add data prep script clarification

README.md

@@ -129,7 +129,7 @@ results[0].plot()
 
 ### Training Data
 
-The three datasets are available in the [MMLA Data Collection](https://huggingface.co/collections/imageomics/mmla). See `prepare_yolo_dataset.py` for details on train/test splits.
+The three datasets are available in the [MMLA Data Collection](https://huggingface.co/collections/imageomics/mmla). See `prepare_yolo_dataset.py` for details on train/test splits; the script runs on standard Python 3.10+ packages, and generates the splits.
 
 #### Dataset splitting strategy
 We applied a stratified 60/40 train-test split across species and locations to evaluate model generalizability. Data was collected from three distinct environments: Mpala Research Centre (location_1), Ol Pejeta Conservancy (location_2), and The Wilds Conservation Center (location_3). The dataset includes four target classes: Zebra, Giraffe, Onager, and African Wild Dog.
@@ -189,7 +189,7 @@ results = model.train(
 
 #### Testing Data
 
-The model was evaluated on a held-out test set located at `images/test` containing:
+The model was evaluated on a held-out test set located at `images/test` (created by running the [data prep script](https://huggingface.co/imageomics/mmla/blob/main/prepare_yolo_dataset.py)) containing:
 - 7658 test images with instances of Zebra, Giraffe, Onager, and Dog