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datadings-3.4.6
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مشاهده بیشترتوضیحات
datadings is a collection of tools to prepare datasets for machine learning. It's easy to use, space-efficient, and blazingly fast.
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | datadings-3.4.6 |
| نام | datadings |
| نسخه کتابخانه | 3.4.6 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Joachim Folz |
| ایمیل نویسنده | joachim.folz@dfki.de |
| آدرس صفحه اصلی | - |
| آدرس اینترنتی | https://pypi.org/project/datadings/ |
| مجوز | MIT |
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 <http://msgpack.org>`_ is used as an efficient storage
format for most supported datasets.
Check out the `documentation <https://datadings.readthedocs.io>`_
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
================ ============================
.. _ADE20k: http://groups.csail.mit.edu/vision/datasets/ADE20K/
.. _ANP460:
.. _CAMVID: http://mi.eng.cam.ac.uk/research/projects/VideoRec/CamVid/
.. _CAT2000: http://saliency.mit.edu/results_cat2000.html
.. _CIFAR: https://www.cs.toronto.edu/~kriz/cifar.html
.. _Cityscapes: https://www.cityscapes-dataset.com/
.. _Coutrot1: http://antoinecoutrot.magix.net/public/databases.html
.. _FIGRIMFixation: http://figrim.mit.edu/index_eyetracking.html
.. _ILSVRC2012: http://www.image-net.org/challenges/LSVRC/2012/
.. _ImageNet21k: https://image-net.org/download.php
.. _InriaBuildings: https://project.inria.fr/aerialimagelabeling/
.. _MIT300: http://saliency.mit.edu/results_mit300.html
.. _MIT1003: http://people.csail.mit.edu/tjudd/WherePeopleLook/index.html
.. _Places365: http://places2.csail.mit.edu/
.. _Places2017: http://places.csail.mit.edu/
.. _RIT18: https://github.com/rmkemker/RIT-18
.. _SALICON2015: http://salicon.net/challenge-2015/
.. _SALICON2017: http://salicon.net/challenge-2017/
.. _Vaihingen: http://www2.isprs.org/commissions/comm3/wg4/2d-sem-label-vaihingen.html
.. _VOC2012: http://host.robots.ox.ac.uk/pascal/VOC/voc2012/
.. _YFCC100m: http://yfcc100m.appspot.com/about
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!
نیازمندی
| مقدار | نام |
|---|---|
| >=3.0.0 | gdown |
| !=0.6.0,<2.0.0,>=0.5.0 | msgpack |
| <1.0.0,>=0.4.2 | msgpack-numpy |
| >=4.0.4 | natsort |
| <2.0.0,>=1.17.0 | numpy |
| <10.0.0,>=7.0.0 | Pillow |
| <3.0.0,>=2.0.0 | requests |
| <2.0.0,>=0.17.0 | scipy |
| <2.0.0,>=1.0.2 | simplebloom |
| <2.0.0,>=1.2.5 | simplejpeg |
| <5.0.0,>=4.23.0 | tqdm |
| >=18.1.1 | pyzmq |
| >=2.4.0 | GDAL |
نحوه نصب
نصب پکیج whl datadings-3.4.6:
pip install datadings-3.4.6.whl
نصب پکیج tar.gz datadings-3.4.6:
pip install datadings-3.4.6.tar.gz