*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 ImageNet, LSUN and MS COCO.
- My post explains PASCAL VOC, SUN Database, Kinetics Dataset and Cityscapes.
- 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) Oxford-IIIT Pet(2012):
- has the 7,349 cat and dog images(3,680 for train and validation, 3,669 for test) each connected to the label from 37 classes:
*Memos:
- Each class has roughly 200 images.
- 3,680 for train or train and validation and 3,669 for test.
- is used for Image Classification and Fine-Grained Image Classification.
- is OxfordIIITPet() in PyTorch. *My post explains
OxfordIIITPet().
(2) Oxford 102 Flower(2008):
- has 8,189 flower images(1,020 for train, 1,020 for validation and 6,149 for test) with the 102 categories(classes). *Each class has 40 to 258 images.
- is used for Fine-Grained Flower Classification.
- is Flowers102() in PyTorch. *My post explains
Flowers102().
(3) Stanford Cars(2013):
- has 16185 car images(8,144 for train and 8,041 for test) with 196 classes.
- is used for Fine-Grained Flower Classification.
- is StanfordCars() in PyTorch. *My post explains
StanfordCars().
(4) Places365(2017):
- has scene images with the 365 scene categories(classes) out of the 434 scene categories(classes) in the Places Database and there are Places365-Standard, Places365-Challenge and Places-Extra69 as you can see here:
*Memos:
- Places365-Standard has 2,168,460 images(1,803,460 for train, 36,500 for validation and 328,500 for test) with the 365 categories(classes) out of the 434 categories(classes) in the Places Database. *There are 50 images per category(class) in the validation set and 900 images per category(class) in the test set.
- Places365-Challenge has 8,391,628 images(8,026,628 for train, 36,500 for validation and 328,500 for test), adding 6,223,168 extra images to the train set of Places365-Standard.
- Places-Extra69 has 105,321 images(98,721 for train and 6,600 for test) with the extra 69 categories(classes) out of the 434 categories(classes) in the Places Database. *Currently, it cannot be downloaded.
- is used for Scene Classification.
- is Places365() in PyTorch. *My post explains
Places365().
(5) Flickr8k(2013):
- has the 8,091 images obtained from flickr with the five different captions for each image.
- is used for Image Captioning.
- is Flickr8k() in PyTorch but it doesn't explain how to set up the dataset to it so I don't know how to load the dataset with it.
(6) Flickr30k(2015):
- has 31,784 images obtained from flickr with the five different captions for each image.
- is used for Image Captioning.
- is Flickr8k() in PyTorch but it doesn't explain how to set up the dataset to it so I don't know how to load the dataset with it.
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