TFDatasets

TFDatasets is a collection of scripts to preprocess various Computer Vision datasets and convert them to TFRecords for easy integration in the tf.data.Dataset pipeline. The code was designed with Python 3+ and tensorflow 1.4+ in mind.

The notebook load_datasets.ipynb displays examples of writing and parsing TFRecords for each dataset. See the last section of this readme for an index of available datasets.

The notebook preprocess.ipynb displays of example of various preprocessing utilities for tf.data.Dataset (adding random crops, occlusion generation, subsampling etc.) demonstrated on the mnist dataset.

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Table of Contents

Dataset	Link	Example	TFRecords contents
ACwS	Apparel Classification with Style		image, class
CartoonSet	CartoonSet		image, bounding-box, attributes
CelebA	CelebA		image, bounding-box, attributes, landmarks
CIFAR-10(0)	CIFAR		image, class, (coarse_class), (coarse_)class-name,
Fashion MNIST	Fashion MNIST		image, class, index
MNIST	MNIST		image, digit-class, index
MNIST-M	MNIST-M		image, digit-class, index
M2NIST	M2NIST		image, segmentation-mask, index
PACS	PACS		image, content-class, style-class
SVHN	SVHN		image, digit-class, index
Tiny ImageNet	Tiny ImageNet		image, class, class-name, bounding-box
VisDA	VisDA		image, class

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Notes on the API

The module for a dataset data is contained in dataset_utils.data.py. It contains three main objects: a Features description, a Converter and a Loader.

Features

A Features object defines the content of a TFRecord example. It is initialized from a list of tuples, where each tuple represents a feature as:

• name: a name, which will be the corresponding feature key in the parsed dictionnary
• type: the type of the feature, one of int, float or bytes (see tfrecords_utils.FeatureType)
• length: whether the tuple should be of fixed or variable length (see tfrecords_utils.FeatureLength)
• shape: if the length is fixed, the shape of the feature, which is a nested list of integers.
• default: A default value for the Records when loading (or None if no missing data)

Converter

A converter object contains one main method, convert that generates the TFRecords.

It generally takes as arguments

• target_path, the path where to solve the TFRecords to.
• compression_type, one of None, 'gzip' or 'zlib', that determines the compression option for the TFRecords.
• save_image_in_records, a boolean which indicates whether to store the image directly in the TFRecords; if the images are large, it can be useful to compress the TFRecords using compression_type. Otherwise, if that argument is False, the function only stores the path to the image, which will then be loaded by the tf.data.Dataset object.

Additionally, in some cases this method takes a keyword argument sort; If this is True, the entries in the TFRecords will be sorted in someway, e.g. by class labels. Note that this means the shuffle_buffer size should be at least equal to the number of samples in the dataset for proper shuffling (hence not optimal for large datasets), but it can be convenient to quickly filter/sample the dataset based on classes.

Loader

A Loader object simply builds a proper parsing function to extract data from the TFRecords and format it correctly. Such a function can then be passed to the tf.data.Dataset API map function to parse the TFRecords dataset.