update file structure

README.md

@@ -24,6 +24,7 @@ size_categories:
 # 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 data.zip file and unzip it. 
 
 > **Folder layout**
 ```
@@ -56,40 +57,7 @@ ride_68496_8ef98b_20240716023032_517__1.png  # corresponding mask
 
 ## 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
+### A) Minimal PyTorch dataset
 
 ```python
 from pathlib import Path
@@ -110,7 +78,7 @@ class TraversabilityDataset(Dataset):
         return Image.open(ip).convert("RGB"), Image.open(mp).convert("L")
 ```
 
-### C) Pre‑processing notes for SAM‑2/SAM‑TP training
+### 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.