mikevoets/create_tfrecords

create_tfrecords

A simpler way of preparing large-scale image dataset by generalizing functions from TensorFlow-slim.

This is a forked and modified repository. The original code can be found here.

Requirements

1. Python 3
2. TensorFlow >= 0.12

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/
    tulips/
        ....jpg
        ....jpg
        ....jpg
    sunflowers/
        ....jpg
    roses/
        ....jpg
    dandelion/
        ....jpg
    daisy/
        ....jpg

 

• 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.