下面是yaml的内容：
# Ultralytics YOLO 🚀, AGPL-3.0 license
# YOLOv8 object detection model with P3-P6 outputs. For Usage examples see https://docs.ultralytics.com/tasks/detect# Parameters
nc: 80 # number of classes
scales: # model compound scaling constants, i.e. 'model=yolov8n-p6.yaml' will call yolov8-p6.yaml with scale 'n'# [depth, width, max_channels]n: [0.33, 0.25, 1024]s: [0.33, 0.50, 1024]m: [0.67, 0.75, 768]l: [1.00, 1.00, 512]x: [1.00, 1.25, 512]# YOLOv8.0x6 backbone
backbone:# [from, repeats, module, args]- [-1, 1, Conv, [64, 3, 2]] # 0-P1/2- [-1, 1, Conv, [128, 3, 2]] # 1-P2/4- [-1, 3, C2f, [128, True]]- [-1, 1, Conv, [256, 3, 2]] # 3-P3/8- [-1, 6, C2f, [256, True]]- [-1, 1, Conv, [512, 3, 2]] # 5-P4/16- [-1, 6, C2f, [512, True]]- [-1, 1, Conv, [768, 3, 2]] # 7-P5/32- [-1, 3, C2f, [768, True]]- [-1, 1, Conv, [1024, 3, 2]] # 9-P6/64- [-1, 3, C2f, [1024, True]]- [-1, 1, SPPF, [1024, 5]] # 11# YOLOv8.0x6 head
head:- [-1, 1, nn.Upsample, [None, 2, "nearest"]] # 12- [[-1, 8], 1, Concat, [1]] #13 cat backbone P5- [-1, 3, C2, [768, False]] # 14- [-1, 1, nn.Upsample, [None, 2, "nearest"]]# 15- [[-1, 6], 1, Concat, [1]] #16 cat backbone P4- [-1, 3, C2, [512, False]] # 17- [-1, 1, nn.Upsample, [None, 2, "nearest"]]# 18- [[-1, 4], 1, Concat, [1]] # 19cat backbone P3- [-1, 3, C2, [256, False]] # 20 (P3/8-small)- [-1, 1, Conv, [256, 3, 2]] # 21- [[-1, 17], 1, Concat, [1]] # cat head P4- [-1, 3, C2, [512, False]] # 23 (P4/16-medium)- [-1, 1, Conv, [512, 3, 2]]- [[-1, 14], 1, Concat, [1]] # cat head P5- [-1, 3, C2, [768, False]] # 26 (P5/32-large)- [-1, 1, Conv, [768, 3, 2]]- [[-1, 11], 1, Concat, [1]] # cat head P6- [-1, 3, C2, [1024, False]] # 29 (P6/64-xlarge)- [[20, 23, 26, 29], 1, Detect, [nc]] #30 Detect(P3, P4, P5, P6)
对于backbone：
（1）from：表示输入。-1表示上层的输出作为本层的输入。
（2）repeats：模块的重复次数。
（3）module：使用的模块。
（4）args：模块里面的参数
（5）计算size的公式：
out_size =（ in_size - k +2 * p）/ s + 1
假设输入是640的size，通道为3，可以编写如下代码查看各个输出维度：
def autopad(k, p=None, d=1):if d > 1:k = d * (k - 1) + 1 if isinstance(k, int) else [d * (x - 1) + 1 for x in k]if p is None:p = k // 2 if isinstance(k, int) else [x // 2 for x in k]return pdef calculate_size(in_size, k, s, p=None, d=1):p = autopad(k, p, d)out_size = ((in_size - k + (2 * p)) // s) + 1return out_sizedef print_layer_info(i, layer_name, layer_type, out_channels, size):print(f'Layer {layer_name}: type={layer_type}, out_channels={out_channels}, size={size}x{size}')input_map = {'channels': 3,'size': 640
}layers = [[-1, 1, "Conv", [64, 3, 2]], # 0-P1/2[-1, 1, "Conv", [128, 3, 2]], # 1-P2/4[-1, 3, "C2f", [128, True]],[-1, 1, "Conv", [256, 3, 2]] , # 3-P3/8[-1, 6, "C2f", [256, True]],[-1, 1, "Conv", [512, 3, 2]], # 5-P4/16[-1, 6, "C2f", [512, True]],[-1, 1, "Conv", [768, 3, 2]], # 7-P5/32[-1, 3, "C2f", [768, True]],[-1, 1, "Conv", [1024, 3, 2]] , # 9-P6/64
