Caffe provides multimedia scientists and practitioners with a clean and modifiable framework for state-of-the-art deep learning algorithms and a collection of reference models. The framework is a BSD-licensed C++ library with Python and MATLAB bindings for training and deploying general-purpose convolutional neural networks and other deep models. Caffe processes over 40 million images a day on a single K40 or Titan GPU (approximately 2.5 ms per image). By separating model representation from actual implementation, Caffe enables seamless switching among platforms for ease of development and deployment from prototyping machines to cloud environments.

A key problem in multimedia data analysis is discovery of effective representations for sensory inputs. While hand-designed features dominated for years, deep models have outperformed them in many domains. Convolutional Neural Networks (CNNs), discriminatively trained via back-propagation, have recently surpassed all known methods for large-scale visual recognition. However, replication of published results can involve months of work by a researcher or engineer. Caffe addresses this challenge by providing clear access to deep architectures written in clean, efficient C++ with CUDA used for GPU computation.

Modularity: the software is designed to be as modular as possible, allowing easy extension to new data formats, network layers, and loss functions. Separation of representation and implementation: model definitions are written as config files using the Protocol Buffer language, supporting network architectures in the form of arbitrary directed acyclic graphs. Test coverage: every single module has a test, and no new code is accepted without corresponding tests. Python and MATLAB bindings enable rapid prototyping. Pre-trained reference models including the landmark AlexNet ImageNet model and the R-CNN detection model are provided off-the-shelf.

Caffe stores and communicates data in 4-dimensional arrays called blobs. Blobs provide a unified memory interface holding batches of images, parameters, or parameter updates. Models are saved as Google Protocol Buffers which offer minimal-size binary strings when serialized. Large-scale data is stored in LevelDB databases providing a throughput of 150 MB/s on commodity machines. This design ensures that data movement between CPU and GPU is transparent to the user.

A Caffe layer is the essence of a neural network layer: it takes one or more blobs as input and yields one or more blobs as output. Layers have a forward pass that takes the inputs and produces the outputs, and a backward pass that takes the gradient with respect to the output and computes gradients with respect to parameters and inputs. Caffe provides a complete set of layer types including convolution, pooling, inner products, nonlinearities like rectified linear and logistic, and loss functions. Layers come with corresponding CPU and GPU routines that produce identical results, with tests to prove it.

Caffe trains models by fast and standard stochastic gradient descent (SGD). Data are processed in mini-batches that pass through the network sequentially. The framework implements learning rate decay schedules, momentum, and snapshots for stopping and resuming training. Fine-tuning — the adaptation of an existing model to new architectures or data — is a standard method in Caffe and has proven to be a highly effective transfer learning technique. The network is run on CPU or GPU by setting a single switch, enabling seamless transitions between development and deployment environments.

Object Classification: Caffe has an online demo showing state-of-the-art classification into 1,000 ImageNet categories, and has successfully trained a model with all 10,000 categories of the full ImageNet. Semantic Features: features extracted from pre-trained networks can identify image styles such as "Vintage" and "Romantic". Object Detection: Caffe has enabled by far the best performance on the PASCAL VOC 2007-2012 and ImageNet 2013 Detection challenge through the R-CNN pipeline combining Selective Search with CNN features. These diverse applications demonstrate Caffe's role as a general-purpose deep learning platform rather than a single-task tool.

Caffe provides the community with a fast, well-tested, and modular framework for deep learning research and deployment. Its open-source nature under the BSD license, combined with pre-trained reference models and comprehensive documentation, lowers the barrier to entry for both researchers and practitioners. The framework's speed, modularity, and openness have already made it one of the most widely adopted deep learning frameworks, and its continued development through community contributions ensures that it evolves alongside advances in the field.