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image and video datasets and models for torch deep learning

Project description

torchvision

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The torchvision package consists of popular datasets, model architectures, and common image transformations for computer vision.

Installation

TorchVision requires PyTorch 1.1 or newer.

Anaconda:

conda install torchvision -c pytorch

pip:

pip install torchvision

From source:

python setup.py install
# or, for OSX
# MACOSX_DEPLOYMENT_TARGET=10.9 CC=clang CXX=clang++ python setup.py 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.

Image Backend

Torchvision currently supports the following image backends:

  • Pillow (default)
  • Pillow-SIMD - a much faster drop-in replacement for Pillow with SIMD. If installed will be used as the default.
  • accimage - if installed can be activated by calling torchvision.set_image_backend('accimage')

C++ API

TorchVision also offers a C++ API that contains C++ equivalent of python models.

Installation From source:

mkdir build
cd build
cmake ..
make
make install

Documentation

You can find the API documentation on the pytorch website: http://pytorch.org/docs/master/torchvision/

Contributing

We appreciate all contributions. If you are planning to contribute back bug-fixes, please do so without any further discussion. If you plan to contribute new features, utility functions or extensions, please first open an issue and discuss the feature with us.

Project details


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