Updated README.md

README.md

@@ -1,3 +1,85 @@
----
-license: mit
----
+---
+license: mit
+task_categories:
+  - visual-document-retrieval
+tags:
+  - real-data
+  - lecture-slides
+  - document-analysis
+---
+
+# RealSlide: Benchmark for Lecture Slide Analysis
+
+This repository contains the RealSlide benchmark dataset, a collection of real lecture slides curated to evaluate models for slide element detection and text query-based slide retrieval. The dataset complements the synthetic dataset generated by the [SynLecSlideGen pipeline](https://github.com/synslidegen/synslidegen_pipeline), as presented in the paper [AI-Generated Lecture Slides for Improving Slide Element Detection and Retrieval](https://huggingface.co/papers/2506.23605). It is designed to test the generalization of models trained on synthetic data to real-world lecture slides.
+
+* Project page: [https://synslidegen.github.io/](https://synslidegen.github.io/)
+<!-- * Code (generation pipeline): [https://github.com/synslidegen/synslidegen_pipeline](https://github.com/synslidegen/synslidegen_pipeline) -->
+<!-- * Dataset repository (on GitHub): [https://github.com/synslidegen/realslide_dataset](https://github.com/synslidegen/realslide_dataset) -->
+
+## How to Download:
+
+### Using Git via terminal
+```bash
+git lfs install
+git clone https://huggingface.co/datasets/nerdyvisky/realslide
+```
+
+### Using Python
+```python
+pip install huggingface_hub
+python
+from huggingface_hub import snapshot_download
+
+repo_id = "nerdyvisky/realslide"  # your full repo path
+local_dir = snapshot_download(repo_id=repo_id, repo_type="dataset")
+```
+
+## Overview of RealSlide Benchmark
+
+The RealSlide benchmark consists of 1050 real-world lecture slides collected from Creative-Commons licensed graduate lecture slide decks. Full list [here](https://docs.google.com/spreadsheets/d/1bX05zEv0hyZ-FAvmyTfdMi8pdsPqv2DQGB_AHmIJIzk/edit?usp=sharing) 
+Each slide is manually annotated by human-annotators with Slide Object Detection in COCO Format and Text-based slide summary to aid benchmarking VLMs for Slide Image related tasks.
+
+
+<!-- ![Overview of RealSlide](https://raw.githubusercontent.com/synslidegen/synslidegen_pipeline/main/code/assets/realslide_overview.jpg) -->
+
+## Dataset Components
+
+The dataset includes samples for two main tasks, each with manually verified annotations:
+
+<!-- * **RealDet (Slide Element Detection):** Contains real lecture slides with annotations for various elements within slides (e.g., titles, text, images). -->
+<!-- * **RealRet (Text Query-based Slide Retrieval):** Contains real lecture slides suitable for retrieval tasks based on text queries, enabling models to retrieve relevant slides based on textual content. -->
+
+<!-- ### RealDet Samples -->
+<!-- <table border="1"> -->
+<!-- <tr> -->
+<!-- <td><img src="https://raw.githubusercontent.com/synslidegen/synslidegen_pipeline/main/code/assets/realdet1.png" alt="RealDet1" width="100%"></td> -->
+<!-- <td><img src="https://raw.githubusercontent.com/synslidegen/synslidegen_pipeline/main/code/assets/realdet2.png" alt="RealDet2" width="100%"></td> -->
+<!-- <td><img src="https://raw.githubusercontent.com/synslidegen/synslidegen_pipeline/main/code/assets/realdet3.png" alt="RealDet3" width="100%"></td> -->
+<!-- </tr> -->
+<!-- </table> -->
+
+<!-- ### RealRet Samples -->
+<!-- <table border="1"> -->
+<!-- <tr> -->
+<!-- <td><img src="https://raw.githubusercontent.com/synslidegen/synslidegen_pipeline/main/code/assets/realret1.png" alt="RealRet1" width="100%"></td> -->
+<!-- <td><img src="https://raw.githubusercontent.com/synslidegen/synslidegen_pipeline/main/code/assets/realret2.png" alt="RealRet2" width="100%"></td> -->
+<!-- <td><img src="https://raw.githubusercontent.com/synslidegen/synslidegen_pipeline/main/code/assets/realret3.png" alt="RealRet3" width="100%"></td> -->
+<!-- </tr> -->
+<!-- </table> -->
+
+## Usage
+
+This dataset can be used for evaluating models trained on synthetic datasets or for fine-tuning models for lecture slide element detection and retrieval. The data is provided with manually verified annotations, making it suitable for benchmarking and performance evaluation.
+
+## Citation
+
+If you use this dataset in your research, please cite the corresponding paper:
+
+```bibtex
+@article{maniyar2025ai,
+  title={AI-Generated Lecture Slides for Improving Slide Element Detection and Retrieval},
+  author={Maniyar, Suyash and Trivedi, Vishvesh and Mondal, Ajoy and Mishra, Anand and Jawahar, CV},
+  journal={arXiv preprint arXiv:2506.23605},
+  year={2025}
+}
+```