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torchvision

https://travis-ci.org/pytorch/vision.svg?branch=masterhttps://pepy.tech/badge/torchvisionhttps://img.shields.io/badge/dynamic/json.svg?label=docs&url=https%3A%2F%2Fpypi.org%2Fpypi%2Ftorchvision%2Fjson&query=%24.info.version&colorB=brightgreen&prefix=v

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

Installation

TorchVision requires PyTorch 1.4 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
# Add -DWITH_CUDA=on support for the CUDA if needed
cmake ..
make
make install

Once installed, the library can be accessed in cmake (after properly configuring CMAKE_PREFIX_PATH) via the TorchVision::TorchVision target:

find_package(TorchVision REQUIRED)
target_link_libraries(my-target PUBLIC TorchVision::TorchVision)

The TorchVision package will also automatically look for the Torch and pybind11 packages and add them as dependencies to my-target, so make sure that they are also available to cmake via the CMAKE_PREFIX_PATH.

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.

Disclaimer on Datasets

This is a utility library that downloads and prepares public datasets. We do not host or distribute these datasets, vouch for their quality or fairness, or claim that you have license to use the dataset. It is your responsibility to determine whether you have permission to use the dataset under the dataset's license.

If you're a dataset owner and wish to update any part of it (description, citation, etc.), or do not want your dataset to be included in this library, please get in touch through a GitHub issue. Thanks for your contribution to the ML community!

About

Datasets, Transforms and Models specific to Computer Vision

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GitHub - eldarkurtic/vision: Datasets, Transforms and Models specific to Computer Vision · GitHub
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torchvision
https://travis-ci.org/pytorch/vision.svg?branch=masterhttps://pepy.tech/badge/torchvisionhttps://img.shields.io/badge/dynamic/json.svg?label=docs&url=https%3A%2F%2Fpypi.org%2Fpypi%2Ftorchvision%2Fjson&query=%24.info.version&colorB=brightgreen&prefix=v
The torchvision package consists of popular datasets, model architectures, and common image transformations for computer vision.
Installation
TorchVision requires PyTorch 1.4 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
# Add -DWITH_CUDA=on support for the CUDA if needed
cmake ..
make
make install
Once installed, the library can be accessed in cmake (after properly configuring CMAKE_PREFIX_PATH) via the TorchVision::TorchVision target:
find_package(TorchVision REQUIRED)
target_link_libraries(my-target PUBLIC TorchVision::TorchVision)
The TorchVision package will also automatically look for the Torch and pybind11 packages and add them as dependencies to my-target,
so make sure that they are also available to cmake via the CMAKE_PREFIX_PATH.
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.
Disclaimer on Datasets
This is a utility library that downloads and prepares public datasets. We do not host or distribute these datasets, vouch for their quality or fairness, or claim that you have license to use the dataset. It is your responsibility to determine whether you have permission to use the dataset under the dataset's license.
If you're a dataset owner and wish to update any part of it (description, citation, etc.), or do not want your dataset to be included in this library, please get in touch through a GitHub issue. Thanks for your contribution to the ML community!
About
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GitHub - eldarkurtic/vision: Datasets, Transforms and Models specific to Computer Vision · GitHub
Skip to content
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torchvision
https://travis-ci.org/pytorch/vision.svg?branch=masterhttps://pepy.tech/badge/torchvisionhttps://img.shields.io/badge/dynamic/json.svg?label=docs&url=https%3A%2F%2Fpypi.org%2Fpypi%2Ftorchvision%2Fjson&query=%24.info.version&colorB=brightgreen&prefix=v
The torchvision package consists of popular datasets, model architectures, and common image transformations for computer vision.
Installation
TorchVision requires PyTorch 1.4 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
# Add -DWITH_CUDA=on support for the CUDA if needed
cmake ..
make
make install
Once installed, the library can be accessed in cmake (after properly configuring CMAKE_PREFIX_PATH) via the TorchVision::TorchVision target:
find_package(TorchVision REQUIRED)
target_link_libraries(my-target PUBLIC TorchVision::TorchVision)
The TorchVision package will also automatically look for the Torch and pybind11 packages and add them as dependencies to my-target,
so make sure that they are also available to cmake via the CMAKE_PREFIX_PATH.
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.
Disclaimer on Datasets
This is a utility library that downloads and prepares public datasets. We do not host or distribute these datasets, vouch for their quality or fairness, or claim that you have license to use the dataset. It is your responsibility to determine whether you have permission to use the dataset under the dataset's license.
If you're a dataset owner and wish to update any part of it (description, citation, etc.), or do not want your dataset to be included in this library, please get in touch through a GitHub issue. Thanks for your contribution to the ML community!
About
Datasets, Transforms and Models specific to Computer Vision
Resources
Readme
BSD-3-Clause license
Activity
Stars
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0 watching
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try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '
GitHub - eldarkurtic/vision: Datasets, Transforms and Models specific to Computer Vision · GitHub
Skip to content
BranchesTags
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torchvision
https://travis-ci.org/pytorch/vision.svg?branch=masterhttps://pepy.tech/badge/torchvisionhttps://img.shields.io/badge/dynamic/json.svg?label=docs&url=https%3A%2F%2Fpypi.org%2Fpypi%2Ftorchvision%2Fjson&query=%24.info.version&colorB=brightgreen&prefix=v
The torchvision package consists of popular datasets, model architectures, and common image transformations for computer vision.
Installation
TorchVision requires PyTorch 1.4 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
# Add -DWITH_CUDA=on support for the CUDA if needed
cmake ..
make
make install
Once installed, the library can be accessed in cmake (after properly configuring CMAKE_PREFIX_PATH) via the TorchVision::TorchVision target:
find_package(TorchVision REQUIRED)
target_link_libraries(my-target PUBLIC TorchVision::TorchVision)
The TorchVision package will also automatically look for the Torch and pybind11 packages and add them as dependencies to my-target,
so make sure that they are also available to cmake via the CMAKE_PREFIX_PATH.
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.
Disclaimer on Datasets
This is a utility library that downloads and prepares public datasets. We do not host or distribute these datasets, vouch for their quality or fairness, or claim that you have license to use the dataset. It is your responsibility to determine whether you have permission to use the dataset under the dataset's license.
If you're a dataset owner and wish to update any part of it (description, citation, etc.), or do not want your dataset to be included in this library, please get in touch through a GitHub issue. Thanks for your contribution to the ML community!
About
Datasets, Transforms and Models specific to Computer Vision
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Readme
BSD-3-Clause license
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try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '
GitHub - eldarkurtic/vision: Datasets, Transforms and Models specific to Computer Vision · GitHub
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BranchesTags
Repository files navigation
torchvision
https://travis-ci.org/pytorch/vision.svg?branch=masterhttps://pepy.tech/badge/torchvisionhttps://img.shields.io/badge/dynamic/json.svg?label=docs&url=https%3A%2F%2Fpypi.org%2Fpypi%2Ftorchvision%2Fjson&query=%24.info.version&colorB=brightgreen&prefix=v
The torchvision package consists of popular datasets, model architectures, and common image transformations for computer vision.
Installation
TorchVision requires PyTorch 1.4 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
# Add -DWITH_CUDA=on support for the CUDA if needed
cmake ..
make
make install
Once installed, the library can be accessed in cmake (after properly configuring CMAKE_PREFIX_PATH) via the TorchVision::TorchVision target:
find_package(TorchVision REQUIRED)
target_link_libraries(my-target PUBLIC TorchVision::TorchVision)
The TorchVision package will also automatically look for the Torch and pybind11 packages and add them as dependencies to my-target,
so make sure that they are also available to cmake via the CMAKE_PREFIX_PATH.
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.
Disclaimer on Datasets
This is a utility library that downloads and prepares public datasets. We do not host or distribute these datasets, vouch for their quality or fairness, or claim that you have license to use the dataset. It is your responsibility to determine whether you have permission to use the dataset under the dataset's license.
If you're a dataset owner and wish to update any part of it (description, citation, etc.), or do not want your dataset to be included in this library, please get in touch through a GitHub issue. Thanks for your contribution to the ML community!
About
Datasets, Transforms and Models specific to Computer Vision
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GitHub - eldarkurtic/vision: Datasets, Transforms and Models specific to Computer Vision · GitHub
Skip to content
BranchesTags
Repository files navigation
torchvision
https://travis-ci.org/pytorch/vision.svg?branch=masterhttps://pepy.tech/badge/torchvisionhttps://img.shields.io/badge/dynamic/json.svg?label=docs&url=https%3A%2F%2Fpypi.org%2Fpypi%2Ftorchvision%2Fjson&query=%24.info.version&colorB=brightgreen&prefix=v
The torchvision package consists of popular datasets, model architectures, and common image transformations for computer vision.
Installation
TorchVision requires PyTorch 1.4 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
# Add -DWITH_CUDA=on support for the CUDA if needed
cmake ..
make
make install
Once installed, the library can be accessed in cmake (after properly configuring CMAKE_PREFIX_PATH) via the TorchVision::TorchVision target:
find_package(TorchVision REQUIRED)
target_link_libraries(my-target PUBLIC TorchVision::TorchVision)
The TorchVision package will also automatically look for the Torch and pybind11 packages and add them as dependencies to my-target,
so make sure that they are also available to cmake via the CMAKE_PREFIX_PATH.
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.
Disclaimer on Datasets
This is a utility library that downloads and prepares public datasets. We do not host or distribute these datasets, vouch for their quality or fairness, or claim that you have license to use the dataset. It is your responsibility to determine whether you have permission to use the dataset under the dataset's license.
If you're a dataset owner and wish to update any part of it (description, citation, etc.), or do not want your dataset to be included in this library, please get in touch through a GitHub issue. Thanks for your contribution to the ML community!
About
Datasets, Transforms and Models specific to Computer Vision
Resources
Readme
BSD-3-Clause license
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GitHub - eldarkurtic/vision: Datasets, Transforms and Models specific to Computer Vision · GitHub
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torchvision
https://travis-ci.org/pytorch/vision.svg?branch=masterhttps://pepy.tech/badge/torchvisionhttps://img.shields.io/badge/dynamic/json.svg?label=docs&url=https%3A%2F%2Fpypi.org%2Fpypi%2Ftorchvision%2Fjson&query=%24.info.version&colorB=brightgreen&prefix=v
The torchvision package consists of popular datasets, model architectures, and common image transformations for computer vision.
Installation
TorchVision requires PyTorch 1.4 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
# Add -DWITH_CUDA=on support for the CUDA if needed
cmake ..
make
make install
Once installed, the library can be accessed in cmake (after properly configuring CMAKE_PREFIX_PATH) via the TorchVision::TorchVision target:
find_package(TorchVision REQUIRED)
target_link_libraries(my-target PUBLIC TorchVision::TorchVision)
The TorchVision package will also automatically look for the Torch and pybind11 packages and add them as dependencies to my-target,
so make sure that they are also available to cmake via the CMAKE_PREFIX_PATH.
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.
Disclaimer on Datasets
This is a utility library that downloads and prepares public datasets. We do not host or distribute these datasets, vouch for their quality or fairness, or claim that you have license to use the dataset. It is your responsibility to determine whether you have permission to use the dataset under the dataset's license.
If you're a dataset owner and wish to update any part of it (description, citation, etc.), or do not want your dataset to be included in this library, please get in touch through a GitHub issue. Thanks for your contribution to the ML community!
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GitHub - eldarkurtic/vision: Datasets, Transforms and Models specific to Computer Vision · GitHub
Skip to content
BranchesTags
Repository files navigation
torchvision
https://travis-ci.org/pytorch/vision.svg?branch=masterhttps://pepy.tech/badge/torchvisionhttps://img.shields.io/badge/dynamic/json.svg?label=docs&url=https%3A%2F%2Fpypi.org%2Fpypi%2Ftorchvision%2Fjson&query=%24.info.version&colorB=brightgreen&prefix=v
The torchvision package consists of popular datasets, model architectures, and common image transformations for computer vision.
Installation
TorchVision requires PyTorch 1.4 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
# Add -DWITH_CUDA=on support for the CUDA if needed
cmake ..
make
make install
Once installed, the library can be accessed in cmake (after properly configuring CMAKE_PREFIX_PATH) via the TorchVision::TorchVision target:
find_package(TorchVision REQUIRED)
target_link_libraries(my-target PUBLIC TorchVision::TorchVision)
The TorchVision package will also automatically look for the Torch and pybind11 packages and add them as dependencies to my-target,
so make sure that they are also available to cmake via the CMAKE_PREFIX_PATH.
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.
Disclaimer on Datasets
This is a utility library that downloads and prepares public datasets. We do not host or distribute these datasets, vouch for their quality or fairness, or claim that you have license to use the dataset. It is your responsibility to determine whether you have permission to use the dataset under the dataset's license.
If you're a dataset owner and wish to update any part of it (description, citation, etc.), or do not want your dataset to be included in this library, please get in touch through a GitHub issue. Thanks for your contribution to the ML community!
About
Datasets, Transforms and Models specific to Computer Vision
Resources
Readme
BSD-3-Clause license
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages