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*Memos:

• My post explainsMNIST,EMNIST,QMNIST,ETLCDB,Kuzushiji andMoving MNIST.
• My post explainsFashion-MNIST,Caltech 101,Caltech 256,CelebA,CIFAR-10 andCIFAR-100.
• My post explainsOxford-IIIT Pet,Oxford 102 Flower,Stanford Cars,Places365,Flickr8k andFlickr30k.
• My post explainsPASCAL VOC,SUN Database,Kinetics Dataset andCityscapes.
• My post explains Image Classification(Recognition), Object Localization, Object Detection and Image Segmentation.
• My post explains Keypoint Detection(Landmark Detection), Image Matching, Object Tracking, Stereo Matching, Video Prediction, Optical Flow, Image Captioning.

(1)ImageNet(2009):

• has the 1,431,167 object images(1,281,167 for train, 50,000 for validation and 100,000 for test) each connected to the label from 1000 classes:*Memos:
  • Each class has the one or more names which represent the same things.
  • You can download the dataset fromKaggle. *You can also downloadILSVRC2012_devkit_t12.tar.gz,ILSVRC2012_img_train.tar andILSVRC2012_img_val.tar.
• isImageNet() in PyTorch. *My post explainsImageNet().

(2)LSUN(Large-scale Scene Understanding)(2015):

• has scene images and there are the 10 datasetsBedroom,Bridge,Church Outdoor,Classroom,Conference Room,Dining Room,Kitchen,Living Room,Restaurant andTower:
  • Bedroom has 3,033,342 bedroom images(3,033,042 for train and 300 for validation).
  • Bridge has 818,987 bridge images(818,687 for train and 300 for validation).
  • Church Outdoor has 126,527 church outdoor images(126,227 for train and 300 for validation).
  • Classroom has 126,527 classroom images(126,227 for train and 300 for validation).
  • Conference Room has 229,369 conference room images(229,069 for train and 300 for validation).
  • Dining Room has 657,871 dining room images(657,571 for train and 300 for validation).
  • Kitchen has 2,212,577 kitchen images(2,212,277 for train and 300 for validation).
  • Living Room has 1,316,102 living room images(1,315,802 for train and 300 for validation).
  • Restaurant has 626,631 restaurant images(626,331 for train and 300 for validation).
  • Tower has 708,564 tower images(708,264 for train and 300 for validation).
• isLSUN() in PyTorch but it hasthe bug.

(3)MS COCO(Microsoft Common Objects in Context)(2014):

• has object images with annotations and there are the 16 datasets2014 Train images and2014 Val images with2014 Train/Val annotations,2014 Test images with2014 Testing Image info,2015 Test images with2015 Testing Image info,2017 Train images and2017 Val images with2017 Train/Val annotations,2017 Stuff Train/Val annotations or2017 Panoptic Train/Val annotations,2017 Test images with2017 Testing Image info and2017 Unlabeled images with2017 Unlabeled Image info:*Memos:
  • 2014 Train images has 82,782 images.
  • 2014 Val images has 40,504 images.
  • 2014 Train/Val annotations has 123,286 annotations(82,782 for train and 40,504 for validation) for2014 Train images and2014 Val images.
  • 2014 Test images has 40,775 images.
  • 2014 Testing Image info has 40,775 annotations for2014 Test images.
  • 2015 Test images has 81,434 images.
  • 2015 Testing Image info has 101,722 annotations(81,434 annotations and 20,288 dev-annotations) for2015 Test images.
  • 2017 Train images has 118,287 images.
  • 2017 Val images has 5,000 images.
  • 2017 Train/Val annotations has 123,287 annotations(118,287 for train and 5,000 for validation) for2017 Train images and2017 Val images.
  • 2017 Stuff Train/Val annotations has 123,287 annotations(118,287 for train and 5,000 for validation) for2017 Train images and2017 Val images.
  • 2017 Panoptic Train/Val annotations has 123,287 annotations(118,287 for train and 5,000 for validation) for2017 Train images and2017 Val images.
  • 2017 Test images has 40,670 images.
  • 2017 Testing Image info has 40,670 annotations for2017 Test images.
  • 2017 Unlabeled images has 123,403 images.
  • 2017 Unlabeled Image info has 123,403 annotations for2017 Unlabeled images.
• is also called just COCO.
• isCocoDetection() andCocoCaptions():*Memos:
  • My post explainsCocoDetection().
  • My post explainsCocoCaptions().