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--- |
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license: mit |
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license_link: https://huggingface.co/microsoft/Florence-2-base-ft/resolve/main/LICENSE |
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pipeline_tag: image-text-to-text |
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tags: |
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- vision |
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- ocr |
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- segmentation |
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- coco |
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--- |
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# TF-ID: Table/Figure IDentifier for academic papers |
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## Model Summary |
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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: |
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| Model | Model size | Model Description | |
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| ------- | ------------- | ------------- | |
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| TF-ID-base[[HF]](https://huggingface.co/yifeihu/TF-ID-base) | 0.23B | Extract tables/figures and their caption text |
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| TF-ID-large[[HF]](https://huggingface.co/yifeihu/TF-ID-large) | 0.77B | Extract tables/figures and their caption text |
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| TF-ID-base-no-caption[[HF]](https://huggingface.co/yifeihu/TF-ID-base-no-caption) | 0.23B | Extract tables/figures without caption text |
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| TF-ID-large-no-caption[[HF]](https://huggingface.co/yifeihu/TF-ID-large-no-caption) | 0.77B | Extract tables/figures without caption text |
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All TF-ID models are finetuned from [microsoft/Florence-2](https://huggingface.co/microsoft/Florence-2-large-ft) checkpoints. |
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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. |
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TF-ID-base and TF-ID-large draw bounding boxes around tables/figures and their caption text. |
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TF-ID-base-no-caption and TF-ID-large-no-caption draw bounding boxes around tables/figures without their caption text. |
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Object Detection results format: |
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{'\<OD>': {'bboxes': [[x1, y1, x2, y2], ...], |
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'labels': ['label1', 'label2', ...]} } |
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## How to Get Started with the Model |
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Use the code below to get started with the model. |
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```python |
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import requests |
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from PIL import Image |
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from transformers import AutoProcessor, AutoModelForCausalLM |
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model = AutoModelForCausalLM.from_pretrained("microsoft/Florence-2-base-ft", trust_remote_code=True) |
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processor = AutoProcessor.from_pretrained("microsoft/Florence-2-base-ft", trust_remote_code=True) |
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prompt = "<OD>" |
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url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/car.jpg?download=true" |
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image = Image.open(requests.get(url, stream=True).raw) |
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inputs = processor(text=prompt, images=image, return_tensors="pt") |
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generated_ids = model.generate( |
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input_ids=inputs["input_ids"], |
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pixel_values=inputs["pixel_values"], |
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max_new_tokens=1024, |
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do_sample=False, |
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num_beams=3 |
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) |
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generated_text = processor.batch_decode(generated_ids, skip_special_tokens=False)[0] |
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parsed_answer = processor.post_process_generation(generated_text, task="<OD>", image_size=(image.width, image.height)) |
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print(parsed_answer) |
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``` |
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## BibTex and citation info |
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``` |
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@article{xiao2023florence, |
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title={Florence-2: Advancing a unified representation for a variety of vision tasks}, |
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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}, |
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journal={arXiv preprint arXiv:2311.06242}, |
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year={2023} |
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} |
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``` |
