"""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(...
# 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, 3157200parameters, 3157184 gradients, 8.9 GFLOPs...
22 [15, 18, 21] 1 2116435 ultralytics.nn.modules.Detect [1, [128, 256, 512]] Model summary: 225 layers, 11135987 parameters, 11135971 gradients, 28.6 GFLOPs Transferred 349/355 items from pretrained weights optimizer: SGD(lr=0.01) with parameter groups 57 weight(decay=0.0), 64 weight(d...
For details see https://docs.ultralytics.com/models/yolov5 # Parameters nc: 80 # number of classes scales: # model compound scaling constants, i.e. 'model=yolov5n.yaml' will call yolov5.yaml with scale 'n' # [depth, width, max_channels] n: [0.33, 0.25, 1024] s: [0.33, 0.50...
examples see https://docs.ultralytics.com/tasks/detect # Parameters nc: 1 # number of ...
For details see https://docs.ultralytics.com/models/rtdetr # Parameters nc: 80 # number of classes scales: # model compound scaling constants, i.e. 'model=yolov8n-cls.yaml' will call yolov8-cls.yaml with scale 'n' # [depth, width, max_channels] l: [1.00, 1.00, 1024] backbone:...
com/models/rtdetr # Parameters nc: 80 # number of classes scales: # model compound scaling constants, i.e. 'model=yolov8n-cls.yaml' will call yolov8-cls.yaml with scale 'n' # [depth, width, max_channels] l: [1.00, 1.00, 1024] backbone: # [from, repeats, module, args] - [-...
"" 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...
# Parametersnc:80# numberofclassesscales:# model compound scaling constants,i.e.'model=yolov8n.yaml'will call yolov8.yamlwithscale'n'#[depth,width,max_channels]n:[0.33,0.25,1024]# YOLOv8n summary:225layers,3157200parameters,3157184gradients,8.9GFLOPss:[0.33,0.50,1024]# YOLOv8s summary:225...
# Ultralytics YOLO 馃殌, GPL-3.0 license# Parametersnc: 3 # number of classes(只需要改这里!!!)depth_multiple: 0.33 # scales module repeatswidth_multiple: 0.50 # scales convolution channels# YOLOv8.0s backbonebackbone: # [from, repeats, module, args] - [-1, 1, Conv, [64, 3, 2]]...