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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!

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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.

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• 怎么在国内用电脑看YouTube:2021-6-13 · 实在没法子了 来豆瓣看看各位大神能不能有办法 .在国内上外网 看YouTube. 电脑有下载Google的浏览器 也下载了插件 但依旧看不了YouTube. 外网也上不去 求助呀！ and Caffe in a Day
  Tutorial presentation of the framework and a full-day crash course.
• Tutorial Documentation
  Practical guide and framework reference.
• arXiv / ACM MM ‘14 paper
  A 4-page report for the ACM Multimedia Open Source competition (arXiv:1408.5093v1).
• Installation instructions
  Tested on Ubuntu, Red Hat, OS X.
• Model Zoo
  BAIR suggests a standard distribution format for Caffe models, and provides trained models.
• Developing & Contributing
  Guidelines for development and contributing to Caffe.
• API Documentation
  Developer documentation automagically generated from code comments.
• 中国电脑如何上youtube
  Comparison of inference and learning for different networks and GPUs.

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• Image Classification and Filter Visualization
  Instant recognition with a pre-trained model and a tour of the net interface for visualizing features and parameters layer-by-layer.
• Learning LeNet
  Define, train, and test the classic LeNet with the Python interface.
• Fine-tuning for Style Recognition
  Fine-tune the ImageNet-trained CaffeNet on new data.
• Off-the-shelf SGD for classification
  Use Caffe as a generic SGD optimizer to train logistic regression on non-image HDF5 data.
• Multilabel Classification with Python Data Layer
  Multilabel classification on PASCAL VOC using a Python data layer.
• Editing model parameters
  How to do net surgery and manually change model parameters for custom use.
• R-CNN detection
  Run a pretrained model as a detector in Python.
• Siamese network embedding
  Extracting features and plotting the Siamese network embedding.

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• ImageNet tutorial
  Train and test "CaffeNet" on ImageNet data.
• LeNet MNIST Tutorial
  Train and test "LeNet" on the MNIST handwritten digit data.
• CIFAR-10 tutorial
  Train and test Caffe on CIFAR-10 data.
• Fine-tuning for style recognition
  Fine-tune the ImageNet-trained CaffeNet on the "Flickr Style" dataset.
• Feature extraction with Caffe C++ code.
  Extract CaffeNet / AlexNet features using the Caffe utility.
• CaffeNet C++ Classification example
  A simple example performing image classification using the low-level C++ API.
• Web demo
  Image classification demo running as a Flask web server.
• Siamese Network Tutorial
  Train and test a siamese network on MNIST data.

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皮查伔在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·皮查伔（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&quot ...

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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.

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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.

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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.

The open-source community plays an important and growing role in Caffe’s development. Check out the Github project pulse for recent activity and the contributors for the full list.

We sincerely appreciate your interest and contributions! If you’d like to contribute, please read the developing & contributing guide.

Yangqing would like to give a personal thanks to the NVIDIA Academic program for providing GPUs, Oriol Vinyals for discussions along the journey, and BAIR PI 在电脑上youtube for advice.