Darknet模型网络结构
时间: 2023-08-03 12:43:28 浏览: 42
Darknet是一个轻量级的神经网络框架,主要用于对象检测和图像识别等计算机视觉任务。Darknet的模型网络结构指的是其所实现的卷积神经网络(CNN)结构。
Darknet的原始网络结构是由53个卷积层和池化层组成的,因此被称为Darknet-53。它的架构可以用以下方式表示:
```
Convolutional Layer (with 32 filters, 3x3 kernel, stride 1, padding 1)
Batch Normalization Layer
Leaky ReLU Activation Layer (with alpha=0.1)
Max Pooling Layer (with size 2x2, stride 2)
Convolutional Layer (with 64 filters, 3x3 kernel, stride 1, padding 1)
Batch Normalization Layer
Leaky ReLU Activation Layer (with alpha=0.1)
Max Pooling Layer (with size 2x2, stride 2)
Convolutional Layer (with 128 filters, 3x3 kernel, stride 1, padding 1)
Batch Normalization Layer
Leaky ReLU Activation Layer (with alpha=0.1)
Convolutional Layer (with 64 filters, 1x1 kernel, stride 1, padding 0)
Batch Normalization Layer
Leaky ReLU Activation Layer (with alpha=0.1)
Convolutional Layer (with 128 filters, 3x3 kernel, stride 1, padding 1)
Batch Normalization Layer
Leaky ReLU Activation Layer (with alpha=0.1)
Max Pooling Layer (with size 2x2, stride 2)
......
```
以上仅为Darknet-53的一部分网络结构示例,具体结构还包括多个残差块、全连接层和YOLOv3输出层等组成,具体可参考Darknet的开源代码。