nc: 80 # 类别数目,nc代表"number of classes",即模型用于检测的对象类别总数。 80表示该模型配置用于检测80种不同的对象。由于默认使用COCO数据集,这里nc=80; scales: # 模型复合缩放常数,用于定义模型的不同尺寸和复杂度。例如 'model=yolov8n.yaml' 将调用带有 'n' 缩放的 yolov8.yaml # [depth, widt...
# Parameters nc: 80 # number of classes scales: # model compound scaling constants, i.e. 'model=yolov8n.yaml' will call yolov8.yaml with scale 'n' # [depth, width, max_channels] n: [0.33, 0.25, 1024] # YOLOv8n summary: 225 layers, 3157200 parameters, 3157184 gradients, 8.9 GFLO...
"""YOLOv8 detection model.""" def __init__(self, cfg="yolov8n.yaml", ch=3, nc=None, verbose=True): # model, input channels, number of classes """Initialize the YOLOv8 detection model with the given config and parameters.""" super().__init__() self.yaml = cfg if isinstance(...
# Ultralytics YOLO 🚀, AGPL-3.0 license# YOLOv8 object detection model with P3-P5 outputs. For Usage examples see https://docs.ultralytics.com/tasks/detect# Parametersnc: 80 # 类别数目,nc代表"number of classes",即模型用于检测的对象类别总数。scales: # 模型复合缩放常数,例如 'model=yolov...
"" def __init__(self, cfg="yolov8n.yaml", ch=3, nc=None, verbose=True): # model, input channels, number of classes """Initialize the YOLOv8 detection model with the given config and parameters.""" super().__init__() self.yaml = cfg if isinstance(cfg, dict) else yaml_model...
com/tasks/detect # Parameters nc: 4 # number of classes scales: # model compound scaling constants, i.e. 'model=yolov8n.yaml' will call yolov8.yaml with scale 'n' # [depth, width, max_channels] n: [0.33, 0.25, 1024] # YOLOv8n summary: 225 layers, 3157200 parameters, 3157184 ...
# numberofclassesnc:1#classnamesnames:0:football 2.2 开启训练 代码语言:javascript 复制 importwarnings warnings.filterwarnings('ignore')from ultralyticsimportYOLOif__name__=='__main__':model=YOLO('ultralytics/cfg/models/v8/yolov8.yaml')model.train(data='data/football/football.yaml',cache=Fal...
# number of classes nc: 7 # class names names: ['car','chemicals vehicle','truck','bus','triangle warning sign','warning sign','warning slogan' ] 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 3 开始训练 ...
(reduction="none")# 保存超参数self.hyp = h# 获取模型的步长信息self.stride = m.stride# model strides# 获取模型的类别数self.nc = m.nc# number of classes# 设置输出通道数,包括类别和回归目标self.no = m.nc + m.reg_max *4# 获取模型的最大回归目标数量self.reg_max = m.reg_max# 保存...
train: train/valid: valid/test: test/# number of classesnc: 12# class namesnames: ["Barn Swallow","Common Firecrest","Common Nightingale","Eurasian Chaffinch","Eurasian Crag Martin","European Goldfinch","European Greenfinch","European Serin","House Sparrow","Spanish Sparrow","Western House ...