# COCO 2017 dataset http://cocodataset.org
# Download command: bash yolov5/data/get_coco2017.sh
# Train command: python train.py --data ./data/coco.yaml
# Dataset should be placed next to yolov5 folder:
#   /parent_folder
#     /coco
#     /yolov5


# train and val datasets (image directory or *.txt file with image paths)
train: /content/drive/My Drive/Colab Notebooks/yolov5/person/train.txt # 118k images
val: /content/drive/My Drive/Colab Notebooks/yolov5/person//val.txt # 5k images
test: /content/drive/My Drive/Colab Notebooks/yolov5/person//val.txt # 20k images for submission to https://competitions.codalab.org/competitions/20794

# number of classes
nc: 1

# class names
names:
- person

# Print classes
# with open('data/coco.yaml') as f:
#   d = yaml.load(f, Loader=yaml.FullLoader)  # dict
#   for i, x in enumerate(d['names']):
#     print(i, x)

