Add metadata, paper link, and GitHub repository

README.md

@@ -1,4 +1,7 @@
 ---
+license: cc-by-nc-4.0
+task_categories:
+- image-text-to-text
 dataset_info:
   features:
   - name: image_id
@@ -27,3 +30,33 @@ configs:
   - split: train
     path: data/train-*
 ---
+
+# SketchVLM: Physics Ball Drop Dataset
+
+This dataset is part of the **SketchVLM** framework, which enables Vision-Language Models (VLMs) to produce non-destructive, editable SVG overlays on input images to visually explain their reasoning. The **Physics Ball Drop** benchmark specifically evaluates a model's ability to predict the trajectory of a ball through obstacles (based on PHYRE).
+
+- **Paper:** [SketchVLM: Vision language models can annotate images to explain thoughts and guide users](https://huggingface.co/papers/2604.22875)
+- **Project Page:** [https://sketchvlm.github.io/](https://sketchvlm.github.io/)
+- **GitHub Repository:** [https://github.com/Brandon-Collins7/sketchvlm](https://github.com/Brandon-Collins7/sketchvlm)
+
+### Sample Usage
+
+To download the dataset using the Hugging Face CLI, run:
+
+```bash
+huggingface-cli download loganbolton/sketchvlm-physics-ball-drop --repo-type dataset --local-dir datasets/ball_drop
+```
+
+### Citation
+
+```bibtex
+@misc{collins2026sketchvlmvisionlanguagemodels,
+      title={SketchVLM: Vision language models can annotate images to explain thoughts and guide users},
+      author={Brandon Collins and Logan Bolton and Hung Huy Nguyen and Mohammad Reza Taesiri and Trung Bui and Anh Totti Nguyen},
+      year={2026},
+      eprint={2604.22875},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV},
+      url={https://arxiv.org/abs/2604.22875},
+}
+```