Índice de códigos fuente
3.1Cómo
explorar el corpus VisSE con Quevedo6.1Simplified
JSON annotation file for the transcription in Figure
6.27.1Example
json annotation file8.1How
to examine the visse-corpus dataset with Quevedo9.1How to
create a Quevedo dataset9.2Example
Quevedo dataset configuration9.3How to launch
Quevedo’s annotation web interface9.4Example use
of Quevedo as a library to access a graphical language dataset
programatically9.5Neural
network configuration with Quevedo9.6Usage of Quevedo’s
CLI to train and test neural networks9.7How to install
and run the web interface to see the example dataset10.1Example of a
Quevedo dataset directory structure10.2How to install
darknet10.3Prepare10.4Example
network training and testing configuration for a Quevedo
dataset10.5Example
logogram pipeline10.6Example
sequence pipeline10.7Example
branching pipeline10.8Default
Quevedo dataset configuration file10.9Creating a
Quevedo dataset10.10Initialize
the dataset with git and DVC10.11Add images
to a dateset10.12Track
dataset data with DVC10.13Run custom
scripts on the dataset10.14Annotate
logograms with the web interface10.15Augment
the dataset with artificial samples10.16Record
data augmentation as a DVC pipeline10.17Split all
logograms into folds10.18Assign a
particular set of graphemes to some folds10.19Train and
test neural networks with DVC pipelines10.20Python
script that uses Quevedo as a library