create_tfrecords
A simpler way of preparing large-scale image dataset by generalizing functions from TensorFlow-slim.
Requirements
- Python 2.7.x
- TensorFlow >= 0.12
NOTE: If you want to run this program on Python 3, clone and run git checkout python-3.0
for the Python 3 branch instead.
Usage
$python create_tfrecord.py --dataset_dir=/path/to/dataset/ --tfrecord_filename=dataset_name
#Example: python create_tfrecord.py --dataset_dir=/path/to/flowers --tfrecord_filename=flowers
#Note that the dataset_dir should be the folder that contains the root directory and not the root directory itself.
Arguments
Required arguments:
- dataset_dir (string): The directory to your dataset that is arranged in a structured way where your subdirectories keep classes of your images.
For example:
flowers\
flower_photos\
tulips\
....jpg
....jpg
....jpg
sunflowers\
....jpg
roses\
....jpg
dandelion\
....jpg
daisy\
....jpg
Note: Your dataset_dir should be /path/to/flowers and not /path/to/flowers/flowers_photos
- tfrecord_filename (string): The output name of your TFRecord files.
Optional Arguments
-
validation_size (float): The proportion of the dataset to be used for evaluation.
-
num_shards (int): The number of shards to split your TFRecord files into.
-
random_seed (int): The random seed number for repeatability.
Complete Guide
For a complete guide, please visit here.