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ImageAI（四）使用Python快速简单实现自定义预测模型的训练CustomModel。。。

更新时间:2023-06-09 10:54:29 阅读：评论：0

ImageAI（四）使⽤Python快速简单实现⾃定义预测模型的训练

电梯销售CustomModel。。。

已经讲解了ImageAI实现图⽚预测，功能物体检测以及视频中物体检测。

同样，仅需⼏⾏主体代码就能完成⾃定义模型的预测。（这⾥主要介绍过程）

ImageAI

准备⼯作以及ImageAI的安装见图⽚预测

物体检测

视频中物体检测

Custom Model Training

与之前类似，ImageAI提供了4种算法（ SqueezeNet，ResNet，InceptionV3 和 DenNet）可⽤于⾃定义预测模型的训练。这⾥会使⽤ResNet

⾃定义预测模型需要准备训练数据（这⾥使⽤了IMAGEAI提供的IdenProf数据集包含了⼗种不同专业的⼯作⼈员）

数据结构如下

==IdenProf

==test

为什么定都北京

==chef

==doctor

...

显微镜的使用步骤

==train

==chef

==doctor

代码如下

from io import open

import requests

import shutil

from zipfile import ZipFile

import os

from imageai.Prediction.Custom import ModelTraining

#导⼊ModelTraining类

>>>>>>>>>>>>>>>###

>>>>>####以下为数据加载处理>>>>>>##

execution_path = os.getcwd()

TRAIN_ZIP_ONE = os.path.join(execution_path,"idenprof-train1.zip")

TRAIN_ZIP_TWO = os.path.join(execution_path,"idenprof-train2.zip")

TEST_ZIP = os.path.join(execution_path,"idenprof-test.zip")

DATASET_DIR = os.path.join(execution_path,"idenprof")

DATASET_TRAIN_DIR = os.path.join(DATASET_DIR,"train")

DATASET_TEST_DIR = os.path.join(DATASET_DIR,"test")

if(ists(DATASET_TRAIN_DIR)==Fal):

os.mkdir(DATASET_TRAIN_DIR)

if(ists(DATASET_TEST_DIR)==Fal):

os.mkdir(DATASET_TEST_DIR)

if(len(os.listdir(DATASET_TRAIN_DIR))<3):

if(ists(TRAIN_ZIP_ONE)==Fal):

print("Downloading idenprof-train1.zip")

青山不语data = ("/OlafenwaMos/IdenProf/releas/download/v1.0/idenprof-train1.zip", stream =True) with open(TRAIN_ZIP_ONE,"wb")as file:

del data

if(ists(TRAIN_ZIP_TWO)==Fal):

print("Downloading idenprof-train2.zip")

data = ("/OlafenwaMos/IdenProf/releas/download/v1.0/idenprof-train2.zip", stream=True) with open(TRAIN_ZIP_TWO,"wb")as file:

del data

print("Extracting idenprof-train1.zip")

extract1 = ZipFile(TRAIN_ZIP_ONE)

extract1.clo()

print("Extracting idenprof-train2.zip")

extract2 = ZipFile(TRAIN_ZIP_TWO)

extract2.clo()

if(len(os.listdir(DATASET_TEST_DIR))<3):

if(ists(TEST_ZIP)==Fal):

print("Downloading idenprof-test.zip")

data = ("/OlafenwaMos/IdenProf/releas/download/v1.0/idenprof-test.zip", stream=True) with open(TEST_ZIP,"wb")as file:

del data

print("Extracting idenprof-test.zip")

extract = ZipFile(TEST_ZIP)

extract.clo()

>>>>上⾯为训练及测试数据的处理>>>>>>####

>>>>>>>>>>>>>>>###

model_trainer = ModelTraining()

#创建ModelTraining类实例

model_trainer.tModelTypeAsResNet()

#将模型类型设置为ResNet 也可以使⽤其他模型

#tModelTypeAsSqueezeNet()

#tModelTypeAsInceptionV3()

食物视频#tModelTypeAsDenNet()

model_trainer.tDataDirectory(DATASET_DIR)

#设置训练数据集路径

ainModel(num_objects=3, num_experiments=10, enhance_data=True, batch_size=32, show_network_summary=True) #训练模型并设置参数详见说明

参数设置说明

num_objects 指定图像数据集中对象的数量及图像的种类（简单起见这⾥只⽤了三类）

num_experiments 图像训练的次数 epochs

enhance_data（可选）指定是否⽣成训练图像副本从⽽得到更好的性能

batch_size 批次处理每批数量

show_network_summary 是否在控制台中显⽰训练的过程

运⾏结果

⾸先展⽰了模型的结构然后是训练的结果

__________________________________________________________________________________________________ Layer (type) Output Shape Param # Connected to

================================================================================================== input_2 (InputLayer) (None, 224, 224, 3) 0

__________________________________________________________________________________________________ conv2d_54 (Conv2D) (None, 112, 112, 64) 9472 input_2[0][0]

__________________________________________________________________________________________________ batch_normalization_54 (BatchNo (None, 112, 112, 64) 256 conv2d_54[0][0]

__________________________________________________________________________________________________ activation_51 (Activation) (None, 112, 112, 64) 0 batch_normalization_54[0][0]

__________________________________________________________________________________________________ max_pooling2d_2 (MaxPooling2D) (None, 55, 55, 64) 0 activation_51[0][0]

__________________________________________________________________________________________________ conv2d_56 (Conv2D) (None, 55, 55, 64) 4160 max_pooling2d_2[0][0]

__________________________________________________________________________________________________ batch_normalization_56 (BatchNo (None, 55, 55, 64) 256 conv2d_56[0][0]

__________________________________________________________________________________________________ activation_52 (Activation) (None, 55, 55, 64) 0 batch_normalization_56[0][0]

__________________________________________________________________________________________________ conv2d_57 (Conv2D) (None, 55, 55, 64) 36928 activation_52[0][0]

__________________________________________________________________________________________________ batch_normalization_57 (BatchNo (None, 55, 55, 64) 256 conv2d_57[0][0]

__________________________________________________________________________________________________ activation_53 (Activation) (None, 55, 55, 64) 0 batch_normalization_57[0][0]

__________________________________________________________________________________________________ conv2d_58 (Conv2D) (None, 55, 55, 256) 16640 activation_53[0][0]

__________________________________________________________________________________________________ conv2d_55 (Conv2D) (None, 55, 55, 256) 16640 max_pooling2d_2[0][0]

__________________________________________________________________________________________________ batch_normalization_58 (BatchNo (None, 55, 55, 256) 1024 conv2d_58[0][0]

__________________________________________________________________________________________________ batch_normalization_55 (BatchNo (None, 55, 55, 256) 1024 conv2d_55[0][0]

__________________________________________________________________________________________________ add_17 (Add) (None, 55, 55, 256) 0 batch_normalization_58[0][0]

batch_normalization_55[0][0]

__________________________________________________________________________________________________ activation_54 (Activation) (None, 55, 55, 256) 0 add_17[0][0]

__________________________________________________________________________________________________ conv2d_59 (Conv2D) (None, 55, 55, 64) 16448 activation_54[0][0]

__________________________________________________________________________________________________ batch_normalization_59 (BatchNo (None, 55, 55, 64) 256 conv2d_59[0][0]

__________________________________________________________________________________________________ activation_55 (Activation) (None, 55, 55, 64) 0 batch_normalization_59[0][0]

__________________________________________________________________________________________________ conv2d_60 (Conv2D) (None, 55, 55, 64) 36928 activation_55[0][0]

__________________________________________________________________________________________________ batch_normalization_60 (BatchNo (None, 55, 55, 64) 256 conv2d_60[0][0]

__________________________________________________________________________________________________ activation_56 (Activation) (None, 55, 55, 64) 0 batch_normalization_60[0][0]

__________________________________________________________________________________________________ conv2d_61 (Conv2D) (None, 55, 55, 256) 16640 activation_56[0][0]

add_18 (Add) (None, 55, 55, 256) 0 batch_normalization_61[0][0]

activation_54[0][0]

__________________________________________________________________________________________________ activation_57 (Activation) (None, 55, 55, 256) 0 add_18[0][0]

__________________________________________________________________________________________________ conv2d_62 (Conv2D) (None, 55, 55, 64) 16448 activation_57[0][0]

__________________________________________________________________________________________________ batch_normalization_62 (BatchNo (None, 55, 55, 64) 256 conv2d_62[0][0]

__________________________________________________________________________________________________ activation_58 (Activation) (None, 55, 55, 64) 0 batch_normalization_62[0][0]

__________________________________________________________________________________________________ conv2d_63 (Conv2D) (None, 55, 55, 64) 36928 activation_58[0][0]

__________________________________________________________________________________________________ batch_normalization_63 (BatchNo (None, 55, 55, 64) 256 conv2d_63[0][0]

__________________________________________________________________________________________________ activation_59 (Activation) (None, 55, 55, 64) 0 batch_normalization_63[0][0]

__________________________________________________________________________________________________ conv2d_64 (Conv2D) (None, 55, 55, 256) 16640 activation_59[0][0]

__________________________________________________________________________________________________ batch_normalization_64 (BatchNo (None, 55, 55, 256) 1024 conv2d_64[0][0]

__________________________________________________________________________________________________ add_19 (Add) (None, 55, 55, 256) 0 batch_normalization_64[0][0]

activation_57[0][0]

__________________________________________________________________________________________________ activation_60 (Activation) (None, 55, 55, 256) 0 add_19[0][0]

__________________________________________________________________________________________________ conv2d_66 (Conv2D) (None, 28, 28, 128) 32896 activation_60[0][0]

无往不复__________________________________________________________________________________________________ batch_normalization_66 (BatchNo (None, 28, 28, 128) 512 conv2d_66[0][0]

__________________________________________________________________________________________________ activation_61 (Activation) (None, 28, 28, 128) 0 batch_normalization_66[0][0]

__________________________________________________________________________________________________ conv2d_67 (Conv2D) (None, 28, 28, 128) 147584 activation_61[0][0]

__________________________________________________________________________________________________ batch_normalization_67 (BatchNo (None, 28, 28, 128) 512 conv2d_67[0][0]

__________________________________________________________________________________________________ activation_62 (Activation) (None, 28, 28, 128) 0 batch_normalization_67[0][0]

__________________________________________________________________________________________________ conv2d_68 (Conv2D) (None, 28, 28, 512) 66048 activation_62[0][0]

__________________________________________________________________________________________________ conv2d_65 (Conv2D) (None, 28, 28, 512) 131584 activation_60[0][0]

__________________________________________________________________________________________________ batch_normalization_68 (BatchNo (None, 28, 28, 512) 2048 conv2d_68[0][0]

__________________________________________________________________________________________________ batch_normalization_65 (BatchNo (None, 28, 28, 512) 2048 conv2d_65[0][0]

__________________________________________________________________________________________________ add_20 (Add) (None, 28, 28, 512) 0 batch_normalization_68[0][0]

batch_normalization_65[0][0]

__________________________________________________________________________________________________ activation_63 (Activation) (None, 28, 28, 512) 0 add_20[0][0]

__________________________________________________________________________________________________ conv2d_69 (Conv2D) (None, 28, 28, 128) 65664 activation_63[0][0]

__________________________________________________________________________________________________ batch_normalization_69 (BatchNo (None, 28, 28, 128) 512 conv2d_69[0][0]

__________________________________________________________________________________________________ activation_64 (Activation) (None, 28, 28, 128) 0 batch_normalization_69[0][0]

__________________________________________________________________________________________________ conv2d_70 (Conv2D) (None, 28, 28, 128) 147584 activation_64[0][0]

__________________________________________________________________________________________________ batch_normalization_70 (BatchNo (None, 28, 28, 128) 512 conv2d_70[0][0]

__________________________________________________________________________________________________ activation_65 (Activation) (None, 28, 28, 128) 0 batch_normalization_70[0][0]

batch_normalization_71 (BatchNo (None, 28, 28, 512) 2048 conv2d_71[0][0]

__________________________________________________________________________________________________ add_21 (Add) (None, 28, 28, 512) 0 batch_normalization_71[0][0]

activation_63[0][0]

__________________________________________________________________________________________________ activation_66 (Activation) (None, 28, 28, 512) 0 add_21[0][0]

__________________________________________________________________________________________________ conv2d_72 (Conv2D) (None, 28, 28, 128) 65664 activation_66[0][0]

__________________________________________________________________________________________________ batch_normalization_72 (BatchNo (None, 28, 28, 128) 512 conv2d_72[0][0]

__________________________________________________________________________________________________ activation_67 (Activation) (None, 28, 28, 128) 0 batch_normalization_72[0][0]

__________________________________________________________________________________________________ conv2d_73 (Conv2D) (None, 28, 28, 128) 147584 activation_67[0][0]

__________________________________________________________________________________________________ batch_normalization_73 (BatchNo (None, 28, 28, 128) 512 conv2d_73[0][0]

__________________________________________________________________________________________________ activation_68 (Activation) (None, 28, 28, 128) 0 batch_normalization_73[0][0]

__________________________________________________________________________________________________ conv2d_74 (Conv2D) (None, 28, 28, 512) 66048 activation_68[0][0]

__________________________________________________________________________________________________ batch_normalization_74 (BatchNo (None, 28, 28, 512) 2048 conv2d_74[0][0]

__________________________________________________________________________________________________ add_22 (Add) (None, 28, 28, 512) 0 batch_normalization_74[0][0]

activation_66[0][0]

__________________________________________________________________________________________________ activation_69 (Activation) (None, 28, 28, 512) 0 add_22[0][0]

__________________________________________________________________________________________________ conv2d_75 (Conv2D) (None, 28, 28, 128) 65664 activation_69[0][0]

__________________________________________________________________________________________________ batch_normalization_75 (BatchNo (None, 28, 28, 128) 512 conv2d_75[0][0]

__________________________________________________________________________________________________ activation_70 (Activation) (None, 28, 28, 128) 0 batch_normalization_75[0][0]

__________________________________________________________________________________________________ conv2d_76 (Conv2D) (None, 28, 28, 128) 147584 activation_70[0][0]

__________________________________________________________________________________________________ batch_normalization_76 (BatchNo (None, 28, 28, 128) 512 conv2d_76[0][0]

__________________________________________________________________________________________________ activation_71 (Activation) (None, 28, 28, 128) 0 batch_normalization_76[0][0]

__________________________________________________________________________________________________ conv2d_77 (Conv2D) (None, 28, 28, 512) 66048 activation_71[0][0]

__________________________________________________________________________________________________ batch_normalization_77 (BatchNo (None, 28, 28, 512) 2048 conv2d_77[0][0]

__________________________________________________________________________________________________ add_23 (Add) (None, 28, 28, 512) 0 batch_normalization_77[0][0]

买家电activation_69[0][0]

__________________________________________________________________________________________________ activation_72 (Activation) (None, 28, 28, 512) 0 add_23[0][0]

颜回简介__________________________________________________________________________________________________ conv2d_79 (Conv2D) (None, 14, 14, 256) 131328 activation_72[0][0]

__________________________________________________________________________________________________ batch_normalization_79 (BatchNo (None, 14, 14, 256) 1024 conv2d_79[0][0]

__________________________________________________________________________________________________ activation_73 (Activation) (None, 14, 14, 256) 0 batch_normalization_79[0][0]

__________________________________________________________________________________________________ conv2d_80 (Conv2D) (None, 14, 14, 256) 590080 activation_73[0][0]

__________________________________________________________________________________________________ batch_normalization_80 (BatchNo (None, 14, 14, 256) 1024 conv2d_80[0][0]

__________________________________________________________________________________________________ activation_74 (Activation) (None, 14, 14, 256) 0 batch_normalization_80[0][0]

__________________________________________________________________________________________________ conv2d_81 (Conv2D) (None, 14, 14, 1024) 263168 activation_74[0][0]

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