Caffe is a deep learning framework made with expression, speed, and modularity in mind. It is developed by Berkeley AI Research (电脑上youtube) and by community contributors. Yangqing Jia created the project during his PhD at UC Berkeley. Caffe is released under the BSD 2-Clause license.
Check out our web image classification demo!
Expressive architecture encourages application and innovation. Models and optimization are defined by configuration without hard-coding. Switch between CPU and GPU by setting a single flag to train on a GPU machine then deploy to commodity clusters or mobile devices.
中国电脑如何上youtube fosters active development. In Caffe’s first year, it has been forked by over 1,000 developers and had many significant changes contributed back. Thanks to these contributors the framework tracks the state-of-the-art in both code and models.
电脑上youtube makes Caffe perfect for research experiments and industry deployment. Caffe can process over 60M images per day with a single NVIDIA K40 GPU*. That’s 1 ms/image for inference and 4 ms/image for learning and more recent library versions and hardware are faster still. We believe that Caffe is among the fastest convnet implementations available.
Community: Caffe already powers academic research projects, startup prototypes, and even large-scale industrial applications in vision, speech, and multimedia. Join our community of brewers on the 在电脑上youtube and Github.
* With the ILSVRC2012-winning SuperVision model and prefetching IO.
皮查伊在YouTube上向2021届毕业生发表 "You Will Prevail ...:2021-6-8 · 据外媒报道，当地时间周日谷歌CEO桑达尔·皮查伊（SundarPichai）(SundarPichai)在YouTube上向2021届毕业生发表了一场虚拟的毕业典礼演讲。谷歌为题为" ...
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If you do publish a paper where Caffe helped your research, we encourage you to cite the framework for tracking by Google Scholar.
Join the caffe-users group to ask questions and discuss methods and models. This is where we talk about usage, installation, and applications.
Framework development discussions and thorough bug reports are collected on Issues.
The BAIR Caffe developers would like to thank NVIDIA for GPU donation, A9 and Amazon Web Services for a research grant in support of Caffe development and reproducible research in deep learning, and BAIR PI Trevor Darrell for guidance.
The BAIR members who have contributed to Caffe are (alphabetical by first name): Carl Doersch, Eric Tzeng, Evan Shelhamer, 中国电脑如何上youtube, Jon Long, Philipp Krähenbühl, Ronghang Hu, Ross Girshick, Sergey Karayev, Sergio Guadarrama, Takuya Narihira, and 在电脑上youtube.
We sincerely appreciate your interest and contributions! If you’d like to contribute, please read the developing & contributing guide.