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---
license: mit
license_link: https://huggingface.co/microsoft/Florence-2-large/resolve/main/LICENSE
pipeline_tag: image-text-to-text
tags:
- vision
- ocr
- segmentation
datasets:
- yifeihu/TFT-ID-1.0-coco
---
# TFT-ID: Table/Figure/Text IDentifier for academic papers
## Model Summary
TFT-ID (Table/Figure/Text IDentifier) is an object detection model finetuned to extract tables, figures, and text sections in academic papers created by [Yifei Hu](https://x.com/hu_yifei).
TFT-ID is finetuned from [microsoft/Florence-2](https://huggingface.co/microsoft/Florence-2-large) checkpoints.
- The model was finetuned with papers from Hugging Face Daily Papers. All 36,000+ bounding boxes are manually annotated and checked by [Yifei Hu](https://x.com/hu_yifei).
- TFT-ID model takes an image of a single paper page as the input, and return bounding boxes for all tables, figures, and text sections in the given page.
- The text sections contain clean text content perfect for downstream OCR workflows. I recommend using **TB-OCR-preview-0.1** [[HF]](https://huggingface.co/yifeihu/TB-OCR-preview-0.1) as the OCR model to convert the text sections into clean markdown and math latex output.
Object Detection results format:
{'\<OD>': {'bboxes': [[x1, y1, x2, y2], ...],
'labels': ['label1', 'label2', ...]} }
## Training Code and Dataset
- Dataset: Coming soon.
- Code: [github.com/ai8hyf/TF-ID](https://github.com/ai8hyf/TF-ID)
## Benchmarks
The model was tested 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/text section in the given page and **does not missing any content**.
Task 1: Table, Figure, and Text Section Identification
| Model | Total Images | Correct Output | Success Rate |
|---------------------------------------------------------------|--------------|----------------|--------------|
| TFT-ID-1.0[[HF]](https://huggingface.co/yifeihu/TFT-ID-1.0) | 373 | 361 | 96.78% |
Task 2: Table and Figure Identification
| Model | Total Images | Correct Output | Success Rate |
|---------------------------------------------------------------|--------------|----------------|--------------|
| **TFT-ID-1.0**[[HF]](https://huggingface.co/yifeihu/TFT-ID-1.0) | 258 | 255 | **98.84%** |
| TF-ID-large[[HF]](https://huggingface.co/yifeihu/TF-ID-large) | 258 | 253 | 98.06% |
Note: 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.
## How to Get Started with the Model
Use the code below to get started with the model.
For non-CUDA environments, please check out this post for a simple patch: [https://huggingface.co/microsoft/Florence-2-base/discussions/4](https://huggingface.co/microsoft/Florence-2-base/discussions/4)
```python
import requests
from PIL import Image
from transformers import AutoProcessor, AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("yifeihu/TFT-ID-1.0", trust_remote_code=True)
processor = AutoProcessor.from_pretrained("yifeihu/TFT-ID-1.0", 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)
```
To visualize the results, see [this tutorial notebook](https://colab.research.google.com/github/roboflow-ai/notebooks/blob/main/notebooks/how-to-finetune-florence-2-on-detection-dataset.ipynb) for more details.
## 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}},
}
``` |