[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"project-2631":3},{"id":4,"name":5,"fullName":6,"owner":7,"repo":5,"description":8,"homepage":9,"htmlUrl":10,"language":11,"languages":10,"totalLinesOfCode":10,"stars":12,"forks":13,"watchers":14,"openIssues":15,"contributorsCount":16,"subscribersCount":16,"size":16,"stars1d":17,"stars7d":18,"stars30d":19,"stars90d":16,"forks30d":16,"starsTrendScore":20,"compositeScore":21,"rankGlobal":10,"rankLanguage":10,"license":22,"archived":23,"fork":23,"defaultBranch":24,"hasWiki":25,"hasPages":25,"topics":26,"createdAt":10,"pushedAt":10,"updatedAt":29,"readmeContent":30,"aiSummary":31,"trendingCount":16,"starSnapshotCount":16,"syncStatus":32,"lastSyncTime":33,"discoverSource":34},2631,"vision","pytorch\u002Fvision","pytorch","Datasets, Transforms and Models specific to Computer Vision","https:\u002F\u002Fpytorch.org\u002Fvision",null,"Python",17735,7225,439,882,0,1,22,69,17,45,"BSD 3-Clause \"New\" or \"Revised\" License",false,"main",true,[27,28],"computer-vision","machine-learning","2026-06-12 02:00:42","# torchvision\n\n[![total torchvision downloads](https:\u002F\u002Fpepy.tech\u002Fbadge\u002Ftorchvision)](https:\u002F\u002Fpepy.tech\u002Fproject\u002Ftorchvision)\n[![documentation](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fdynamic\u002Fjson.svg?label=docs&url=https%3A%2F%2Fpypi.org%2Fpypi%2Ftorchvision%2Fjson&query=%24.info.version&colorB=brightgreen&prefix=v)](https:\u002F\u002Fpytorch.org\u002Fvision\u002Fstable\u002Findex.html)\n\nThe torchvision package consists of popular datasets, model architectures, and common image transformations for computer\nvision.\n\n## Installation\n\nPlease refer to the [official\ninstructions](https:\u002F\u002Fpytorch.org\u002Fget-started\u002Flocally\u002F) to install the stable\nversions of `torch` and `torchvision` on your system.\n\nTo build source, refer to our [contributing\npage](https:\u002F\u002Fgithub.com\u002Fpytorch\u002Fvision\u002Fblob\u002Fmain\u002FCONTRIBUTING.md#development-installation).\n\nThe following is the corresponding `torchvision` versions and supported Python\nversions.\n\n| `torch`            | `torchvision`      | Python              |\n| ------------------ | ------------------ | ------------------- |\n| `main` \u002F `nightly` | `main` \u002F `nightly` | `>=3.10`, `\u003C=3.14`  |\n| `2.10`             | `0.25`             | `>=3.10`, `\u003C=3.14`  |\n| `2.9`              | `0.24`             | `>=3.10`, `\u003C=3.14`  |\n| `2.8`              | `0.23`             | `>=3.9`, `\u003C=3.13`   |\n| `2.7`              | `0.22`             | `>=3.9`, `\u003C=3.13`   |\n| `2.6`              | `0.21`             | `>=3.9`, `\u003C=3.12`   |\n\n\u003Cdetails>\n    \u003Csummary>older versions\u003C\u002Fsummary>\n\n| `torch` | `torchvision`     | Python                    |\n|---------|-------------------|---------------------------|\n| `2.5`              | `0.20`             | `>=3.9`, `\u003C=3.12`   |\n| `2.4`              | `0.19`             | `>=3.8`, `\u003C=3.12`   |\n| `2.3`              | `0.18`             | `>=3.8`, `\u003C=3.12`   |\n| `2.2`              | `0.17`             | `>=3.8`, `\u003C=3.11`   |\n| `2.1`              | `0.16`             | `>=3.8`, `\u003C=3.11`   |\n| `2.0`              | `0.15`             | `>=3.8`, `\u003C=3.11`   |\n| `1.13`  | `0.14`            | `>=3.7.2`, `\u003C=3.10`       |\n| `1.12`  | `0.13`            | `>=3.7`, `\u003C=3.10`         |\n| `1.11`  | `0.12`            | `>=3.7`, `\u003C=3.10`         |\n| `1.10`  | `0.11`            | `>=3.6`, `\u003C=3.9`          |\n| `1.9`   | `0.10`            | `>=3.6`, `\u003C=3.9`          |\n| `1.8`   | `0.9`             | `>=3.6`, `\u003C=3.9`          |\n| `1.7`   | `0.8`             | `>=3.6`, `\u003C=3.9`          |\n| `1.6`   | `0.7`             | `>=3.6`, `\u003C=3.8`          |\n| `1.5`   | `0.6`             | `>=3.5`, `\u003C=3.8`          |\n| `1.4`   | `0.5`             | `==2.7`, `>=3.5`, `\u003C=3.8` |\n| `1.3`   | `0.4.2` \u002F `0.4.3` | `==2.7`, `>=3.5`, `\u003C=3.7` |\n| `1.2`   | `0.4.1`           | `==2.7`, `>=3.5`, `\u003C=3.7` |\n| `1.1`   | `0.3`             | `==2.7`, `>=3.5`, `\u003C=3.7` |\n| `\u003C=1.0` | `0.2`             | `==2.7`, `>=3.5`, `\u003C=3.7` |\n\n\u003C\u002Fdetails>\n\n## Image Backends\n\nTorchvision currently supports the following image backends:\n\n- torch tensors\n- PIL images:\n    - [Pillow](https:\u002F\u002Fpython-pillow.org\u002F)\n    - [Pillow-SIMD](https:\u002F\u002Fgithub.com\u002Fuploadcare\u002Fpillow-simd) - a **much faster** drop-in replacement for Pillow with SIMD.\n\nRead more in in our [docs](https:\u002F\u002Fpytorch.org\u002Fvision\u002Fstable\u002Ftransforms.html).\n\n## Documentation\n\nYou can find the API documentation on the pytorch website: \u003Chttps:\u002F\u002Fpytorch.org\u002Fvision\u002Fstable\u002Findex.html>\n\n## Contributing\n\nSee the [CONTRIBUTING](CONTRIBUTING.md) file for how to help out.\n\n## Disclaimer on Datasets\n\nThis is a utility library that downloads and prepares public datasets. We do not host or distribute these datasets,\nvouch for their quality or fairness, or claim that you have license to use the dataset. It is your responsibility to\ndetermine whether you have permission to use the dataset under the dataset's license.\n\nIf you're a dataset owner and wish to update any part of it (description, citation, etc.), or do not want your dataset\nto be included in this library, please get in touch through a GitHub issue. Thanks for your contribution to the ML\ncommunity!\n\n## Pre-trained Model License\n\nThe pre-trained models provided in this library may have their own licenses or terms and conditions derived from the\ndataset used for training. It is your responsibility to determine whether you have permission to use the models for your\nuse case.\n\nMore specifically, SWAG models are released under the CC-BY-NC 4.0 license. See\n[SWAG LICENSE](https:\u002F\u002Fgithub.com\u002Ffacebookresearch\u002FSWAG\u002Fblob\u002Fmain\u002FLICENSE) for additional details.\n\n## Citing TorchVision\n\nIf you find TorchVision useful in your work, please consider citing the following BibTeX entry:\n\n```bibtex\n@software{torchvision2016,\n    title        = {TorchVision: PyTorch's Computer Vision library},\n    author       = {TorchVision maintainers and contributors},\n    year         = 2016,\n    journal      = {GitHub repository},\n    publisher    = {GitHub},\n    howpublished = {\\url{https:\u002F\u002Fgithub.com\u002Fpytorch\u002Fvision}}\n}\n```\n","torchvision 是一个专注于计算机视觉任务的 PyTorch 库，提供了常用的数据集、模型架构以及图像变换工具。其核心功能包括对多种流行数据集的支持（如 CIFAR-10, ImageNet 等），预定义的经典及最新的深度学习模型（如 ResNet, VGG, Faster R-CNN 等），还有丰富的图像处理操作，这些都极大地简化了从数据准备到模型训练再到结果分析的过程。此外，torchvision 支持使用 Torch 张量和 PIL 图像作为后端，确保了灵活性与效率。此库非常适合用于学术研究、教育目的或是工业界中需要快速原型设计和部署计算机视觉应用的场景。",2,"2026-06-11 02:50:34","top_language"]