Frameworks and datasets

Several DL frameworks and datasets have been developed in the last few years. various frameworks and libraries have also been used in order to expedite the work with good results. Through their use, the training process has become easier. Table 4 lists the most utilized frameworks and libraries.

Table 4 List of the most common frameworks and libraries

Framework

License

Core language

Year of release

Homepages

TensorFlow

Apache 2.0

C++ & Python

2015

https://www.tensorflow.org/

Keras

MIT

Python

2015

https://keras.io/

Caffe

BSD

C++

2015

http://caffe.berkeleyvision.org/

MatConvNet

Oxford

MATLAB

2014

http://www.vlfeat.org/matconvnet/

MXNet

Apache 2.0

C++

2015

https://github.com/dmlc/mxnet

CNTK

MIT

C++

2016

https://github.com/Microsoft/CNTK

Theano

BSD

Python

2008

http://deeplearning.net/software/theano/

Torch

BSD

C & Lua

2002

http://torch.ch/

DL4j

Apache 2.0

Java

2014

https://deeplearning4j.org/

Gluon

AWS Microsoft

C++

2017

https://github.com/gluon-api/gluon-api/

OpenDeep

MIT

Python

2017

http://www.opendeep.org/

Based on the star ratings on Github, as well as our own background in the field, TensorFlow is deemed the most effective and easy to use. It has the ability to work on several platforms. (Github is one of the biggest software hosting sites, while Github stars refer to how well-regarded a project is on the site). Moreover, there are several other benchmark datasets employed for different DL tasks. Some of these are listed in Table 5.

Table 5 Benchmark datasets

Dataset

Num. of classes

Applications

Link to dataset

ImageNet

1000

Image classification, object localization, object detection, etc.

http://www.image-net.org/

CIFAR10/100

10/100

Image classification

https://www.cs.toronto.edu/~kriz/cifar.html

MNIST

10

Classification of handwritten digits

http://yann.lecun.com/exdb/mnist/

Pascal VOC

20

Image classification, segmentation, object detection

http://host.robots.ox.ac.uk/pascal/VOC/voc2012/

Microsoft COCO

80

Object detection, semantic segmentation

https://cocodataset.org/#home

YFCC100M

8M

Video and image understanding

http://projects.dfki.unikl.de/yfcc100m/

YouTube-8M

4716

Video classification

https://research.google.com/youtube8m/

UCF-101

101

Human action detection

https://www.crcv.ucf.edu/data/UCF101.php

Kinetics

400

Human action detection

https://deepmind.com/research/open-source/kinetics

Google Open Images

350

Image classification, segmentation, object detection

https://storage.googleapis.com/openimages/web/index.html

CalTech101

101

Classification

http://www.vision.caltech.edu/Image_Datasets/Caltech101/

Labeled Faces in the Wild

–

Face recognition

http://vis-www.cs.umass.edu/lfw/

MIT-67 scene dataset

67

Indoor scene recognition

http://web.mit.edu/torralba/www/indoor.htm