SAR/data/CT-seg-cls4.yaml

# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
# COCO128-seg dataset https://www.kaggle.com/ultralytics/coco128 (first 128 images from COCO train2017) by Ultralytics
# Example usage: python train.py --data coco128.yaml
# parent
# ├── yolov5
# └── datasets
#     └── coco128-seg  ← downloads here (7 MB)


# 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: D:/data/data_yolo/ # dataset root dir
#train: D:\hiddz\CT\测试数据\images  # train images (relative to 'path') 128 images
#val: ./images/val  # val images (relative to 'path') 128 images

# Classes
#names:
#  0: crack
#  1: hole
 # 2: debonding
  #3: rarefaction
#  0: 裂纹
#  1: 孔洞
#  2: 脱毡
#  3: 裂纹
#  4: 孔洞
#  5: 脱毡
#  6: 疏松

path: 'C:\Users\Administrator\Desktop\SAR\'
test:
train: data\HRSID_YOLO\train
val: data\HRSID_YOLO\val


names:
 0: Ship