train: 
- /workspace2/yolo_dataset/train_4_with_small_lab/train/images
- /workspace2/yolo_dataset/2025-01-15-sea-salt_81/train/images
val:
- /workspace2/yolo_dataset/train_4_with_small_lab/valid/images
- /workspace2/yolo_dataset/2025-01-15-sea-salt_81/valid/images

nc: 80
names: ['person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light',
        'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow',
        'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee',
        'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard',
        'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple',
        'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch',
        'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone',
        'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear',
        'hair drier', 'toothbrush']


# 全部预埋件数据重新训练
# python train.py --data data/ymj_02-26.yaml --batch-size 32 --device=0 --epoch=50 --weights yolov5s.pt --imgsz 640