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Fine-tuning CaffeNet for Style Recognition on “Flickr Style” Data Fine-tuning takes an already learned model, adapts the architecture, and resumes training from the already learned model weights. Let’s fine-tune the BAIR-distributed CaffeNet model on a different dataset, Flickr Style, to predict image style instead of object category. Explanation The Flickr-sourced images of the Style dataset are

Siamese Network Training with Caffe This example shows how you can use weight sharing and a contrastive loss function to learn a model using a siamese network in Caffe. We will assume that you have caffe successfully compiled. If not, please refer to the Installation page. This example builds on the MNIST tutorial so it would be a good idea to read that before continuing. The guide specifies all p

# glog wget https://storage.googleapis.com/google-code-archive-downloads/v2/code.google.com/google-glog/glog-0.3.3.tar.gz tar zxvf glog-0.3.3.tar.gz cd glog-0.3.3 ./configure make && make install # gflags wget https://github.com/schuhschuh/gflags/archive/master.zip unzip master.zip cd gflags-master mkdir build && cd build export CXXFLAGS="-fPIC" && cmake .. && make VERBOSE=1 make && make install #

Caffe Model Zoo Lots of researchers and engineers have made Caffe models for different tasks with all kinds of architectures and data: check out the model zoo! These models are learned and applied for problems ranging from simple regression, to large-scale visual classification, to Siamese networks for image similarity, to speech and robotics applications. To help share these models, we introduce

OS X Installation We highly recommend using the Homebrew package manager. Ideally you could start from a clean /usr/local to avoid conflicts. In the following, we assume that you’re using Anaconda Python and Homebrew. CUDA: Install via the NVIDIA package that includes both CUDA and the bundled driver. CUDA 7 is strongly suggested. Older CUDA require libstdc++ while clang++ is the default compiler

for CUDA version. Note, the cuda version may break if your NVIDIA driver and CUDA toolkit are not installed by APT. Package status of CPU-only version Package status of CUDA version Installing Caffe from source We may install the dependencies by merely one line sudo apt build-dep caffe-cpu # dependencies for CPU-only version sudo apt build-dep caffe-cuda # dependencies for CUDA version CUDA: Insta

Installation Prior to installing, have a glance through this guide and take note of the details for your platform. We install and run Caffe on Ubuntu 16.04–12.04, OS X 10.11–10.8, and through Docker and AWS. The official Makefile and Makefile.config build are complemented by a community CMake build. Step-by-step Instructions: Docker setup out-of-the-box brewing Ubuntu installation the standard pla

Caffe Tutorial Caffe is a deep learning framework and this tutorial explains its philosophy, architecture, and usage. This is a practical guide and framework introduction, so the full frontier, context, and history of deep learning cannot be covered here. While explanations will be given where possible, a background in machine learning and neural networks is helpful. Philosophy In one sip, Caffe i

Caffe Caffe is a deep learning framework made with expression, speed, and modularity in mind. It is developed by Berkeley AI Research (BAIR) 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! Why Caffe? Expressive architecture encourages application and innovat

Caffe Caffe is a deep learning framework made with expression, speed, and modularity in mind. It is developed by Berkeley AI Research (BAIR) 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! Why Caffe? Expressive architecture encourages application and innovat