update readme.md

README.md

@@ -15,21 +15,14 @@ VisualHeist is an object detection model finetuned to extract tables and figures
 - visualheist-base[[HF]](https://huggingface.co/shixuanleong/visualheist-base) (0.23B)
 - visualheist-large[[HF]](https://huggingface.co/shixuanleong/visualheist-large) (0.77B)
 
-The models are finetuned from [microsoft/Florence-2](https://huggingface.co/microsoft/Florence-2-large-ft) checkpoints.
-VisualHeist is inspired by and adapted from [yifeihu/TF-ID](https://huggingface.co/yifeihu/TF-ID-large)
+**The base model is recommended if you are running it on low-RAM systems**
+
+The models are finetuned from [microsoft/Florence-2](https://huggingface.co/microsoft/Florence-2-large-ft) checkpoints. VisualHeist is inspired by and adapted from [yifeihu/TF-ID](https://huggingface.co/yifeihu/TF-ID-large)
 
 - The models were finetuned with 3435 figures and 1716 tables from 110 PDF articles across various publishers. All bounding boxes are manually annotated using [CoCo Annotator](https://github.com/jsbroks/coco-annotator).
 - TF-ID models take an image of a single paper page as the input, and return image files for all figures, schemes and tables in the given page. 
 
 
-**The base model is recommended if you are running it on low-RAM systems**
-
-![image/png](https://huggingface.co/yifeihu/TF-ID-base/resolve/main/td-id-caption.png)
-
-Object Detection results format: 
-{'\<OD>': {'bboxes': [[x1, y1, x2, y2], ...], 
-'labels': ['label1', 'label2', ...]} }
-
 ## Training Code and Dataset
 - Dataset: [Zenodo repository](https://doi.org/10.5281/zenodo.14917752)
 - Code: [github.com/aspuru-guzik-group/mermaid](https://github.com/aspuru-guzik-group/mermaid)
@@ -43,7 +36,9 @@ and science education. These PDFs, published between 1949 and 2025, include both
 We also additionally curated another collection of 98 literature articles (MERMaid-100) reporting novel reaction methodologies that spans 
 three distinct chemical domains: organic electrosynthesis, photocatalysis, and organic synthesis. 
 
-The full DOI lists can be downloaded from our [Zenodo repository](https://doi.org/10.5281/zenodo.14917752).
+Additional performance discussion can be found from our [preprint article](XXXXXXX) 
+
+The full DOI lists can be downloaded from our[Zenodo repository](https://doi.org/10.5281/zenodo.14917752).
 
 The evaluation results for visualheist-large are: 
 |                                                       | Total Images | F1 score |
@@ -55,48 +50,13 @@ The evaluation results for visualheist-large are:
 | MERMaid-100   | 100          | 99%         |
 
  
-## How to Get Started with the Model
+## Running the Model
 
-Use the code below to get started with the model.
-
-```python
-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)
-```
+Refer to our [github repository](https://github.com/aspuru-guzik-group/mermaid) for detailed instructions on how to run the model 
 
-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}},
-}
+<To be updated with our archive citation> 
 ```