docs: add DADA-seg dataset card

README.md

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+---
+annotations_creators:
+- expert-generated
+language_creators:
+- crowdsourced
+language:
+- en
+license: apache-2.0
+multilinguality:
+- monolingual
+size_categories:
+- 1K<n<10K
+source_datasets:
+- original
+task_categories:
+- image-segmentation
+task_ids:
+- semantic-segmentation
+pretty_name: DADA-seg
+tags:
+- multimodal
+- autonomous-driving
+- event-camera
+- accident-scene
+- semantic-segmentation
+- iros-2021
+- tits-2021
+---
+
+# DADA-seg: Multimodal Accident Scene Segmentation Benchmark
+
+[IROS 2021 & IEEE T-ITS 2021] Official Dataset Repository for the **DADA-seg** accident scene segmentation benchmark.
+
+- **GitHub Repository:** https://github.com/InSAI-Lab/ISSAFE
+- **Model Checkpoints:** [InSAI-Lab/ISSAFE](https://huggingface.co/InSAI-Lab/ISSAFE)
+- **Collection:** [ISSAFE & EDCNet (IROS 2021 & T-ITS 2021)](https://huggingface.co/collections/InSAI-Lab/issafe-and-edcnet-iros-2021-and-t-its-2021-6ad1b2429101a4c06509e4cb)
+- **Paper (IROS 2021):** [ISSAFE: Improving Semantic Segmentation in Accidents by Fusing Event-based Data](https://arxiv.org/abs/2008.08974)
+- **Paper (IEEE T-ITS 2021):** [Exploring Event-Driven Dynamic Context for Accident Scene Segmentation](https://arxiv.org/abs/2112.05006)
+
+---
+
+## Dataset Description
+
+Ensuring traffic safety requires robust environmental perception even under severe accident scenarios. Most critical traffic accident scenes involve extreme dynamics, high-speed motions, vehicle rollovers, collisions, and motion blur, causing conventional frame-based RGB segmentation models to degrade drastically.
+
+**DADA-seg** is a pixel-wise annotated multimodal benchmark selected from DADA-2000. It provides paired:
+- **RGB image frames** (anchor frames and preceding frames)
+- **High-temporal event camera volume representations** (`.npz` files with temporal bins)
+- **Fine-grained pixel-level semantic ground truth** annotations
+
+The validation set contains **313 carefully verified and annotated accident scenes** across challenging traffic conditions.
+
+## File Structure
+
+The complete dataset archive `DADA-seg.tar.gz` (~448 MB) unpacks into the following standard directory structure:
+
+```text
+DADA_seg/
+├── event/
+│   └── val/              # Event volume tensors (.npz)
+├── gtFine/
+│   └── val/              # Pixel-level semantic annotations (19 Cityscapes-compatible classes)
+└── leftImg8bit/
+    └── val/              # Corresponding RGB image frames
+```
+
+In addition to `DADA-seg.tar.gz`, modular archives are also provided:
+- `dada_val_img_label.tar.gz` (~47 MB): Contains `leftImg8bit/val` and `gtFine/val`.
+- `dada_val_event.tar.gz` (~402 MB): Contains `event/val` volume tensors.
+- `dada_val_label_only.zip` (~2.8 MB): Contains only ground truth semantic masks (`gtFine/val`).
+
+## Benchmark Results
+
+Evaluation on DADA-seg demonstrates that incorporating event-driven dynamic context brings **+8.2% mIoU gain** over RGB-only baselines and outperforms >20 competitive segmentation architectures under accident scenarios.
+
+| Method | Modality | Backbone | DADA-seg mIoU (%) | Gain over RGB |
+| :--- | :---: | :---: | :---: | :---: |
+| ResNet Baseline | RGB | ResNet-18 | 29.8% | - |
+| ISSAFE (IROS 2021) | RGB + Event | ResNet-18 | 34.5% | +4.7% |
+| **EDCNet (T-ITS 2021)** | **RGB + Event ($B=2$)** | **ResNet-18** | **38.0%** | **+8.2%** |
+
+## Quick Start & Download
+
+Using the Hugging Face CLI:
+
+```bash
+# Download complete dataset archive
+hf download InSAI-Lab/DADA-seg DADA-seg.tar.gz --type dataset --local-dir ./dataset
+
+# Extract into dataset directory
+cd ./dataset
+tar -zxvf DADA-seg.tar.gz
+```
+
+Using Python (`huggingface_hub`):
+
+```python
+from huggingface_hub import hf_hub_download
+
+dataset_path = hf_hub_download(
+    repo_id="InSAI-Lab/DADA-seg",
+    repo_type="dataset",
+    filename="DADA-seg.tar.gz",
+    local_dir="./dataset"
+)
+```
+
+## Citation
+
+```bibtex
+@inproceedings{zhang2021issafe,
+  author={Zhang, Jiaming and Yang, Kailun and Stiefelhagen, Rainer},
+  booktitle={2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)}, 
+  title={ISSAFE: Improving Semantic Segmentation in Accidents by Fusing Event-based Data}, 
+  year={2021},
+  pages={1132-1139},
+  doi={10.1109/IROS51168.2021.9636109}
+}
+
+@article{zhang2021edcnet,
+  author={Zhang, Jiaming and Yang, Kailun and Stiefelhagen, Rainer},
+  journal={IEEE Transactions on Intelligent Transportation Systems}, 
+  title={Exploring Event-Driven Dynamic Context for Accident Scene Segmentation}, 
+  year={2021},
+  pages={1-17},
+  doi={10.1109/TITS.2021.3134828}
+}
+```
+
+## License
+
+This dataset is released under the [Apache-2.0 License](https://www.apache.org/licenses/LICENSE-2.0).