Updated readme.md

README.md

@@ -1,16 +1,49 @@
 ---
+pretty_name: K600 Frozen Test Clips (for GRW Smoothing)
+language: en
 license: other
+task_categories:
+  - video-classification
+tags:
+  - kinetics-600
+  - video
+  - webdataset
+  - evaluation
 ---
 
-# K600 Test Set (NeurIPS 2025)
+# K600 Frozen Test Clips (for reproducible evaluation)
 
-This repository contains the test set tarball used in our NeurIPS 2025 paper.
+This repository provides a **frozen** set of extracted clips used for evaluation in the paper:
 
-## Files
-- `k600_test_ds.tar.gz`
+## Paper
+- https://huggingface.co/papers/2511.20928
+- https://arxiv.org/abs/2511.20928
 
-## Related
-- Model weights: https://huggingface.co/DrGil/grw-smoothing-movinet
+## Why this dataset exists
+Kinetics videos are hosted on YouTube, and availability can change over time. This dataset exists to provide a **stable evaluation set** aligned with the paper’s experiments.
+
+## What’s included
+- `k600_test_ds.tar.gz` — an archive containing extracted test clips organized by class.
+
+## Code (reproduction instructions)
+For the **exact, step-by-step instructions** to reproduce results (expected directory layout, caching, and evaluation pipeline), please refer to:
+- https://github.com/cmusatyalab/grw-smoothing
+
+## Related model weights
+- https://huggingface.co/DrGil/grw-smoothing-movinet
+
+## Licensing / redistribution note
+This dataset is marked as `other` because it is derived from Kinetics/YouTube-sourced videos and may involve varying rights. Please ensure your usage complies with the original rights and applicable terms.
+
+## Citation
+If you use this dataset, please cite:
+
+```bibtex
+@inproceedings{goldman2025grwsmoothing,
+  title     = {Smooth Regularization for Efficient Video Recognition},
+  author    = {Gil Goldman and Raja Giryes and Mahadev Satyanarayanan},
+  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
+  year      = {2025},
+  url       = {https://arxiv.org/abs/2511.20928}
+}
 
-## Notes
-Please ensure you comply with the original dataset/video licensing and redistribution terms.