## File: README.md # torchvision [](https://pepy.tech/project/torchvision) [](https://pytorch.org/vision/stable/index.html) The torchvision package consists of popular datasets, model architectures, and common image transformations for computer vision. ## Installation 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 | | ------------------ | ------------------ | ------------------- | | `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` | 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: - [Pillow](https://python-pillow.org/) - [Pillow-SIMD](https://github.com/uploadcare/pillow-simd) - a **much faster** drop-in replacement for Pillow with SIMD. Read more in in our [docs](https://pytorch.org/vision/stable/transforms.html). ## Documentation You can find the API documentation on the pytorch website: ## Contributing See the [CONTRIBUTING](CONTRIBUTING.md) 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](https://github.com/facebookresearch/SWAG/blob/main/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 `_ 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 `_ 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 `__ 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 `_ for more details about this class. .. autosummary:: :toctree: generated/ :template: function.rst 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 `_ 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 `_ for more details about this class. .. autosummary:: :toctree: generated/ :template: function.rst 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 `__ 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 `_ for more details about this class. .. autosummary:: :toctree: generated/ :template: function.rst efficientnet_b0 efficientnet_b1 efficientnet_b2 efficientnet_b3 efficientnet_b4 efficientnet_b5 efficientnet_b6 efficientnet_b7