Cleanup README.md

CONTRIBUTING.md

@@ -30,30 +30,49 @@ clear and has sufficient instructions to be able to reproduce the issue.
 
 ## Development installation
 
-### Install PyTorch Nightly 
+
+### Dependencies
+
+Start by installing the **nightly** build of PyTorch following the [official
+instructions](https://pytorch.org/get-started/locally/).
+
+**Optionally**, install `libpng` and `libjpeg-turbo` if you want to enable
+support for
+native encoding / decoding of PNG and JPEG formats in
+[torchvision.io](https://pytorch.org/vision/stable/io.html#image):
 
 ```bash
-conda install pytorch -c pytorch-nightly
-# or with pip (see https://pytorch.org/get-started/locally/)
-# pip install numpy
-# pip install --pre torch -f https://download.pytorch.org/whl/nightly/cu102/torch_nightly.html
+conda install libpng libjpeg-turbo -c pytorch
 ```
 
-### Install Torchvision
+Note: you can use the `TORCHVISION_INCLUDE` and `TORCHVISION_LIBRARY`
+environment variables to tell the build system where to find those libraries if
+they are in specific locations. Take a look at
+[setup.py](https://github.com/pytorch/vision/blob/main/setup.py) for more
+details.
+
+### Clone and install torchvision
 
 ```bash
 git clone https://github.com/pytorch/vision.git
 cd vision
-python setup.py develop
+python setup.py develop  # use install instead of develop if you don't care about development.
 # or, for OSX
 # MACOSX_DEPLOYMENT_TARGET=10.9 CC=clang CXX=clang++ python setup.py develop
-# for C++ debugging, please use DEBUG=1
+# for C++ debugging, use DEBUG=1
 # DEBUG=1 python setup.py develop
-pip install flake8 typing mypy pytest pytest-mock scipy
 ```
-You may also have to install `libpng-dev` and `libjpeg-turbo8-dev` libraries:
-```bash
-conda install libpng jpeg
+
+By default, GPU support is built if CUDA is found and `torch.cuda.is_available()` is true. It's possible to force
+building GPU support by setting `FORCE_CUDA=1` environment variable, which is useful when building a docker image.
+
+We don't officially support building from source using `pip`, but _if_ you do, you'll need to use the
+`--no-build-isolation` flag.
+
+Other development dependencies include:
+
+```
+pip install flake8 typing mypy pytest pytest-mock scipy
 ```
 
 ## Development Process
@@ -192,7 +211,7 @@ Please refer to the guidelines in [Contributing to Torchvision - Models](https:/
 
 ### New dataset
 
-More details on how to add a new dataset will be provided later. Please, do not send any PR with a new dataset without discussing 
+Please, do not send any PR with a new dataset without discussing 
 it in an issue as, most likely, it will not be accepted.
 
 ### Pull Request

README.md

@@ -8,8 +8,14 @@ vision.
 
 ## Installation
 
-We recommend Anaconda as Python package management system. Please refer to [pytorch.org](https://pytorch.org/) for the
-detail of PyTorch (`torch`) installation. The following is the corresponding `torchvision` versions and supported Python
+Please refer to the [official
+instructions](https://pytorch.org/get-started/locally/) to install the stable
+versions of `torch` and `torchvision` on your system.
+
+To build source, refer to our [contributing
+page](https://github.com/pytorch/vision/blob/main/CONTRIBUTING.md#development-installation).
+
+The following is the corresponding `torchvision` versions and supported Python
 versions.
 
 | `torch`            | `torchvision`      | Python              |
@@ -39,55 +45,18 @@ versions.
 
 </details>
 
-Anaconda:
-
-```
-conda install torchvision -c pytorch
-```
-
-pip:
-
-```
-pip install torchvision
-```
+## Image Backends
 
-From source:
-
-```
-python setup.py install
-# or, for OSX
-# MACOSX_DEPLOYMENT_TARGET=10.9 CC=clang CXX=clang++ python setup.py install
-```
-
-We don't officially support building from source using `pip`, but _if_ you do, you'll need to use the
-`--no-build-isolation` flag. In case building TorchVision from source fails, install the nightly version of PyTorch
-following the linked guide on the
-[contributing page](https://github.com/pytorch/vision/blob/main/CONTRIBUTING.md#development-installation) and retry the
-install.
-
-By default, GPU support is built if CUDA is found and `torch.cuda.is_available()` is true. It's possible to force
-building GPU support by setting `FORCE_CUDA=1` environment variable, which is useful when building a docker image.
+Torchvision currently supports the following image backends:
 
-## Image Backend
+- torch tensors
+- PIL images:
+    - [Pillow](https://python-pillow.org/)
+    - [Pillow-SIMD](https://github.com/uploadcare/pillow-simd) - a **much faster** drop-in replacement for Pillow with SIMD.
 
-Torchvision currently supports the following image backends:
+Read more in in our [docs](https://pytorch.org/vision/stable/transforms.html).
 
-- [Pillow](https://python-pillow.org/) (default)
-- [Pillow-SIMD](https://github.com/uploadcare/pillow-simd) - a **much faster** drop-in replacement for Pillow with SIMD.
-  If installed will be used as the default.
-- [accimage](https://github.com/pytorch/accimage) - if installed can be activated by calling
-  `torchvision.set_image_backend('accimage')`
-- [libpng](http://www.libpng.org/pub/png/libpng.html) - can be installed via conda `conda install libpng` or any of the
-  package managers for debian-based and RHEL-based Linux distributions.
-- [libjpeg](http://ijg.org/) - can be installed via conda `conda install jpeg` or any of the package managers for
-  debian-based and RHEL-based Linux distributions. [libjpeg-turbo](https://libjpeg-turbo.org/) can be used as well.
-
-**Notes:** `libpng` and `libjpeg` are optional dependencies. If any of them is available on the system, 
-torchvision will provide encoding/decoding image functionalities from `torchvision.io.image`. 
-When building torchvision from source, `libpng` and `libjpeg` can be found on the standard library locations.
-Otherwise, please use `TORCHVISION_INCLUDE` and `TORCHVISION_LIBRARY` environment variables to set up include and library paths.
-
-## Video Backend
+## [UNSTABLE] Video Backend
 
 Torchvision currently supports the following video backends:
 

docs/source/io.rst

@@ -1,11 +1,37 @@
-Reading/Writing images and videos
-=================================
+Decoding / Encoding images and videos
+=====================================
 
 .. currentmodule:: torchvision.io
 
 The :mod:`torchvision.io` package provides functions for performing IO
-operations. They are currently specific to reading and writing video and
-images.
+operations. They are currently specific to reading and writing images and
+videos.
+
+Images
+------
+
+.. autosummary::
+    :toctree: generated/
+    :template: function.rst
+
+    read_image
+    decode_image
+    encode_jpeg
+    decode_jpeg
+    write_jpeg
+    encode_png
+    decode_png
+    write_png
+    read_file
+    write_file
+
+.. autosummary::
+    :toctree: generated/
+    :template: class.rst
+
+    ImageReadMode
+
+
 
 Video
 -----
@@ -20,7 +46,7 @@ Video
 
 
 Fine-grained video API
-----------------------
+^^^^^^^^^^^^^^^^^^^^^^
 
 In addition to the :mod:`read_video` function, we provide a high-performance 
 lower-level API for more fine-grained control compared to the :mod:`read_video` function.
@@ -61,28 +87,3 @@ Example of inspecting a video:
     # the constructor we select a default video stream, but
     # in practice, we can set whichever stream we would like 
     video.set_current_stream("video:0")
-
-
-Image
------
-
-.. autosummary::
-    :toctree: generated/
-    :template: class.rst
-
-    ImageReadMode
-
-.. autosummary::
-    :toctree: generated/
-    :template: function.rst
-
-    read_image
-    decode_image
-    encode_jpeg
-    decode_jpeg
-    write_jpeg
-    encode_png
-    decode_png
-    write_png
-    read_file
-    write_file