add_argument ( '-r', '-ratio', help = 'The ratio of the number of test images over the total number of images. ' 'Defaults to the same directory as IMAGEDIR.', type = str, default = None ) parser. add_argument ( '-o', '-outputDir', help = 'Path to the output folder where the train and test dirs should be created. If not specified, the CWD will be used.', type = str, default = os. add_argument ( '-i', '-imageDir', help = 'Path to the folder where the image dataset is stored. ArgumentParser ( description = 'Partition dataset of images into training and testing sets', formatter_class = argparse. join ( train_dir, xml_filename )) def main (): # Initiate argument parser parser = argparse. splitext ( filename ) + '.xml' copyfile ( os. join ( train_dir, filename )) if copy_xml : xml_filename = os.

remove ( images ) for filename in images : copyfile ( os. join ( test_dir, xml_filename )) images. join ( test_dir, filename )) if copy_xml : xml_filename = os. randint ( 0, len ( images ) - 1 ) filename = images copyfile ( os. ceil ( ratio * num_images ) for i in range ( num_test_images ): idx = random. makedirs ( test_dir ) images = )+(?i)(.jpg|.jpeg|.png)$', f )] num_images = len ( images ) num_test_images = math. ''' import os import re from shutil import copyfile import argparse import math import random def iterate_dir ( source, dest, ratio, copy_xml ): source = source. x, -xml Set this flag if you want the xml annotation files to be processed and copied over. r RATIO, -ratio RATIO The ratio of the number of test images over the total number of images. Defaults to the same directory as IMAGEDIR. o OUTPUTDIR, -outputDir OUTPUTDIR Path to the output folder where the train and test dirs should be created. ''' usage: partition_dataset.py Partition dataset of images into training and testing sets optional arguments: -h, -help show this help message and exit -i IMAGEDIR, -imageDir IMAGEDIR Path to the folder where the image dataset is stored.

If you do not understand most of the things mentioned above, no need to worry, as weвЂ™ll see how all the files are generated further down. It is not used by TensorFlow in any way, but it generally helps when you have a few training folders and/or you are revisiting a trained model after some time. README.md: This is an optional file which provides some general information regarding the training conditions of our model. Pre-trained-models: This folder will contain the downloaded pre-trained models, which shall be used as a starting checkpoint for our training jobs. Each subfolder will contain the training pipeline configuration file *.config, as well as all files generated during the training and evaluation of our model.

Models: This folder will contain a sub-folder for each of training job. Images/test: This folder contains a copy of all images, and the respective *.xml files, which will be used to test our model. Images/train: This folder contains a copy of all images, and the respective *.xml files, which will be used to train our model.

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