
 ```
 ## Quick Demo
 - Run the demo/demo.py
 ```shell script

 <h1>StructEqTable-Deploy: A High-efficiency Open-source Toolkit for Table-to-Latex Transformation</h1>
+[[ Github Repo ]](https://github.com/UniModal4Reasoning/StructEqTable-Deploy) [[ Related Paper ]](https://arxiv.org/abs/2406.11633) [[ Website ]](https://unimodal4reasoning.github.io/DocGenome_page/)
+[[ Dataset (Google Drive)]](https://drive.google.com/drive/folders/1OIhnuQdIjuSSDc_QL2nP4NwugVDgtItD) [[ Dataset (Hugging Face) ]](https://huggingface.co/datasets/U4R/DocGenome/tree/main) [[Models 🤗(Hugging Face)]](https://huggingface.co/U4R/StructTable-base/tree/main)
 </div>
 Welcome to the official repository of StructEqTable-Deploy, a solution that converts images of Table into LaTeX, powered by scalable data from [DocGenome benchmark](https://unimodal4reasoning.github.io/DocGenome_page/).
 ## Overview
 Table is an effective way to represent structured data in scientific publications, financial statements, invoices, web pages, and many other scenarios. Extracting tabular data from a visual table image and performing the downstream reasoning tasks according to the extracted data is challenging, mainly due to that tables often present complicated column and row headers with spanning cell operation. To address these challenges, we present TableX, a large-scale multi-modal table benchmark extracted from [DocGenome benchmark](https://unimodal4reasoning.github.io/DocGenome_page/) for table pre-training, comprising more than 2 million high-quality Image-LaTeX pair data covering 156 disciplinary classes. Besides, benefiting from such large-scale data, we train an end-to-end model, StructEqTable, which provides the capability to precisely obtain the corresponding LaTeX description from a visual table image and perform multiple table-related reasoning tasks, including structural extraction and question answering, broadening its application scope and potential.
 ## Changelog
 Tips: Current version of StructEqTable is able to process table images from scientific documents such as arXiv, Scihub papers. Times New Roman And Songti(宋体) are main fonts used in table image, other fonts may decrease the accuracy of the model's output.
 - **[2024/8/22] 🔥 We have released our [latest model](https://huggingface.co/U4R/StructTable-base/tree/v0.2), fine-tuned on the DocGenome dataset. This version features improved inference speed and robustness, achieved through data augmentation and reduced image token num.**
+- [2024/8/08] We have released the TensorRT accelerated version, which only takes about 1 second for most images on GPU A100. Please follow the tutorial to install the environment and compile the model weights.
 - [2024/7/30] We have released the first version of StructEqTable.
 ## TODO
 pip install struct-eqtable==0.1.0
 ```
+## Model Zoo
+| Model | Image Token Num | Model Size | Training Data | Data Augmentation | TensorRT | HuggingFace |
+|---------------------|---------------------|------------|------------------|-------------------|----------|-------------------|
+| StructEqTable-base | 4096 | ~300M | DocGenome |  | ☑️ | [v0.1](https://huggingface.co/U4R/StructTable-base/tree/v0.1) |
+| StructEqTable-base | 2048 | ~300M | DocGenome | ☑️ | ☑️ | [v0.2](https://huggingface.co/U4R/StructTable-base/tree/v0.2) |
 ## Quick Demo
 - Run the demo/demo.py
 ```shell script