What's News

Add image data layer to support multi-label output.
Add more augmentation method for data transform.
An example usage is (3x64x64 output):

layer {
  name: "data"
  type: "ImageData"
  top: "data"
  top: "label"
  top: "score"
  include {
    phase: TRAIN
  }
  transform_param {
    # scale: 0.00390625
    mirror: true
    # crop_size: 0          # crop_size and crop_pad cannot be specified at the same time
    # mean_file: "mean_file"
    mean_value: 127         # repeat this option for other channels
    # force_color: false    # Force the decoded image to have 3 color channels.
    # force_gray: false     # Force the decoded image to have 1 color channels.

    ############################Notice################################
    # If you would like to use below Augmentation
    # Please clone caffe source from https://github.com/BobLiu20/caffe

    max_rotate_angle: 2.0   # Specify the angle for doing random rotate
    min_contrast: 0.8       # contrast, brightness in random
    max_contrast: 1.2       # contrast, brightness in random
    max_brightness_shift: 5 # contrast, brightness in random
    max_smooth: 6           # random kernel size of blur
    max_color_shift: 10     # random shift R, G, B 
    crop_pad: 15            # random cutting edge in top, bottom, left and right
    crop_pad_new_size: 64   # for crop_pad. resize image to it after all pre-process
  }
  image_data_param {
    source: "train.txt"
    batch_size: 256
    shuffle: true
  }
}

layer {
  name: "data"
  type: "ImageData"
  top: "data"
  top: "label"
  top: "score"
  include {
    phase: TEST
  }
  transform_param {
    # scale: 0.00390625
    mirror: false
    # crop_size: 0          # crop_size and crop_pad cannot be specified at the same time
    # mean_file: "mean_file"
    mean_value: 127         # repeat this option for other channels

    ############################Notice################################
    # If you would like to use below Augmentation
    # Please clone caffe source from https://github.com/BobLiu20/caffe

    crop_pad: 15            # random cutting edge in top, bottom, left and right
    crop_pad_new_size: 64   # for crop_pad. resize image to it after all pre-process
  }
  image_data_param {
    source: "train.txt"
    batch_size: 256
  }
}

Caffe

Caffe is a deep learning framework made with expression, speed, and modularity in mind. It is developed by Berkeley AI Research (BAIR)/The Berkeley Vision and Learning Center (BVLC) and community contributors.

Check out the project site for all the details like

and step-by-step examples.

Custom distributions

Intel Caffe (Optimized for CPU and support for multi-node), in particular Xeon processors (HSW, BDW, Xeon Phi).
OpenCL Caffe e.g. for AMD or Intel devices.
Windows Caffe

Community

Please join the caffe-users group or gitter chat to ask questions and talk about methods and models. Framework development discussions and thorough bug reports are collected on Issues.

Happy brewing!

License and Citation

Caffe is released under the BSD 2-Clause license. The BAIR/BVLC reference models are released for unrestricted use.

Please cite Caffe in your publications if it helps your research:

@article{jia2014caffe,
  Author = {Jia, Yangqing and Shelhamer, Evan and Donahue, Jeff and Karayev, Sergey and Long, Jonathan and Girshick, Ross and Guadarrama, Sergio and Darrell, Trevor},
  Journal = {arXiv preprint arXiv:1408.5093},
  Title = {Caffe: Convolutional Architecture for Fast Feature Embedding},
  Year = {2014}
}