BENCHMARK DATABASES

Image classification or detection or segmentation

List of datasets are used in the field of image processing and computer vision:

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

▪ CIFAR 10/100 : https://www.cs.toronto.edu/~kriz/cifar.html

▪ SVHN/ SVHN2: http://ufldl.stanford.edu/housenumbers/

▪ CalTech 101/256: http://www.vision.caltech.edu/Image_Datasets/Caltech101/

▪ STL-10 : https://cs.stanford.edu/~acoates/stl10/

▪ NORB : http://www.cs.nyu.edu/~ylclab/data/norbv1.0/

▪ SUN-dataset: http://groups.csail.mit.edu/vision/SUN/

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

▪ National Data Science Bowl Competition: http://www.datasciencebowl.com/

▪ COIL 20/100: http://www.cs.columbia.edu/CAVE/software/softlib/coil20.php http://www.cs.columbia.edu/CAVE/software/

softlib/coil-100.php

▪ MS COCO DATASET : http://mscoco.org/

▪ MIT-67 scene dataset: http://web.mit.edu/torralba/www/indoor.html

▪ Caltech-UCSD Birds-200 dataset:http://www.vision.caltech.edu/visipedia/CUB-200-2011.html

▪ Pascal VOC 2007 dataset: http://host.robots.ox.ac.uk/pascal/VOC/voc2007/

▪ H3D Human Attributes dataset: https://www2.eecs.berkeley.edu/Research/Projects/CS/vision/shape/poselets/

▪ Face recognition dataset: http://viswww.cs.umass.edu/lfw/

▪ For more data-set visit : https://www.kaggle.com/

▪ http://homepages.inf.ed.ac.uk/rbf/CVonline/Imagedbase.htm

Books on deep learning

▪ https://github.com/HFTrader/DeepLearningBookhttp s://github.com/janishar/mit-deep-learning-book-pdf

▪ http://www.deeplearningbook.org/

Tutorials on deep learning

▪ http://deeplearning.net/tutorial/

▪ http://deeplearning.stanford.edu/tutorial/

▪ http://deeplearning.net/tutorial/deeplearning.pdf

▪ Courses on Reinforcement Learning: http://rll.berkeley.edu/deeprlcourse/