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README.md
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---
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dataset_size: 5939257990
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configs:
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- config_name: default
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data_files:
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- split: all
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path: data/all-*
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---
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---
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dataset_info:
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features:
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- name: image
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dtype: image
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- name: mask
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dtype: image
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- name: scene
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dtype: string
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- name: filename
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dtype: string
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- name: relpath
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dtype: string
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splits:
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- name: all
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num_bytes: 5939257990
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num_examples: 7436
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download_size: 5733614565
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dataset_size: 5939257990
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configs:
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- config_name: default
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data_files:
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- split: all
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path: data/all-*
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task_categories:
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- fill-mask
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language:
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- en
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size_categories:
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- 1B<n<10B
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---
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# RUGD (Unofficial Mirror)
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> **Unofficial mirror of the RUGD dataset. I am *not* an author or owner of RUGD. All credit to the original creators.**
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The **RUGD** dataset contains outdoor, unstructured environments for autonomous navigation and visual perception research (e.g., semantic segmentation and scene understanding).
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This repository re-hosts the original files **as a convenience mirror** for researchers who may have trouble accessing the official hosting due to connectivity or browser warnings.
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**Note on the official site:** the original download page has, at times, been served over plain HTTP or with configurations that some browsers flag as “Not secure.” This mirror is provided to make access easier; please prefer the official source when it works for you.
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---
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## Attribution & Citation
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If you use this dataset, **please cite the original paper**:
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```bibtex
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@inproceedings{RUGD2019IROS,
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author = {Wigness, Maggie and Eum, Sungmin and Rogers, John G and Han, David and Kwon, Heesung},
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title = {A RUGD Dataset for Autonomous Navigation and Visual Perception in Unstructured Outdoor Environments},
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booktitle = {International Conference on Intelligent Robots and Systems (IROS)},
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year = {2019}
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}
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```
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**Authors:** Maggie Wigness, Sungmin Eum, John G. Rogers, David Han, Heesung Kwon
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**Conference:** IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2019
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---
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## What’s in this mirror
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* Original RUGD archives/directories re-hosted for convenience.
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* File names and structure preserved where possible.
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* No modifications to the data itself.
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> If you’re an RUGD maintainer and would like this mirror updated or removed, please open a Discussion on this repo or contact me and I will promptly comply.
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---
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## License & Usage
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This mirror **does not grant any license**. Usage is governed by the **original RUGD terms**. Before using the data, **review and comply with the upstream license/terms and any restrictions (e.g., research/non-commercial)**. If any license file was included upstream, it is mirrored here unchanged.
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---
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## How to load (examples)
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### Option A — Use 🤗 `imagefolder`
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If this repo is organized as folders of images (and optional labels), you can leverage the generic loader:
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```python
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from datasets import load_dataset
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# Replace "your-username/rugd" with the actual repo name
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ds = load_dataset(
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"imagefolder",
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data_dir="hf://datasets/your-username/rugd",
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split="train" # or "validation"/"test" if you provide splits
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)
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# Access a sample
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sample = ds[0]
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image = sample["image"] # PIL.Image
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```
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If you provide segmentation masks in a parallel folder (e.g., `images/` and `annotations/`), consider adding a small dataset script or a `DatasetDict` mapping so users get `{"image": ..., "label": ...}` pairs directly.
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### Option B — Stream raw files
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If you just want to iterate over files as artifacts:
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```python
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from huggingface_hub import hf_hub_download, list_repo_files
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repo_id = "your-username/rugd"
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files = list_repo_files(repo_id=repo_id, repo_type="dataset")
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# Download a specific file (example)
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path = hf_hub_download(repo_id=repo_id, repo_type="dataset", filename=files[0])
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```
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---
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## Intended tasks (non-exhaustive)
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* **Semantic Segmentation**
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* **Scene Understanding / Traversability**
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* **Perception for UGV/Off-road Robotics**
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> For class definitions, color maps, and evaluation protocols, please refer to the original RUGD documentation/paper.
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---
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## Provenance & Integrity
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* Source: Official RUGD release (re-hosted).
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* Files are mirrored “as-is.” If upstream checksums are available, you should verify them against the mirrored content.
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---
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## Acknowledgements
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All credit and thanks to the RUGD authors and their institutions for creating and releasing this dataset to the community.
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---
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## Contact / Takedown
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If you represent the original authors/rights holders and want this mirror changed or removed, please open a Discussion here or contact me directly and I��ll address it quickly.
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---
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**Disclaimer:** This repository is **only a mirror** to improve accessibility for researchers. I make **no claim of authorship** and **no warranty** about the content. Always consult and follow the original dataset’s terms.
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