效果图

以下内容为tensorflow官方测试代码，仅用于测试自己模型训练的效果，不是训练代码

导包

import warnings
warnings.filterwarnings("ignore")
import numpy as np
import os
import six.moves.urllib as urllib
import sys
import tarfile
import tensorflow as tf
import zipfile

from distutils.version import StrictVersion
from collections import 

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mask_rcnn_inception_v2_coco.config

# Mask R-CNN with Inception V2
# Configured for MSCOCO Dataset.
# Users should configure the fine_tune_checkpoint field in the train config as
# well as the label_map_path and input_path fields in the train_input_reader and
# eval_input_reader. Search for "PATH_TO_

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string_int_label_map_pb2.py

# Generated by the protocol buffer compiler.  DO NOT EDIT!
# source: object_detection/protos/string_int_label_map.proto

import sys
_b=sys.version_info[0]<3 and (lambda x:x) or (lambda x:x.encode('latin1'))
from google.protobuf import descriptor as _descriptor
from goo

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export_inference_graph.py

# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licen

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下载模型

地址： https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md

# 这个模型速度快 轻量级
http://download.tensorflow.org/models/object_detection/mask_rcnn_inception_v2_coco_2018_01_28.tar.gz

训练自己的分割模型

1. 准备好训练图片

• 训练集的图片必须小于1M，手机拍摄的原图会让CPU内存爆炸

• 标注产生的json文件必须和标图片在一

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训练自己的分类模型

基于inception v3

介绍

下载 inception v3 模型

http://download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz

其中的 ++classify_image_graph_def.pb++ 文件就是模型文件，但是pb文件只能使用，不能继续训练。

++imagenet_synset_to_human_label_map.txt++ ++imagenet_2012_challenge_label_map_proto.pbtxt++ 这两个文件是数字编号对应物品英文描

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验证码生成

# 验证码生成库
# pip install captcha
from captcha.image import ImageCaptcha  
import numpy as np
from PIL import Image
import random
import sys

number = ['1','2','3','4','5','a','b','c','d','e','A','G']

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from keras.applications.vgg16 import VGG16
from keras.models import Sequential
from keras.layers import Conv2D,MaxPool2D,Activation,Dropout,Flatten,Dense
from keras.optimizers import SGD
from keras.preprocessing.image import ImageDataGenerator,img_to_array,load_img
import numpy as np

#VGG16是用image

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import keras
from keras.models import Sequential
from keras.layers import Conv2D,MaxPool2D,Dense,Dropout,Flatten
from keras.optimizers import Adam
from keras.preprocessing.image import load_img,ImageDataGenerator,img_to_array

# 定义模型

model = Sequential()

model.add(Conv2D(input_shape=(150,150,3),f

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import numpy as np
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Activation, Flatten, Reshape
from keras.layers import Conv2D, Conv2DTranspose, UpSampling2D
from keras.layers import LeakyReLU, Dropout
from keras.layers import BatchNormalization

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