Add pipeline tag and improve model card

README.md

@@ -1,26 +1,30 @@
 ---
 license: apache-2.0
+pipeline_tag: image-classification
+tags:
+- medical
+- surgical
+- endoscopy
 ---
 
 <div align="center">
   <img src="https://cdn-uploads.huggingface.co/production/uploads/67d9504a41d31cc626fcecc8/cE7UgFfJJ2gUHJr0SSEhc.png"> </img>
 </div>
 
-
-
-
 [📚 Paper](https://arxiv.org/abs/2503.19740) - [🤖 GitHub](https://github.com/visurg-ai/LEMON) 
 
-We provide the models used in our data curation pipeline in [📚 LEMON: A Large Endoscopic MONocular Dataset and Foundation Model for Perception in Surgical Settings](https://arxiv.org/abs/2503.19740) to assist with constructing the LEMON dataset (for more details about the LEMON dataset and our 
-LemonFM foundation model, please visit our github repository at [🤖 GitHub](https://github.com/visurg-ai/LEMON)) .
+This repository provides the models used in the data curation pipeline for the paper [LEMON: A Large Endoscopic MONocular Dataset and Foundation Model for Perception in Surgical Settings](https://arxiv.org/abs/2503.19740). These models assist in constructing the LEMON dataset by filtering and processing surgical video content. 
 
+For more details about the LEMON dataset and our LemonFM foundation model, please visit our [GitHub repository](https://github.com/visurg-ai/LEMON).
+
+## Citation
 
 If you use our dataset, model, or code in your research, please cite our paper:
 
-```
+```bibtex
 @misc{che2025lemonlargeendoscopicmonocular,
       title={LEMON: A Large Endoscopic MONocular Dataset and Foundation Model for Perception in Surgical Settings}, 
-      author={Chengan Che and Chao Wang and Tom Vercauteren and Sophia Tsoka and Luis C. Garcia-Peraza-Herrera},
+      author={Chengan Che and Chao Wang and Tom Vercauteren messenger, Sophia Tsoka and Luis C. Garcia-Peraza-Herrera},
       year={2025},
       eprint={2503.19740},
       archivePrefix={arXiv},
@@ -29,10 +33,9 @@ If you use our dataset, model, or code in your research, please cite our paper:
 }
 ```
 
+## Model Overview
 
-
-This Hugging Face repository includes video storyboard classification models, frame classification models, and non-surgical object detection models. The model loader file can be found at [model_loader.py](https://huggingface.co/visurg/Surg3M_curation_models/blob/main/model_loader.py)
-
+This Hugging Face repository includes video storyboard classification models, frame classification models, and non-surgical object detection models. The model loader file can be found at [model_loader.py](https://huggingface.co/visurg/Surg3M_curation_models/blob/main/model_loader.py).
 
 <div align="center">
 <table style="margin-left: auto; margin-right: auto;">
@@ -59,15 +62,16 @@ This Hugging Face repository includes video storyboard classification models, fr
 </table>
 </div>
 
-
 The data curation pipeline leading to the clean videos in the LEMON dataset is as follows:
 <div align="center">
   <img src="https://cdn-uploads.huggingface.co/production/uploads/67d9504a41d31cc626fcecc8/jzw36jlPT-V_I-Vm01OzO.png"> </img>
 </div>
 
-Usage
---------
-**Video classification models** are employed in the step **2** of the data curation pipeline to classify a video storyboard as either surgical or non-surgical, the models usage is as follows:
+## Usage
+
+### Video classification models
+**Video classification models** are employed in step **2** of the data curation pipeline to classify a video storyboard as either surgical or non-surgical:
+
    ```python
    import torch
    import torchvision
@@ -102,7 +106,8 @@ Usage
    outputs = net(img_tensor)
    ```
 
-**Frame classification models** are used in the step **3** of the data curation pipeline to classify a frame as either surgical or non-surgical, the models usage is as follows:
+### Frame classification models
+**Frame classification models** are used in step **3** of the data curation pipeline to classify a frame as either surgical or non-surgical:
 
    ```python
    import torch
@@ -137,7 +142,8 @@ Usage
    outputs = net(img_tensor)
    ```
 
-**Non-surgical object detection models** are used to obliterate the non-surgical region in the surgical frames (e.g. user interface information), the models usage is as follows:
+### Non-surgical object detection models
+**Non-surgical object detection models** are used to obliterate the non-surgical region in the surgical frames (e.g. user interface information):
 
    ```python
    import torch
@@ -170,4 +176,4 @@ Usage
 
    # Extract features from the image
    outputs = net(img_tensor)
-   ```
+   ```