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---
dataset_name: SAM-TP Traversability Dataset
pretty_name: SAM-TP Traversability Dataset (Flattened)
tasks:
- image-segmentation
- semantic-segmentation
tags:
- robotics
- navigation
- traversability
- outdoor
- sam2
- bev
license: cc-by-4.0
annotations_creators:
- machine-assisted
- humans
language:
- en
size_categories:
- n<50K
---
# SAM‑TP Traversability Dataset
This repository contains pixel‑wise **traversability masks** paired with egocentric RGB images, prepared in a **flat, filename‑aligned** layout that is convenient for training SAM‑2 / SAM‑TP‑style segmentation models.
To use the dataset, simply download the sam2_flat_fold57.zip file and unzip it.
> **Folder layout**
```
.
├─ images/ # RGB frames (.jpg/.png). Filenames are globally unique.
├─ annotations/ # Binary masks (.png/.jpg). Filenames match images 1‑to‑1.
└─ manifest.csv # Provenance rows and any missing‑pair notes.
```
Each `annotations/<FILENAME>` is the mask for `images/<FILENAME>` (same filename, different folder).
---
## File naming
Filenames are made globally unique by concatenating the original subfolder path and the local stem with `__` separators, e.g.
```
ride_68496_8ef98b_20240716023032_517__1.jpg
ride_68496_8ef98b_20240716023032_517__1.png # corresponding mask
```
---
## Mask format
- Single‑channel binary masks; foreground = **traversable**, background = **non‑traversable**.
- Stored as `.png` or `.jpg` depending on source. If your pipeline requires PNG, convert on the fly in your dataloader.
- Values are typically `{0, 255}`. You can binarize via `mask = (mask > 127).astype(np.uint8)`.
---
## How to use
### A) Minimal PyTorch dataset
```python
from pathlib import Path
from PIL import Image
from torch.utils.data import Dataset
class TraversabilityDataset(Dataset):
def __init__(self, root):
root = Path(root)
self.img_dir = root / "images"
self.msk_dir = root / "annotations"
self.items = sorted([p for p in self.img_dir.iterdir() if p.is_file()])
def __len__(self):
return len(self.items)
def __getitem__(self, idx):
ip = self.items[idx]
mp = self.msk_dir / ip.name
return Image.open(ip).convert("RGB"), Image.open(mp).convert("L")
```
### B) Pre‑processing notes for SAM‑2/SAM‑TP training
- Resize/pad to your training resolution (commonly **1024×1024**) with masks aligned.
- Normalize images per your backbone’s recipe.
- If your trainer expects COCO‑RLE masks, convert PNG → RLE in the dataloader stage.
---
## Provenance & splits
- The dataset was flattened from mirrored directory trees (images and annotations) with 1‑to‑1 filename alignment.
- If you create explicit `train/val/test` splits, please add a `split` column to a copy of `manifest.csv` and contribute it back.
---
## License
Data: **CC‑BY‑4.0** (Attribution). See `LICENSE` for details.
---
## Citation
If you use this dataset in academic or industrial research, please cite the accompanying paper/report describing the data collection and labeling protocol:
> **GeNIE: A Generalizable Navigation System for In-the-Wild Environments**
> Available at: [https://arxiv.org/abs/2506.17960](https://arxiv.org/abs/2506.17960)
> Contains the SAM-TP traversability dataset and evaluation methodology.
```
@article{wang2025genie,
title = {GeNIE: A Generalizable Navigation System for In-the-Wild Environments},
author = {Wang, Jiaming and et al.},
journal = {arXiv preprint arXiv:2506.17960},
year = {2025},
url = {https://arxiv.org/abs/2506.17960}
}
```
```
@misc{sam_tp_dataset,
title = {SAM‑TP Traversability Dataset},
howpublished = {Hugging Face Datasets},
year = {2025},
note = {URL: https://huggingface.co/datasets/jamiewjm/sam-tp}
}
```
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