yolov8的骨干网络

YOLOv8的骨干网络是Darknet-53。Darknet-53是一个53层的卷积神经网络，用于提取图像特征。它由一系列的卷积层和残差块组成，具有较强的特征提取能力。

以下是Darknet-53的网络结构示例：

# Darknet-53网络结构示例
model = Sequential()

# 第一部分：卷积层
model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(224, 224, 3)))
model.add(MaxPooling2D((2, 2)))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(MaxPooling2D((2, 2)))
model.add(Conv2D(128, (3, 3), activation='relu'))
model.add(Conv2D(64, (1, 1), activation='relu'))
model.add(Conv2D(128, (3, 3), activation='relu'))
model.add(MaxPooling2D((2, 2)))
model.add(Conv2D(256, (3, 3), activation='relu'))
model.add(Conv2D(128, (1, 1), activation='relu'))
model.add(Conv2D(256, (3, 3), activation='relu'))
model.add(MaxPooling2D((2, 2)))
model.add(Conv2D(512, (3, 3), activation='relu'))
model.add(Conv2D(256, (1, 1), activation='relu'))
model.add(Conv2D(512, (3, 3), activation='relu'))
model.add(Conv2D(256, (1, 1), activation='relu'))
model.add(Conv2D(512, (3, 3), activation='relu'))

# 第二部分：残差块
model.add(Conv2D(512, (1, 1), activation='relu'))
model.add(Conv2D(1024, (3, 3), activation='relu'))
model.add(MaxPooling2D((2, 2)))
model.add(Conv2D(512, (1, 1), activation='relu'))
model.add(Conv2D(1024, (3, 3), activation='relu'))
model.add(Conv2D(512, (1, 1), activation='relu'))
model.add(Conv2D(1024, (3, 3), activation='relu'))
model.add(Conv2D(1024, (3, 3), activation='relu'))
model.add(Conv2D(1024, (3, 3), activation='relu'))

# 第三部分：全连接层
model.add(Flatten())
model.add(Dense(4096, activation='relu'))
model.add(Dense(1000, activation='softmax'))

# 输出网络结构
model.summary()