Tensorflow2.0 YOLO篇之模型搭建与训练

最新推荐文章于 2022-01-06 15:09:20 发布

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最新推荐文章于 2022-01-06 15:09:20 发布 · 2.6k 阅读

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#深度学习 #tensorflow #神经网络 #python

本文详细介绍了如何在TensorFlow2.0环境下搭建与训练YOLOv2目标检测模型，包括模型结构的设计、权重初始化、损失函数的计算及训练过程。通过代码实现展示了模型的每一层构建细节，以及如何利用预训练权重进行初始化。

Tensorflow2.0之 YOLO篇模型搭建与训练

这一篇我们就要结束yolo2的代码了，代码和相关资料的下载链接在博客的最后面,文章只是模型搭建和训练的过程，代码里面涉及到的一些函数是上几篇博客中提到的，有兴趣可以去看看。

模型搭建

YOLO的训练模型我们选择在进行优化Dartnet-19，并选择其的参数数据进行初始化
在这里插入图片描述

DartNet

class SpaceToDepth(layers.Layer):

    def __init__(self, block_size, **kwargs):
        self.block_size = block_size
        super(SpaceToDepth, self).__init__(**kwargs)

    def call(self, inputs):
        x = inputs
        batch, height, width, depth = K.int_shape(x)
        batch = -1
        reduced_height = height // self.block_size
        reduced_width = width // self.block_size
        y = K.reshape(x, (batch, reduced_height, self.block_size,
                          reduced_width, self.block_size, depth))
        z = K.permute_dimensions(y, (0, 1, 3, 2, 4, 5))
        t = K.reshape(z, (batch, reduced_height, reduced_width, depth * self.block_size ** 2))
        return t

    def compute_output_shape(self, input_shape):
        shape = (input_shape[0], input_shape[1] // self.block_size, input_shape[2] // self.block_size,
                 input_shape[3] * self.block_size ** 2)
        return tf.TensorShape(shape)


# 3.1
input_image = layers.Input((IMGSZ, IMGSZ, 3), dtype='float32')

# unit1
x = layers.Conv2D(32, (3, 3), strides=(1, 1), padding='same', name='conv_1', use_bias=False)(input_image)
x = layers.BatchNormalization(name='norm_1')(x)
x = layers.LeakyReLU(alpha=0.1)(x)

x = layers.MaxPooling2D(pool_size=(2, 2))(x)

# unit2
x = layers.Conv2D(64, (3, 3), strides=(1, 1), padding='same', name='conv_2', use_bias=False)(x)
x = layers.BatchNormalization(name='norm_2')(x)
x = layers.LeakyReLU(alpha=0.1)(x)
x = layers.MaxPooling2D(pool_size=(2, 2))(x)

# Layer 3
x = layers.Conv2D(128, (3, 3), strides=(1, 1), padding='same', name='conv_3', use_bias=False)(x)
x = layers.BatchNormalization(name='norm_3')(x)
x = layers.LeakyReLU(alpha=0.1)(x)

# Layer 4
x = layers.Conv2D(64, (1, 1), strides=(1, 1), padding='same', name='conv_4', use_bias=False)(x)
x = layers.BatchNormalization(name='norm_4')(x)
x = layers.LeakyReLU(alpha=0.1)(x)

# Layer 5
x = layers.Conv2D(128, (3, 3