- Caffe | Deep Learning Framework
Caffe Deep learning framework by BAIR Created by Yangqing Jia Lead Developer Evan Shelhamer View On GitHub 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
- Caffe | Installation
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
- Caffe | Caffe Tutorial
View On GitHub 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
- Caffe | Interfaces
View On GitHub Interfaces Caffe has command line, Python, and MATLAB interfaces for day-to-day usage, interfacing with research code, and rapid prototyping While Caffe is a C++ library at heart and it exposes a modular interface for development, not every occasion calls for custom compilation The cmdcaffe, pycaffe, and matcaffe interfaces are here for you Command Line The command line
- Caffe | Model Zoo
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
- Caffe | Solver Model Optimization
View On GitHub Solver The solver orchestrates model optimization by coordinating the network’s forward inference and backward gradients to form parameter updates that attempt to improve the loss The responsibilities of learning are divided between the Solver for overseeing the optimization and generating parameter updates and the Net for yielding loss and gradients The Caffe solvers are
- Caffe | LeNet MNIST Tutorial
If it complains that wget or gunzip are not installed, you need to install them respectively After running the script there should be two datasets, mnist_train_lmdb, and mnist_test_lmdb LeNet: the MNIST Classification Model Before we actually run the training program, let’s explain what will happen We will use the LeNet network, which is known to work well on digit classification tasks
- Caffe | Blobs, Layers, and Nets - dominoqq
Blobs, Layers, and Nets: anatomy of a Caffe model Deep networks are compositional models that are naturally represented as a collection of inter-connected layers that work on chunks of data Caffe defines a net layer-by-layer in its own model schema The network defines the entire model bottom-to-top from input data to loss As data and derivatives flow through the network in the forward and
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