TF ID Base

🚀 TF-ID: Table/Figure IDentifier for academic papers

TF-ID is a family of object detection models designed to extract tables and figures from academic papers. It offers four versions to meet different needs, all fine - tuned from the microsoft/Florence - 2 checkpoints.

✨ Features

• Multiple Model Versions: Available in base and large sizes, with and without caption text extraction options.
• Accurate Detection: Trained on manually - annotated data from Hugging Face Daily Papers to ensure high - quality bounding box detection for tables and figures.
• Flexible Output: Depending on the model version, it can return bounding boxes with or without caption text.

📦 Installation

The installation process is mainly about setting up the necessary Python libraries. You can use the following code to load the model:

import requests
from PIL import Image
from transformers import AutoProcessor, AutoModelForCausalLM 

model = AutoModelForCausalLM.from_pretrained("yifeihu/TF-ID-base", trust_remote_code=True)
processor = AutoProcessor.from_pretrained("yifeihu/TF-ID-base", trust_remote_code=True)

💻 Usage Examples

Basic Usage

import requests
from PIL import Image
from transformers import AutoProcessor, AutoModelForCausalLM 

model = AutoModelForCausalLM.from_pretrained("yifeihu/TF-ID-base", trust_remote_code=True)
processor = AutoProcessor.from_pretrained("yifeihu/TF-ID-base", trust_remote_code=True)

prompt = "<OD>"

url = "https://huggingface.co/yifeihu/TF-ID-base/resolve/main/arxiv_2305_10853_5.png?download=true"
image = Image.open(requests.get(url, stream=True).raw)

inputs = processor(text=prompt, images=image, return_tensors="pt")

generated_ids = model.generate(
    input_ids=inputs["input_ids"],
    pixel_values=inputs["pixel_values"],
    max_new_tokens=1024,
    do_sample=False,
    num_beams=3
)
generated_text = processor.batch_decode(generated_ids, skip_special_tokens=False)[0]

parsed_answer = processor.post_process_generation(generated_text, task="<OD>", image_size=(image.width, image.height))

print(parsed_answer)

Advanced Usage

To visualize the results, you can refer to this tutorial notebook for more details.

📚 Documentation

Model Summary

TF-ID (Table/Figure IDentifier) is a family of object detection models finetuned to extract tables and figures in academic papers created by Yifei Hu. They come in four versions:

• The models were finetuned with papers from Hugging Face Daily Papers. All bounding boxes are manually annotated and checked by humans.
• TF-ID models take an image of a single paper page as the input, and return bounding boxes for all tables and figures in the given page.
• TF-ID-base and TF-ID-large draw bounding boxes around tables/figures and their caption text.
• TF-ID-base-no-caption and TF-ID-large-no-caption draw bounding boxes around tables/figures without their caption text.

Large models are always recommended!

Object Detection results format: {'<OD>': {'bboxes': [[x1, y1, x2, y2], ...], 'labels': ['label1', 'label2', ...]} }

Training Code and Dataset

• Dataset: yifeihu/TF-ID-arxiv-papers
• Code: github.com/ai8hyf/TF-ID

Benchmarks

We tested the models on paper pages outside the training dataset. The papers are a subset of huggingface daily paper.

Correct output - the model draws correct bounding boxes for every table/figure in the given page.

Depending on the use cases, some "incorrect" output could be totally usable. For example, the model draw two bounding boxes for one figure with two child components.

🔧 Technical Details

• Model Architecture: Based on the microsoft/Florence - 2 architecture, fine - tuned for table and figure extraction in academic papers.
• Training Data: Papers from Hugging Face Daily Papers, with all bounding boxes manually annotated.

📄 License

This project is licensed under the MIT License. You can find the full license text here.

BibTex and citation info

@misc{TF-ID,
  author = {Yifei Hu},
  title = {TF-ID: Table/Figure IDentifier for academic papers},
  year = {2024},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/ai8hyf/TF-ID}},
}