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Training
tselea edited this page Dec 18, 2019
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Each training operation includes:
- pre-processing - actions applied to the input data
- model configuration - specify inputs and outputs, customize model training parameters
It is mandatory to prepare a training configuration YAML file, that sets the parameters for the training process.
The pre-processing steps includes:
- tiling the image in smaller patches
- standardise the pixel values based on Z-Score
Starting the training step (from the src
directory):
python -m hugin.tools.cli train --config /path/to/training_config.yaml --keras-batch-size 5
The --config
parameter is mandatory. This should be followed by the path to the training configuration file.
The --keras-batch-size
is optional. If not present, the training batch size is set from the configuration file.
An example file can be found at hugin/etc/usecases/s2-forestry/train_unet_corine_gt.yaml.
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data_pattern: Mandatory parameter. This is the regex pattern that must match all the possible image types and ground truth (GT) images that should be included in the sampling. The regex should include named groups (?P<group_name>regex), in order to make the image name pattern easier to identify.
Example: ''(?PT[0-9A-Z]+)(?P[A-Z0-9]+)(?P[A-Z0-9_a-z]+)(?P[0-9a-z]+)(?P[0-9]+)..*$''
Explanation: A match for the pattern is: T31UES_20180508T104031_B02_10m_4326.jp2