Update README.md

README.md

@@ -20,7 +20,7 @@ annotations_creators:
 
 # SWiM: Spacecraft With Masks
 
-A large-scale instance segmentation dataset of nearly 64k annotated spacecraft images that was created using real spacecraft models, superimposed on a mixture of real and synthetic backgrounds generated using NASA's TTALOS pipeline. To mimic camera distortions and noise in real-world image acquisition, we also added different types of noise and distortion to the images.
+A large-scale instance segmentation dataset of nearly 64k annotated spacecraft images created using real spacecraft models, superimposed on a mixture of real and synthetic backgrounds generated using NASA's TTALOS pipeline. To mimic camera distortions and noise in real-world image acquisition, we added different types of noise and distortion.
 
 ## Dataset Summary
 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.
@@ -43,7 +43,7 @@ Due to Hugging Face Hub's per-directory file limit (10,000 files), this dataset
               └── ...
 
 ```
-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**.
+If you're using models/tools like **YOLO** or others that expect a **flat directory**, kindly use the --flatten argument provided in our utils/download_swim.py script.
 
 **YOLO Example Structure:**
 ```
@@ -59,7 +59,7 @@ If you're using models/tools like **YOLO** or others that expect a **flat direct
 
 ### Utility Scripts
 
-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. Note, the scripts are in the utils subdirectory.
+The following scripts help you download 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. Note, the scripts are in the utils subdirectory.
 
 
 #### 1. Setup
@@ -75,7 +75,7 @@ Create your virtual environment to help manage dependencies and prevent conflict
 
 #### 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.
+This script is helpful for quick local inspection, prototyping, or lightweight evaluation without downloading the full dataset.
 
 Usage:
     python3 utils/sample_swim.py --output-dir ./samples --count 100
@@ -105,7 +105,7 @@ Features:
 - 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.
+You can use this script to download the complete dataset for model training or offline access.
 
 Usage:
     # Download all chunks (flattened/ YOLO format)