Datasets for Computer Vision (4)

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*Memos:

• 
  My post explains MNIST, EMNIST, QMNIST, ETLCDB, Kuzushiji and Moving MNIST.


• 
  My post explains Fashion-MNIST, Caltech 101, Caltech 256, CelebA, CIFAR-10 and CIFAR-100.


• 
  My post explains Oxford-IIIT Pet, Oxford 102 Flower, Stanford Cars, Places365, Flickr8k and Flickr30k.

(1) ImageNet(2009):

• has the 1,331,167 object images(1,281,167 for train and 50,000 for validation) 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 ILSVRC2012_devkit_t12.tar.gz, ILSVRC2012_img_train.tar and ILSVRC2012_img_val.tar.
  
  
  
  
  
  



• is ImageNet() in PyTorch.

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

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



• is LSUN() in PyTorch but it has the bug.

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

• has object images with annotations and there are the 16 datasets 2014 Train images and 2014 Val images with 2014 Train/Val annotations, 2014 Test images with 2014 Testing Image info, 2015 Test images with 2015 Testing Image info, 2017 Train images and 2017 Val images with 2017 Train/Val annotations, 2017 Stuff Train/Val annotations or 2017 Panoptic Train/Val annotations, 2017 Test images with 2017 Testing Image info and 2017 Unlabeled images with 2017 Unlabeled Image info:
  *Memos:
  
  
  
  
  • 
    2014 Train images has 82,782 images.
  
  
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    2014 Val images has 40,504 images.
  
  
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    2014 Train/Val annotations has 123,286 annotations(82,782 for train and 40,504 for validation) for 2014 Train images and 2014 Val images.
  
  
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    2014 Test images has 40,775 images.
  
  
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    2014 Testing Image info has 40,775 annotations for 2014 Test images.
  
  
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    2015 Test images has 81,434 images.
  
  
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    2015 Testing Image info has 81,434 annotations for 2015 Test images.
  
  
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    2017 Train images has 118,287 images.
  
  
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    2017 Val images has 5,000 images.
  
  
  • 
    2017 Train/Val annotations has 123,287 annotations(118,287 for train and 5,000 for validation) for 2017 Train images and 2017 Val images.
  
  
  • 
    2017 Stuff Train/Val annotations has 123,287 annotations(118,287 for train and 5,000 for validation) for 2017 Train images and 2017 Val images.
  
  
  • 
    2017 Panoptic Train/Val annotations has 123,287 annotations(118,287 for train and 5,000 for validation) for 2017 Train images and 2017 Val images.
  
  
  • 
    2017 Test images has 40,670 images.
  
  
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    2017 Testing Image info has 40,670 annotations for 2017 Test images.
  
  
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    2017 Unlabeled images has 123,403 images.
  
  
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    2017 Unlabeled Image info has 123,403 annotations for 2017 Unlabeled images.
  
  
  
  
  
  



• is also called just COCO.



• is CocoDetection() or CocoCaptions()