torchvision
The torchvision package consists of popular datasets, model architectures, and common image transformations for computer vision.
Installation
Please refer to the official
instructions to install the stable
versions of torch and torchvision on your system.
To build source, refer to our contributing page.
The following is the corresponding torchvision versions and supported Python
versions.
torch |
torchvision |
Python |
|---|---|---|
main / nightly |
main / nightly |
>=3.8, <=3.11 |
2.1 |
0.16 |
>=3.8, <=3.11 |
2.0 |
0.15 |
>=3.8, <=3.11 |
1.13 |
0.14 |
>=3.7.2, <=3.10 |
older versions
torch |
torchvision |
Python |
|---|---|---|
1.12 |
0.13 |
>=3.7, <=3.10 |
1.11 |
0.12 |
>=3.7, <=3.10 |
1.10 |
0.11 |
>=3.6, <=3.9 |
1.9 |
0.10 |
>=3.6, <=3.9 |
1.8 |
0.9 |
>=3.6, <=3.9 |
1.7 |
0.8 |
>=3.6, <=3.9 |
1.6 |
0.7 |
>=3.6, <=3.8 |
1.5 |
0.6 |
>=3.5, <=3.8 |
1.4 |
0.5 |
==2.7, >=3.5, <=3.8 |
1.3 |
0.4.2 / 0.4.3 |
==2.7, >=3.5, <=3.7 |
1.2 |
0.4.1 |
==2.7, >=3.5, <=3.7 |
1.1 |
0.3 |
==2.7, >=3.5, <=3.7 |
<=1.0 |
0.2 |
==2.7, >=3.5, <=3.7 |
Image Backends
Torchvision currently supports the following image backends:
- torch tensors
- PIL images:
- Pillow
- Pillow-SIMD - a much faster drop-in replacement for Pillow with SIMD.
Read more in in our docs.
[UNSTABLE] Video Backend
Torchvision currently supports the following video backends:
- pyav (default) - Pythonic binding for ffmpeg libraries.
- video_reader - This needs ffmpeg to be installed and torchvision to be built from source. There shouldn't be any conflicting version of ffmpeg installed. Currently, this is only supported on Linux.
conda install -c conda-forge 'ffmpeg<4.3'
python setup.py install
Using the models on C++
TorchVision provides an example project for how to use the models on C++ using JIT Script.
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 package and add it as a dependency to
my-target, so make sure that it is also available to cmake via the CMAKE_PREFIX_PATH.
For an example setup, take a look at examples/cpp/hello_world.
Python linking is disabled by default when compiling TorchVision with CMake, this allows you to run models without any
Python dependency. In some special cases where TorchVision's operators are used from Python code, you may need to link
to Python. This can be done by passing -DUSE_PYTHON=on to CMake.
TorchVision Operators
In order to get the torchvision operators registered with torch (eg. for the JIT), all you need to do is to ensure that
you #include <torchvision/vision.h> in your project.
Documentation
You can find the API documentation on the pytorch website: https://pytorch.org/vision/stable/index.html
Contributing
See the CONTRIBUTING file for how to help out.
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!
Pre-trained Model License
The pre-trained models provided in this library may have their own licenses or terms and conditions derived from the dataset used for training. It is your responsibility to determine whether you have permission to use the models for your use case.
More specifically, SWAG models are released under the CC-BY-NC 4.0 license. See SWAG LICENSE for additional details.
Citing TorchVision
If you find TorchVision useful in your work, please consider citing the following BibTeX entry:
@software{torchvision2016,
title = {TorchVision: PyTorch's Computer Vision library},
author = {TorchVision maintainers and contributors},
year = 2016,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/pytorch/vision}}
}
5 maintainers
Metadata
5 maintainers
Release files for torchvision 0.17.1
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Built distributions (wheels)
Total release size: 114.3 MB
Release files / torchvision-0.17.1-cp312-cp312-win_amd64.whl
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Release files / torchvision-0.17.1-cp312-cp312-manylinux2014_aarch64.whl
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Release files / torchvision-0.17.1-cp312-cp312-macosx_11_0_arm64.whl
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Release files / torchvision-0.17.1-cp311-cp311-win_amd64.whl
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Release files / torchvision-0.17.1-cp311-cp311-manylinux2014_aarch64.whl
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Release files / torchvision-0.17.1-cp311-cp311-manylinux1_x86_64.whl
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Release files / torchvision-0.17.1-cp310-cp310-win_amd64.whl
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Release files / torchvision-0.17.1-cp39-cp39-win_amd64.whl
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| Download URL | torchvision-0.17.1-cp38-cp38-manylinux2014_aarch64.whl |
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| Download URL | torchvision-0.17.1-cp38-cp38-manylinux1_x86_64.whl |
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| Download URL | torchvision-0.17.1-cp38-cp38-macosx_11_0_arm64.whl |
|---|---|
| Size | 1.6 MB |
| Tags | CPython 3.8 macOS 11.0+ ARM64 |
|
SHA-256 checksum How to use checksums |
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BLAKE2b-256 checksum How to use checksums |
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| Upload date | |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/4.0.2 CPython/3.11.5
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Release files / torchvision-0.17.1-cp38-cp38-macosx_10_13_x86_64.whl
| Download URL | torchvision-0.17.1-cp38-cp38-macosx_10_13_x86_64.whl |
|---|---|
| Size | 1.7 MB |
| Tags | CPython 3.8 macOS 10.13+ x86-64 |
|
SHA-256 checksum How to use checksums |
2621097065fa1c827885e2b52102e839a3541b933b7a90e0fa3c42c3de1bc3cf
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BLAKE2b-256 checksum How to use checksums |
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|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/4.0.2 CPython/3.11.5
|
