GitLab

Add document entry for datasets.Omniglot

* Add docs to UCF101 and HMDB51 datasets

* Remove extra line

* Lint

Also add docs

* Added general reader for sn3 tensors in "pascalvincent" format

* Added class QMNIST into mnist.py

* QMNIST dataset: make some pt files smaller

* Change request from fmassa.

* read_sn3_pascalvincent_tensor: cse

* read_sn3_pascalvincent_tensor: check file size (when strict!=False)

* Fix lint

* More lint

* Add documentation and expose QMNIST to dataset namespace

* add USPS dataset

* minor fixes

* Improvements to the USPS dataset

Add it to the documentation, expose it to torchvision.datasets
and inherit from VisionDataset

* Added Semantic Boundaries Dataset

* Updated docs and datasets importing module

* Improved docs

* Add KMNIST

* Add documentation

* Add KMNIST to module's __all__ list

* Add Cityscapes Dataset

* Rename 'label' to 'semantic' to make the meaning more clear

* Add support for gtCoarse target set

* VOC Dataset, linted, flak8 passing, samples on gist.

* Double backtick on values.

* Apply suggestions from code review

Add suggestions from @ellisbrown, using dict of dicts instead of array index.
Co-Authored-By: bpinaya <bpg_92@hotmail.com>

* Fixed errors with the new comments.

* Added documentation on RST

* Removed getBB, added parse_voc_xml, variable naming change.

* Removed unused variable, removed VOC_CLASSES, two new gists for test.

* Add Flickr8k and Flickr30k Datasets

* Add Flickr to the dataset docs

* Sort ids, glob during construction

* annFile -> ann_file

* Fix undefined variable name bug

* Add SBU Captioned Photo Dataset

* Add SBU to the dataset docs

* Adding tests to ImageFolder

* Adding DatasetFolder class

* Fix tests for pytest and code for lint checker

* Adding mock to requirements for ImageFolder tests

* Remove mocks from requirements

This reverts commit ab03dc43.

* Adding tests to ImageFolder

* Adding DatasetFolder class

* Fix tests for pytest and code for lint checker

* Adding mock to requirements for ImageFolder tests

It was pointing to an old version where the validation pre-processing was the same as the training pre-processing, and thus had randomness in it (instead of CenterCrop).

* EMNIST dataset + speedup *MNIST preprocessing

added CIFAR100 autoclass in docs