FINETUNE GOOGLENET CAFFE CODE
Follow along with do-it-yourself code notebooks. Join our tour from the 1989 LeNet for digit recognition to today’s top ILSVRC14 vision models. To this end we present the Caffe framework, public reference models, and working examples for deep learning. While deep learning and deep features have recently achieved strong results in many tasks, a common framework and shared models are needed to advance further research and applications and reduce the barrier to entry. Both the ideas and implementation of state-of-the-art deep learning models will be presented. This tutorial is designed to equip researchers and developers with the tools and know-how needed to incorporate deep learning into their work. Join our community of brewers on the caffe-users group and Github. We believe that Caffe is the fastest convnet implementation available.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. Caffe can processover 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.Speed makes Caffe perfect for research experiments and industry deployment. 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.Caffe’s extensible code fosters active development. Models and optimization are defined by configuration without hard-coding. It is developed by the Berkeley Vision and Learning Center (BVLC) and by community contributors.Ĭaffe’s expressive architecture encourages application and innovation. Caffe (Convolutional Architecture for Fast Feature Embedding) is a deep learning framework made with expression, speed, and modularity in mind.
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