[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"project-1687":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":16,"stars7d":17,"stars30d":18,"stars90d":16,"forks30d":16,"starsTrendScore":19,"compositeScore":20,"rankGlobal":10,"rankLanguage":10,"license":21,"archived":22,"fork":22,"defaultBranch":23,"hasWiki":24,"hasPages":24,"topics":25,"createdAt":10,"pushedAt":10,"updatedAt":29,"readmeContent":30,"aiSummary":31,"trendingCount":16,"starSnapshotCount":16,"syncStatus":17,"lastSyncTime":32,"discoverSource":33},1687,"caffe","BVLC\u002Fcaffe","BVLC","Caffe: a fast open framework for deep learning.","http:\u002F\u002Fcaffe.berkeleyvision.org\u002F",null,"C++",34578,18470,2055,903,0,2,12,1,72.2,"Other",false,"master",true,[26,27,28],"deep-learning","machine-learning","vision","2026-06-12 04:00:10","# Caffe\n\n[![Build Status](https:\u002F\u002Ftravis-ci.org\u002FBVLC\u002Fcaffe.svg?branch=master)](https:\u002F\u002Ftravis-ci.org\u002FBVLC\u002Fcaffe)\n[![License](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Flicense-BSD-blue.svg)](LICENSE)\n\nCaffe is a deep learning framework made with expression, speed, and modularity in mind.\nIt is developed by Berkeley AI Research ([BAIR](http:\u002F\u002Fbair.berkeley.edu))\u002FThe Berkeley Vision and Learning Center (BVLC) and community contributors.\n\nCheck out the [project site](http:\u002F\u002Fcaffe.berkeleyvision.org) for all the details like\n\n- [DIY Deep Learning for Vision with Caffe](https:\u002F\u002Fdocs.google.com\u002Fpresentation\u002Fd\u002F1UeKXVgRvvxg9OUdh_UiC5G71UMscNPlvArsWER41PsU\u002Fedit#slide=id.p)\n- [Tutorial Documentation](http:\u002F\u002Fcaffe.berkeleyvision.org\u002Ftutorial\u002F)\n- [BAIR reference models](http:\u002F\u002Fcaffe.berkeleyvision.org\u002Fmodel_zoo.html) and the [community model zoo](https:\u002F\u002Fgithub.com\u002FBVLC\u002Fcaffe\u002Fwiki\u002FModel-Zoo)\n- [Installation instructions](http:\u002F\u002Fcaffe.berkeleyvision.org\u002Finstallation.html)\n\nand step-by-step examples.\n\n## Custom distributions\n\n - [Intel Caffe](https:\u002F\u002Fgithub.com\u002FBVLC\u002Fcaffe\u002Ftree\u002Fintel) (Optimized for CPU and support for multi-node), in particular Intel® Xeon processors.\n- [OpenCL Caffe](https:\u002F\u002Fgithub.com\u002FBVLC\u002Fcaffe\u002Ftree\u002Fopencl) e.g. for AMD or Intel devices.\n- [Windows Caffe](https:\u002F\u002Fgithub.com\u002FBVLC\u002Fcaffe\u002Ftree\u002Fwindows)\n\n## Community\n\n[![Join the chat at https:\u002F\u002Fgitter.im\u002FBVLC\u002Fcaffe](https:\u002F\u002Fbadges.gitter.im\u002FJoin%20Chat.svg)](https:\u002F\u002Fgitter.im\u002FBVLC\u002Fcaffe?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge)\n\nPlease join the [caffe-users group](https:\u002F\u002Fgroups.google.com\u002Fforum\u002F#!forum\u002Fcaffe-users) or [gitter chat](https:\u002F\u002Fgitter.im\u002FBVLC\u002Fcaffe) to ask questions and talk about methods and models.\nFramework development discussions and thorough bug reports are collected on [Issues](https:\u002F\u002Fgithub.com\u002FBVLC\u002Fcaffe\u002Fissues).\n\nHappy brewing!\n\n## License and Citation\n\nCaffe is released under the [BSD 2-Clause license](https:\u002F\u002Fgithub.com\u002FBVLC\u002Fcaffe\u002Fblob\u002Fmaster\u002FLICENSE).\nThe BAIR\u002FBVLC reference models are released for unrestricted use.\n\nPlease cite Caffe in your publications if it helps your research:\n\n    @article{jia2014caffe,\n      Author = {Jia, Yangqing and Shelhamer, Evan and Donahue, Jeff and Karayev, Sergey and Long, Jonathan and Girshick, Ross and Guadarrama, Sergio and Darrell, Trevor},\n      Journal = {arXiv preprint arXiv:1408.5093},\n      Title = {Caffe: Convolutional Architecture for Fast Feature Embedding},\n      Year = {2014}\n    }\n","Caffe是一个专为深度学习设计的快速开源框架。它以表达性、速度和模块化为核心特点，支持多种硬件加速，并且提供了丰富的预训练模型库。Caffe特别适用于计算机视觉任务，如图像分类、目标检测等，但也可应用于其他类型的机器学习问题。其高效的数据处理能力和灵活的网络结构定义使得Caffe成为研究者和开发者进行实验与部署的理想选择。此外，Caffe拥有活跃的社区支持及详细的文档教程，便于新用户快速上手。","2026-06-11 02:45:26","top_all"]