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TF-ID-base

Image-Text-to-Text
Transformers
PyTorch
Safetensors
florence2
text-generation
vision
ocr
segmentation
custom_code
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metadata
license: mit
license_link: https://huggingface.co/microsoft/Florence-2-base-ft/resolve/main/LICENSE
pipeline_tag: image-text-to-text
tags:
  - vision
  - ocr
  - segmentation
  - coco

TF-ID: Table/Figure IDentifier for academic papers

Model Summary

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

Model Model size Model Description
TF-ID-base[HF] 0.23B Extract tables/figures and their caption text
TF-ID-large[HF] 0.77B Extract tables/figures and their caption text
TF-ID-base-no-caption[HF] 0.23B Extract tables/figures without caption text
TF-ID-large-no-caption[HF] 0.77B Extract tables/figures without caption text
All TF-ID models are finetuned from microsoft/Florence-2 checkpoints.

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.

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

How to Get Started with the Model

Use the code below to get started with the model.

import requests

from PIL import Image
from transformers import AutoProcessor, AutoModelForCausalLM 


model = AutoModelForCausalLM.from_pretrained("microsoft/Florence-2-base-ft", trust_remote_code=True)
processor = AutoProcessor.from_pretrained("microsoft/Florence-2-base-ft", trust_remote_code=True)

prompt = "<OD>"

url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/car.jpg?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)

BibTex and citation info 

@article{xiao2023florence,
  title={Florence-2: Advancing a unified representation for a variety of vision tasks},
  author={Xiao, Bin and Wu, Haiping and Xu, Weijian and Dai, Xiyang and Hu, Houdong and Lu, Yumao and Zeng, Michael and Liu, Ce and Yuan, Lu},
  journal={arXiv preprint arXiv:2311.06242},
  year={2023}
}