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#Caffe finetune googlenet full#
#Caffe finetune googlenet code#

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

#Caffe finetune googlenet full#

The open-source community plays an important and growing role in Caffe’s development.Ĭheck out the Github project pulse for recent activity and the contributors for the full list. The BAIR members who have contributed to Caffe are (alphabetical by first name):Ĭarl Doersch, Eric Tzeng, Evan Shelhamer, Jeff Donahue, Jon Long, Philipp Krähenbühl, Ronghang Hu, Ross Girshick, Sergey Karayev, Sergio Guadarrama, Takuya Narihira, and Yangqing Jia. 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. This is where we talk about usage, installation, and applications.įramework development discussions and thorough bug reports are collected on Issues. Join the caffe-users group to ask questions and discuss methods and models. If you do publish a paper where Caffe helped your research, we encourage you to cite the framework for tracking by Google Scholar.

#Caffe finetune googlenet code#

Guidelines for development and contributing to Caffe.ĭeveloper documentation automagically generated from code comments.Ĭomparison of inference and learning for different networks and GPUs. Tutorial presentation of the framework and a full-day crash course.Ī 4-page report for the ACM Multimedia Open Source competition (arXiv:1408.5093v1).īAIR suggests a standard distribution format for Caffe models, and provides trained models.

DIY Deep Learning for Vision with Caffe and Caffe in a Day.

* With the ILSVRC2012-winning SuperVision model and prefetching IO. Join our community of brewers on the caffe-users group and Github. We believe that Caffe is among the fastest convnet implementations available.Ĭommunity: Caffe already powers academic research projects, startup prototypes, and even large-scale industrial applications in vision, speech, and multimedia. That’s 1 ms/image for inference and 4 ms/image for learning and more recent library versions and hardware are faster still. Speed makes Caffe perfect for research experiments and industry deployment.Ĭaffe can process over 60M images per day with a single NVIDIA K40 GPU*. Thanks to these contributors the framework tracks the state-of-the-art in both code and models.

In Caffe’s first year, it has been forked by over 1,000 developers and had many significant changes contributed back. Switch between CPU and GPU by setting a single flag to train on a GPU machine then deploy to commodity clusters or mobile devices.Įxtensible code fosters active development. Models and optimization are defined by configuration without hard-coding. Yangqing Jia created the project during his PhD at UC Berkeley.Ĭaffe is released under the BSD 2-Clause license.Ĭheck out our web image classification demo! Why Caffe?Įxpressive architecture encourages application and innovation. It is developed by Berkeley AI Research ( BAIR) and by community contributors. Caffe is a deep learning framework made with expression, speed, and modularity in mind.