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Project Introduction

This project is based on CNN5/DenseNet+BLSTM/LSTM+CTC to realize verification code identification. This project is only for training the model, If you need to deploy the model, please move to https://github.com/kerlomz/captcha_platform

Attention

  1. Choose your version: This project uses GPU for training by default. You can use the CPU version by replacing tensorflow-gpu==1.10.0 in the requirements.txt file with tensorflow==1.10.0

Ready to work

If you want to use the GPU for training, you must first install CUDA and cuDNN: https://www.tensorflow.org/install/install_sources#tested_source_configurations Please open the above link to view the version of CUDA and cuDNN corresponding to the current TensorFlow version.

Start

  1. Install the python 3.6 environment (with pip)
  2. Install virtualenv pip3 install virtualenv
  3. Create a separate virtual environment for the project: 
    virtualenv -p /usr/bin/python3 venv # venv is the name of the virtual environment.
cd venv/ # venv is the name of the virtual environment.
source bin/activate # to activate the current virtual environment.
cd captcha_trainer # captcha_trainer is the project path.
  4. pip install -r requirements.txt

Configuration

  1. config.yaml - System Config

    # - requirement.txt  -  GPU: tensorflow-gpu, CPU: tensorflow
# - If you use the GPU version, you need to install some additional applications.
# TrainRegex and TestRegex: Default matching apple_20181010121212.jpg file.
# - The Default is .*?(?=_.*\.)
# TrainsPath and TestPath: The local absolute path of your training and testing set.
# TestSetNum: This is an optional parameter that is used when you want to extract some of the test set
# - from the training set when you are not preparing the test set separately.
System:
  DeviceUsage: 0.7
  TrainsPath: 'E:\Task\Trains\YourModelName\'
  TrainRegex: '.*?(?=_)'
  TestPath: 'E:\Task\TestGroup\YourModelName\'
  TestRegex: '.*?(?=_)'
  TestSetNum: 1000

# CNNNetwork: [CNN5, DenseNet]
# RecurrentNetwork: [BLSTM, LSTM]
# - The recommended configuration is CNN5+BLSTM / DenseNet+BLSTM
# HiddenNum: [64, 128, 256]
# - This parameter indicates the number of nodes used to remember and store past states.
NeuralNet:
  CNNNetwork: CNN5
  RecurrentNetwork: BLSTM
  HiddenNum: 64
  KeepProb: 0.98

# SavedSteps: A Session.run() execution is called a Epochs,
# - Used to save training progress, Default value is 100.
# ValidationSteps: Used to calculate accuracy, Default value is 100.
# TestNum: The number of samples for each test batch.
# - A test for every saved steps.
# EndAcc: Finish the training when the accuracy reaches [EndAcc*100]%.
# EndEpochs: Finish the training when the epoch is greater than the defined epoch.
Trains:
  SavedSteps: 100
  ValidationSteps: 500
  EndAcc: 0.975
  EndEpochs: 1
  BatchSize: 64
  TestBatchSize: 400
  LearningRate: 0.01
  DecayRate: 0.98
  DecaySteps: 10000

    There are several common examples of TrainRegex: i. apple_20181010121212.jpg

    .*?(?=_.*\.)

    ii apple.png

    .*?(?=\.)

  2. model.yaml - Model Config

    # Sites: A bindable parameter used to select a model. 
# - If this parameter is defined, 
# - it can be identified by using the model_site parameter 
# - to identify a model that is inconsistent with the actual size of the current model.
# ModelName: Corresponding to the model file in the model directory,
# - such as YourModelName.pb, fill in YourModelName here.
# ModelType: This parameter is also used to locate the model. 
# - The difference from the sites is that if there is no corresponding site, 
# - the size will be used to assign the model. 
# - If a model of the corresponding size and corresponding to the ModelType is not found, 
# - the model belonging to the category is preferentially selected.
# CharSet: Provides a default optional built-in solution:
# - [ALPHANUMERIC, ALPHANUMERIC_LOWER, ALPHANUMERIC_UPPER,
# -- NUMERIC, ALPHABET_LOWER, ALPHABET_UPPER, ALPHABET]
# - Or you can use your own customized character set like: ['a', '1', '2'].
# CharExclude: CharExclude should be a list, like: ['a', '1', '2']
# - which is convenient for users to freely combine character sets.
# - If you don't want to manually define the character set manually,
# - you can choose a built-in character set
# - and set the characters to be excluded by CharExclude parameter.
Model:
  Sites: []
  ModelName: YourModelName
  ModelType: 150x50
  CharSet: ALPHANUMERIC_LOWER
  CharExclude: []
  CharReplace: {}
  ImageWidth: 150
  ImageHeight: 50

# Binaryzation: [-1: Off, >0 and < 255: On].
# Smoothing: [-1: Off, >0: On].
# Blur: [-1: Off, >0: On].
Pretreatment:
  Binaryzation: -1
  Smoothing: -1
  Blur: -1

Tools

  1. Pretreatment Previewer python -m tools.preview
  2. Navigator (Currently only supports character set recommendations) python -m tools.navigator
  3. PyInstaller Package 
    pip install pyinstaller
python -m tools.package

Run

  1. python trains.py

License

This project use SATA License (Star And Thank Author License), so you have to star this project before using. Read the license carefully.

Introduction

https://www.jianshu.com/p/80ef04b16efc

Donate

Thank you very much for your support of my project. 赞助