captcha_fit_model: Fit Captcha model

In decryptr/captcha: Solve Text on Image Computer Vision Challenges

Fit Captcha model

Description

Provides a basic interface for fitting custom models from a fully labeled data. Annotation can be done manually using the captcha_annotate() function presented earlier or with another method developed by the user. The model uses a convolutional neural network architecture, similar to the LeNet-5 model.

Usage

captcha_fit_model(
  dir,
  dir_valid = NULL,
  prop_valid = 0.2,
  dropout = 0.25,
  dense_units = 200,
  decay = 0.99,
  batch_size = 40,
  epochs = 100
)

Arguments

dir	directory where the classified images are
dir_valid	(optional) directory to validation files
prop_valid	proportion of total images considered to validation. Defaults to 0.2.
dropout	dropout hyperparameter. Default 0.25.
dense_units	number of dense units to use after convolution steps. Defaults to 200.
decay	Weight decay applied each epoch.
batch_size	Mini batch size. Default 40.
epochs	Number of epochs to use. Default 100. The model uses early stopping, so it is possible that the procedure ends before the total number of epochs actually run.

Value

fitted model of class luz_module_fitted.

The modeling step has some assumptions about the file names. Images must be in a folder and have the pattern ⁠path/to/file/<id>_<lab>.<ext>⁠, where:

• ⁠<id>⁠: can be any name, preferably without accents or other special characters, to avoid encoding issues. It usually contains a name for the type and a hash to identify the image uniquely. Note: When annotating a file, the id must be unique, as two Captchas can have the same label.

• ⁠<lab>⁠: is the Captcha label. It is a string of characters between ⁠[a-zA-Z0-9]⁠, which can be case-sensitive if necessary. All labels must have the same length.

• ⁠<ext>⁠: file extension. It can be .png, .jpeg or .jpg. The operations also work for the .svg format, but it may have problems due to the image's transparency.

An important note is that the model stops fitting after 20 iterations without significant increment of accuracy (chosen as 1%; for more details, see vignette("advanced").

decryptr/captcha documentation built on April 15, 2023, 12:42 a.m.