Instructions to use yifeihu/TF-ID-large-no-caption with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use yifeihu/TF-ID-large-no-caption with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="yifeihu/TF-ID-large-no-caption", trust_remote_code=True)

# Load model directly
from transformers import AutoProcessor, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("yifeihu/TF-ID-large-no-caption", trust_remote_code=True)
model = AutoModelForImageTextToText.from_pretrained("yifeihu/TF-ID-large-no-caption", trust_remote_code=True)

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use yifeihu/TF-ID-large-no-caption with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "yifeihu/TF-ID-large-no-caption"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "yifeihu/TF-ID-large-no-caption",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/yifeihu/TF-ID-large-no-caption

SGLang

How to use yifeihu/TF-ID-large-no-caption with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "yifeihu/TF-ID-large-no-caption" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "yifeihu/TF-ID-large-no-caption",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "yifeihu/TF-ID-large-no-caption" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "yifeihu/TF-ID-large-no-caption",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use yifeihu/TF-ID-large-no-caption with Docker Model Runner:
```
docker model run hf.co/yifeihu/TF-ID-large-no-caption
```

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 created by Yifei Hu. 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] (Recommended)	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] (Recommended)	0.77B	Extract tables/figures without caption text
All TF-ID models are finetuned from microsoft/Florence-2 checkpoints.

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.

Model	Total Images	Correct Output	Success Rate
TF-ID-base[HF]	258	251	97.29%
TF-ID-large[HF]	258	253	98.06%

Model	Total Images	Correct Output	Success Rate
TF-ID-base-no-caption[HF]	261	253	96.93%
TF-ID-large-no-caption[HF]	261	254	97.32%

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.

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)

To visualize the results, see this tutorial notebook 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}},
}

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Safetensors

Model size

0.8B params

Tensor type

F32

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Image-Text-to-Text

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TF-ID-large-no-caption