基于keras框架的Vgg深度学习神经网络衣服多标签分类识别系统

第一步：准备数据

6种衣服组合数据：'black_jeans', 'blue_dress', 'blue_jeans', 'blue_shirt', 'red_dress', 'red_shirt'，总共有2167张图片，每个文件夹单独放一种动物

第二步：搭建模型

本文选择vgg，其网络结构如下：

参考代码如下：

class SmallerVGGNet:
	@staticmethod
	def build(width, height, depth, classes, finalAct="softmax"):
		# initialize the model along with the input shape to be
		# "channels last" and the channels dimension itself
		model = Sequential()
		inputShape = (height, width, depth)
		chanDim = -1

		# if we are using "channels first", update the input shape
		# and channels dimension
		if K.image_data_format() == "channels_first":
			inputShape = (depth, height, width)
			chanDim = 1

		# CONV => RELU => POOL
		model.add(Conv2D(32, (3, 3), padding="same",
			input_shape=inputShape))
		model.add(Activation("relu"))
		model.add(BatchNormalization(axis=chanDim))
		model.add(MaxPooling2D(pool_size=(3, 3)))
		model.add(Dropout(0.25))

		# (CONV => RELU) * 2 => POOL
		model.add(Conv2D(64, (3, 3), padding="same"))
		model.add(Activation("relu"))
		model.add(BatchNormalization(axis=chanDim))
		model.add(Conv2D(64, (3, 3), padding="same"))
		model.add(Activation("relu"))
		model.add(BatchNormalization(axis=chanDim))
		model.add(MaxPooling2D(pool_size=(2, 2)))
		model.add(Dropout(0.25))

		# (CONV => RELU) * 2 => POOL
		model.add(Conv2D(128, (3, 3), padding="same"))
		model.add(Activation("relu"))
		model.add(BatchNormalization(axis=chanDim))
		model.add(Conv2D(128, (3, 3), padding="same"))
		model.add(Activation("relu"))
		model.add(BatchNormalization(axis=chanDim))
		model.add(MaxPooling2D(pool_size=(2, 2)))
		model.add(Dropout(0.25))

		# first (and only) set of FC => RELU layers
		model.add(Flatten())
		model.add(Dense(1024))
		model.add(Activation("relu"))
		model.add(BatchNormalization())
		model.add(Dropout(0.5))

		# softmax classifier
		model.add(Dense(classes))
		model.add(Activation(finalAct))

		# return the constructed network architecture
		return model

第三步：统计正确率

第四步：搭建GUI界面

第五步：整个工程的内容

有训练代码和训练好的模型以及训练过程，提供数据，提供GUI界面代码

项目完整文件下载请见演示与介绍视频的简介处给出：➷➷➷

https://www.bilibili.com/video/BV1pJBrYdEvS/