UTIL IMG_NORM - WHOIGit/ifcb_classifier Wiki
Calculating MEAN and STD for
Intensity normalization is the act of normalizing overall pixel values in multiple images into the same statistical distribution. By standardizing the input range of each pixel in an image, a neural net can more easily perform gradient descent to find good weights.
neuston_util.py CALC_IMG_NORM allows a user to calculate the MEAN and STD values of a given
SRC Dataset. Dataset Directories and Dataset Config CSVs are both accepted, as well as other dataset modifying runtime flags. Depending on the memory available on your system, you may need to adjust the
--batch-size flag. This process runs on the CPU.
Resultant MEAN and STD values are output to the terminal stdout.
neuston_util.py CALC_IMG_NORM path/to/MyDataset
usage: neuston_util.py CALC_IMG_NORM [-h] [--resize N] [--class-config CSV COL] [--class-min MIN] [--class-max MAX] [--batch-size B] SRC positional arguments: SRC optional arguments: -h, --help show this help message and exit --resize N Default is 299 (for inception_v3) --class-config CSV COL Skip/combine classes as defined by column COL of a CSV configuration file. --class-min MIN Exclude classes with fewer than MIN instances. Default is 2. --class-max MAX Limit classes to a MAX number of instances. If multiple datasets are specified via a dataset-configuration csv, classes from lower-priority datasets are truncated first. --batch-size B Number of images per minibatch