File size: 4,338 Bytes
8b0eabc |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 |
---
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.
> **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) Load with `datasets` (ImageFolder‑style)
```python
from datasets import load_dataset
from pathlib import Path
from PIL import Image
REPO = "jamiewjm/sam-tp" # e.g. "jamiewjm/sam-tp"
ds_imgs = load_dataset(
"imagefolder",
data_dir=".",
data_files={"image": f"hf://datasets/{REPO}/images/**"},
split="train",
)
ds_msks = load_dataset(
"imagefolder",
data_dir=".",
data_files={"mask": f"hf://datasets/{REPO}/annotations/**"},
split="train",
)
# Build a mask index by filename
mask_index = {Path(r["image"]["path"]).name: r["image"]["path"] for r in ds_msks}
row = ds_imgs[0]
img_path = Path(row["image"]["path"])
msk_path = Path(mask_index[img_path.name])
img = Image.open(img_path).convert("RGB")
msk = Image.open(msk_path).convert("L")
```
### B) 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")
```
### C) 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}
}
```
|