Update README.md

README.md

@@ -26,38 +26,70 @@ A large-scale instance segmentation dataset of nearly 64k annotated spacecraft i
 The dataset contains over 63,917 annotated images with instance masks for varied spacecraft. It's structured for YOLO and segmentation applications, and chunked to stay within Hugging Face's per-folder file limits.
 
 
-## How to Use
+## How to Use/Download
 ### Directory Structure Note
 
 Due to Hugging Face Hub's per-directory file limit (10,000 files), this dataset is chunked: each logical split (like `train/labels/`) is subdivided into folders (`000/`, `001/`, ...) containing no more than 5,000 files each.
 
 **Example Structure:**
+'''
+  Baseline
+    ├──train/
+         ├──images/
+              ├── 000/
+              │   ├── img_0.png
+              │   └── ...
+              ├── 001/
+              └── ...
+
+'''
+If you're using models/tools like **YOLO** or others that expect a **flat directory**, you may need to **merge these subfolders at load-time or during preprocessing**.
+
+**YOLO Example Structure:**
+'''
+  Baseline
+    ├──train/
+         ├──images/
+              ├── img_0.png
+              ├── imag_99.png
+              └── ...
 
-    labels/
-      ├── 000/
-      │   ├── img_0.png
-      │   └── ...
-      ├── 001/
-      └── ...
+'''
       
-If you're using models/tools like **YOLO** or others that expect a **flat directory**, you may need to **merge these subfolders at load-time or during preprocessing**.
 
-## Utility Scripts
+### Utility Scripts
 
-### 1. Setup
+The following scripts help you with the download of this dataset. Due to the large nature of the data and the custom directory structure, it is recommended to use the following scripts to either sample or to download the entire dataset.
 
-Create your virtual environment:
 
+#### 1. Setup
+
+Create your virtual environment to help manage dependencies and prevent conflicts:
+'''
   python -m venv env
   
   source env/bin/activate # On Windows: env\Scripts\activate
   
   pip install -r requirements.txt
+'''
+
+#### 2. Sample 500 items from a specific chunk:
+
+This script is useful for quick local inspection, prototyping, or lightweight evaluation without downloading the full dataset.
 
+Usage:
+    python3 utils/sample_swim.py --output-dir ./samples --count 100
 
-### 2. Sample 500 items from a specific chunk:
+Arguments:
+    --repo-id          Hugging Face dataset repository ID
+    --image-subdir     Path to image subdirectory inside the dataset repo
+    --label-subdir     Path to corresponding label subdirectory
+    --output-dir       Directory to save downloaded files
+    --count            Number of samples to download
 
-  python sample_swim.py
+Example Usage with all args:
+'''
+  python3 sample_swim.py
   
   --repo-id JeffreyJsam/SWiM-SpacecraftWithMasks
   
@@ -68,10 +100,36 @@ Create your virtual environment:
   --output-dir ./Sampled-SWiM
   
   --count 500
+'''
+#### 3. Download the entire dataset (optionally flatten chunks for YOLO format):
 
-### 3. Download the entire dataset (optionally flatten chunks):
+Streams and downloads the full paired dataset (images + label txt files) from a Hugging Face Hub repository. It recursively processes all available chunk subfolders (e.g., '000', '001', ...) under given parent paths.
 
-  python download_swim.py
+Features:
+- Recursively discovers subdirs (chunks) using HfFileSystem
+- Optionally flattens the directory structure by removing the deepest chunk level
+- Saves each .png image with its corresponding .txt label
+
+Use this script if you want to download the complete dataset for model training or offline access.
+
+Usage:
+    # Download all chunks (flattened/ YOLO format)
+    python utils/download_swim.py --output-dir ./SWiM --flatten
+
+    # Download specific chunks
+    python3 utils/download_swim.py --chunks 000 001 002 --flatten False
+
+Arguments:
+    --repo-id          Hugging Face dataset repository ID
+    --images-parent    Parent directory for image chunks (e.g., Baseline/images/train)
+    --labels-parent    Parent directory for label chunks (e.g., Baseline/labels/train)
+    --output-dir       Where to save the downloaded dataset
+    --flatten          Remove final 'chunk' subdir in output paths (default: True)
+    --chunks           Specific chunk names (e.g., 000 001); omit to download all
+
+Example usage with all args:
+'''
+  python3 download_swim.py
   
   --repo-id JeffreyJsam/SWiM-SpacecraftWithMasks
   
@@ -82,7 +140,7 @@ Create your virtual environment:
   --output-dir ./SWiM
   
   --flatten
-
+'''
   
 **Arguments are all configurable—see `--help` for details.**