Caffe: An Open Source Deep Learning Framework

Caffe is an open-source, deep learning framework made with expression, speed, and modularity in mind. It is developed by the Berkeley Vision and Learning Center (BVLC) and by community contributors. Caffe is designed for expressiveness, speed, and modularity, making it easy to specify models, train them, and deploy them.

Features

• Expressive Language: Caffe uses a concise and expressive language for defining models.
• Speed: Caffe is designed for speed, making it suitable for real-time applications.
• Modularity: Caffe is modular, allowing users to easily extend and customize it.

Getting Started

To get started with Caffe, you can visit the official Caffe website.

Example

Here is a simple example of a Caffe model:

layer {
  name: "conv1"
  type: "Convolution"
  bottom: "data"
  top: "conv1"
  blobs_lr: 1
  blobs_lr: 1
  weight_filler {
    type: "gaussian"
    std: 0.005
  }
}

Resources

• Caffe Documentation
• Caffe Tutorial
• Caffe Model Zoo

Convolution Layer