|
|
--- |
|
|
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} |
|
|
} |
|
|
``` |
|
|
|
