vision Documentation

Datasets, Transforms and Models specific to Computer Vision

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File: README.md

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 >=3.10, <=3.14
2.13 0.28 >=3.10, <=3.14
2.12 0.27 >=3.10, <=3.14
2.11 0.26 >=3.10, <=3.14
2.10 0.25 >=3.10, <=3.14
text
older versions
torch torchvision Python
2.9 0.24 >=3.10, <=3.14
2.8 0.23 >=3.9, <=3.13
2.7 0.22 >=3.9, <=3.13
2.6 0.21 >=3.9, <=3.12
2.5 0.20 >=3.9, <=3.12
2.4 0.19 >=3.8, <=3.12
2.3 0.18 >=3.8, <=3.12
2.2 0.17 >=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
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:

Read more in in our docs.

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:

bibtex
@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}}
}

File: docs/source/_templates/class.rst

.. role:: hidden
:class: hidden-section
.. currentmodule:: {{ module }}

{{ name | underline}}

.. autoclass:: {{ name }}
:members:

File: docs/source/_templates/class_dataset.rst

.. role:: hidden
:class: hidden-section
.. currentmodule:: {{ module }}

{{ name | underline}}

.. autoclass:: {{ name }}
:members:
getitem,
{% if "category_name" in methods %} category_name {% endif %}
:special-members:

File: docs/source/_templates/function.rst

.. role:: hidden
:class: hidden-section
.. currentmodule:: {{ module }}

{{ name | underline}}

.. autofunction:: {{ name }}

File: docs/source/models/convnext.rst

ConvNeXt

.. currentmodule:: torchvision.models

The ConvNeXt model is based on the A ConvNet for the 2020s <https://arxiv.org/abs/2201.03545>_ paper.

Model builders

The following model builders can be used to instantiate a ConvNeXt model, with or
without pre-trained weights. All the model builders internally rely on the
torchvision.models.convnext.ConvNeXt base class. Please refer to the source code <https://github.com/pytorch/vision/blob/main/torchvision/models/convnext.py>_ for
more details about this class.

.. autosummary::
:toctree: generated/
:template: function.rst

convnext_tiny
convnext_small
convnext_base
convnext_large

File: docs/source/models/deeplabv3.rst

DeepLabV3

.. currentmodule:: torchvision.models.segmentation

The DeepLabV3 model is based on the Rethinking Atrous Convolution for Semantic Image Segmentation <https://arxiv.org/abs/1706.05587>__ paper.

.. betastatus:: segmentation module

Model builders

The following model builders can be used to instantiate a DeepLabV3 model with
different backbones, with or without pre-trained weights. All the model builders
internally rely on the torchvision.models.segmentation.deeplabv3.DeepLabV3 base class. Please
refer to the source code <https://github.com/pytorch/vision/blob/main/torchvision/models/segmentation/deeplabv3.py>_
for more details about this class.

.. autosummary::
:toctree: generated/
:template: function.rst

text
deeplabv3_mobilenet_v3_large
deeplabv3_resnet50
deeplabv3_resnet101

File: docs/source/models/densenet.rst

DenseNet

.. currentmodule:: torchvision.models

The DenseNet model is based on the Densely Connected Convolutional Networks <https://arxiv.org/abs/1608.06993>_ paper.

Model builders

The following model builders can be used to instantiate a DenseNet model, with or
without pre-trained weights. All the model builders internally rely on the
torchvision.models.densenet.DenseNet base class. Please refer to the source code <https://github.com/pytorch/vision/blob/main/torchvision/models/densenet.py>_ for
more details about this class.

.. autosummary::
:toctree: generated/
:template: function.rst

text
densenet121
densenet161
densenet169
densenet201

File: docs/source/models/efficientnet.rst

EfficientNet

.. currentmodule:: torchvision.models

The EfficientNet model is based on the EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks <https://arxiv.org/abs/1905.11946>__
paper.

Model builders

The following model builders can be used to instantiate an EfficientNet model, with or
without pre-trained weights. All the model builders internally rely on the
torchvision.models.efficientnet.EfficientNet base class. Please refer to the source code <https://github.com/pytorch/vision/blob/main/torchvision/models/efficientnet.py>_ for
more details about this class.

.. autosummary::
:toctree: generated/
:template: function.rst

text
efficientnet_b0
efficientnet_b1
efficientnet_b2
efficientnet_b3
efficientnet_b4
efficientnet_b5
efficientnet_b6
efficientnet_b7

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