Updated datasets.rst, combined all commits (pytorch#931)

Added MNIST in the docs

Updated incomplete cifar doc

Updated the datasets.rst to include all datasets

Author: skrish13

docs/source/torchvision/datasets.rst

@@ -3,11 +3,13 @@ torchvision.datasets
 
 The following dataset loaders are available:
 
+-  `MNIST`_
 -  `COCO (Captioning and Detection)`_
 -  `LSUN Classification`_
 -  `ImageFolder`_
 -  `Imagenet-12`_
 -  `CIFAR10 and CIFAR100`_
+-  `STL10`_
 
 Datasets have the API:
 
@@ -33,6 +35,15 @@ but they all take the keyword args:
    transforms it. For example, take in the caption string and return a
    tensor of word indices.
 
+MNIST
+~~~~~
+
+``dset.MNIST(root, train=True, transform=None, target_transform=None, download=False)``
+
+- ``root`` : root directory of dataset where ``processed/training.pt`` and  ``processed/test.pt`` exist.
+- ``train`` : ``True`` = Training set, ``False`` = Test set
+-  ``download`` : ``True`` = downloads the dataset from the internet and puts it in root directory. If dataset already downloaded, place the processed dataset (function available in mnist.py) in the ``processed`` folder.
+
 COCO
 ~~~~
 
@@ -82,11 +93,42 @@ LSUN
 ``dset.LSUN(db_path, classes='train', [transform, target_transform])``
 
 -  db\_path = root directory for the database files
--  classes =
--  ‘train’ - all categories, training set
--  ‘val’ - all categories, validation set
--  ‘test’ - all categories, test set
--  [‘bedroom\_train’, ‘church\_train’, …] : a list of categories to load
+-  ``classes`` = ``‘train’`` (all categories, training set), ``‘val’`` (all categories, validation set), ``‘test’`` (all categories, test set)
+-  [``‘bedroom\_train’``, ``‘church\_train’``, …] : a list of categories to load
+
+ImageFolder
+~~~~~~~~~~~
+
+A generic data loader where the images are arranged in this way:
+
+::
+
+    root/dog/xxx.png
+    root/dog/xxy.png
+    root/dog/xxz.png
+
+    root/cat/123.png
+    root/cat/nsdf3.png
+    root/cat/asd932_.png
+
+``dset.ImageFolder(root="root folder path", [transform, target_transform])``
+
+It has the members:
+
+-  ``self.classes`` - The class names as a list
+-  ``self.class_to_idx`` - Corresponding class indices
+-  ``self.imgs`` - The list of (image path, class-index) tuples
+
+Imagenet-12
+~~~~~~~~~~~
+
+This is simply implemented with an ImageFolder dataset.
+
+The data is preprocessed `as described
+here <https://github.com/facebook/fb.resnet.torch/blob/master/INSTALL.md#download-the-imagenet-dataset>`__
+
+`Here is an
+example <https://github.com/pytorch/examples/blob/27e2a46c1d1505324032b1d94fc6ce24d5b67e97/imagenet/main.py#L48-L62>`__.
 
 CIFAR
 ~~~~~
@@ -99,11 +141,22 @@ CIFAR
    ``cifar-10-batches-py``
 -  ``train`` : ``True`` = Training set, ``False`` = Test set
 -  ``download`` : ``True`` = downloads the dataset from the internet and
-   puts it in root directory. If dataset already downloaded, do
+   puts it in root directory. If dataset already downloaded, doesn't do anything.
+
+STL10
+~~~~~
+
+``dset.STL10(root, split='train', transform=None, target_transform=None, download=False)``
+
+-  ``root`` : root directory of dataset where there is folder ``stl10_binary``
+-  ``split`` : ``'train'`` = Training set, ``'test'`` = Test set, ``'unlabeled'`` = Unlabeled set,    ``'train+unlabeled'`` = Training + Unlabeled set (missing label marked as ``-1``)
+-  ``download`` : ``True`` = downloads the dataset from the internet and puts it in root directory. If dataset already downloaded, doesn't do anything.
 
+.. _MNIST: #mnist
 .. _COCO (Captioning and Detection): #coco
 .. _LSUN Classification: #lsun
 .. _ImageFolder: #imagefolder
 .. _Imagenet-12: #imagenet-12
 .. _CIFAR10 and CIFAR100: #cifar
-.. _COCO API to be installed: https://github.com/pdollar/coco/tree/master/PythonAPI
+.. _STL10: #stl10
+.. _COCO API to be installed: https://github.com/pdollar/coco/tree/master/PythonAPI