# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
# COCO128 dataset https://www.kaggle.com/ultralytics/coco128 (first 128 images from COCO train2017)
# Example usage: python train.py --data coco128.yaml
# parent
# ├── yolov5
# └── datasets
#     └── coco128  ← downloads here


# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
path: /scratch/labourde/yolo25000  # dataset root dir
train: ./images/  # train images (relative to 'path')
val: ./images/  # val images (relative to 'path')
test:  # test images (optional)

# Classes
nc: 4 # number of classes
names: ['Car', 'Cat', 'Dog', 'Flower'] # class names