vision README

The project's README from github.com/pytorch/vision. As one markdown file: README.md.

torchvision

total torchvision downloads documentation

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
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:

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:

@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}}
}

vision (pytorch/vision)

Searching Database…

18K ·GitHub
这是干什么的?Torchvision 是 PyTorch 的计算机视觉工具包:提供常用的数据集、现成的模型结构,以及视觉任务中常见的图像变换。
它是独立的框架,还是 PyTorch 的一部分?它是一个配套库,不是框架:和 PyTorch 一起按版本配套安装(torchvision 0.24 对应 torch 2.9),并构建在 torch 张量生态之上。
我需要特定版本的 Python 或 torch 吗?需要——版本是绑定好的:每个 torchvision 版本对应一个 torch 版本和一个 Python 版本区间,例如 0.24 搭配 torch 2.9,main/nightly 构建要求 Python 3.10+。
torchvision 自己会托管我用的那些数据集吗?不会——这个库只负责下载和准备公开数据集。确认你在对应许可协议下有权使用某个数据集,完全是你自己的责任。
它的许可证宽松吗,有没有坑?代码采用 BSD-3-Clause,但预训练模型可能带有来自其训练数据的额外条款——例如 SWAG 权重是 CC-BY-NC 4.0,只能用于非商业用途。
典型的使用流程是什么样的?加载一个公开数据集,把图像送进变换(transform)流水线做预处理和数据增强,然后训练或微调自带的某个预训练模型结构。
我可以喂给它哪些图像格式?它支持 torch 张量和 PIL 图像。为了更快的解码速度,你可以把 Pillow 换成 Pillow-SIMD,这是一个用 SIMD 加速的即插即用替代品。
写论文时怎么引用它?README 里附有用于学术引用 TorchVision 的 BibTeX 条目,完整的 API 文档托管在 PyTorch 官网上。
这个项目还在积极维护吗?是的——它跟随 PyTorch 发布,并公布了配套版本对照表(torch 2.9 对应 torchvision 0.24),提供 nightly 构建,仓库大约有 18k 颗星,且没有被归档。
Creator
pytorch
Size
1.2 GB
Stars
18K
Age
9y
Commits · 90 Days