datadings 3.4.6

datadings is a collection of tools to prepare datasets for machine learning. It's easy to use, space-efficient, and blazingly fast.

datadings is a collection of tools to prepare datasets for machine learning, based on two simple principles

Datasets are collections of individual data samples.

Each sample is a dictionary with descriptive keys.

For supervised training with images samples are dictionaries like this:

{"key": unique_key, "image": imagedata, "label": label}

msgpack is used as an efficient storage format for most supported datasets.

Check out the documentation for more details.

Supported datasets

Dataset	Short Description
ADE20k	Scene Parsing, Segmentation
ANP460	own Eye-Tracking dataset (Jalpa)
CAMVID	Motion-based Segmentation
CAT2000	MIT Saliency
CIFAR	32x32 color image classification with 10/100 classes
Cityscapes	Segmentation, Semantic understanding of urban street scenes
Coutrot1	Eye-Tracking, Saliency
FIGRIMFixation	Eye-Tracking, Saliency
ILSVRC2012	Imagenet Large Scale Visual Recognition Challenge
ImageNet21k	A superset of ILSVRC2012 with 11 M images for 10450 classes
InriaBuildings	Inria Areal Image Labeling Dataset (Buildings), Segmentation, Remote Sensing
MIT1003	Eye-Tracking, Saliency, Learning to predict where humans look
MIT300	Eye-Tracking, Saliency
Places2017	MIT Places, Scene Recognition
Places365	MIT Places365, Scene Recognition
RIT18	High-Res Multispectral Semantic Segmentation, Remote Sensing
SALICON2015	Saliency in Context, Eye-Tracking
SALICON2017	Saliency in Context, Eye-Tracking
VOC2012	Pascal Visual Object Classes Challenge
Vaihingen	Remote Sensing, Semantic Object Classification, Segmentation
YFCC100m	Yahoo Flickr Creative Commons 100 M pics

Command line tools

• datadings-write creates new dataset files.

• datadings-cat prints the (abbreviated) contents of a dataset file.

• datadings-shuffle shuffles an existing dataset file.

• datadings-merge merges two or more dataset files.

• datadings-split splits a dataset file into two or more subsets.

• datadings-bench runs some basic read performance benchmarks.

Basic usage

Each dataset defines modules to read and write in the datadings.sets package. For most datasets the reading module only contains additional metadata like class labels and distributions.

Let’s consider the MIT1003 dataset as an example.

MIT1003_write is an executable that creates dataset files. It can be called directly or through datadings-write. Three files will be written:

• MIT1003.msgpack contains sample data

• MIT1003.msgpack.index contains index for random access

• MIT1003.msgpack.md5 contains MD5 hashes of both files

Reading all samples sequentially, using a MsgpackReader as a context manager:

with MsgpackReader('MIT1003.msgpack') as reader:
    for sample in reader:
        [do dataset things]

This standard iterator returns dictionaries. Use the rawiter() method to get samples as messagepack encoded bytes instead.

Reading specific samples:

reader.seek_key('i14020903.jpeg')
print(reader.next()['key'])
reader.seek_index(100)
print(reader.next()['key'])

Reading samples as raw bytes:

raw = reader.rawnext()
for raw in reader.rawiter():
    print(type(raw), len(raw))

Number of samples:

print(len(reader))

You can also change the order and selection of iterated samples with augments. For example, to randomize the order of samples, wrap the reader in a Shuffler:

from datadings.reader import Shuffler
with Shuffler(MsgpackReader('MIT1003.msgpack')) as reader:
    for sample in reader:
        # do dataset things, but in random order!

A common use case is to iterate over the whole dataset multiple times. This can be done with the Cycler:

from datadings.reader import Cycler
with Cycler(MsgpackReader('MIT1003.msgpack')) as reader:
    for sample in reader:
        # do dataset things, but FOREVER!