Assemble new features to enhance Caffe.
The Caffe revision: 99bd99795dcdf0b1d3086a8d67ab1782a8a08383(commit SHA)
Common: you can use directly
Special: need special net structure
Experienced: need to adjust parameters or try some times
layer {
name: "center_loss"
type: "CenterLoss"
bottom: "fc5"
bottom: "label"
top: "center_loss"
param {
lr_mult: 1
decay_mult: 2
}
center_loss_param {
num_output: 10572
center_filler {
type: "xavier"
}
}
loss_weight: 0.008
}
layer {
name: "upsample5"
type: "Upsample"
bottom: "pool5"
top: "pool5_D"
bottom: "pool5_mask"
upsample_param {
scale: 2
upsample_w: 30
upsample_h: 23
}
}
layer {
name: "branch1_1_conv1"
type: "ConvolutionDepthwise"
bottom: "pool1"
top: "branch1_1_conv1"
convolution_param {
num_output: 24
kernel_size: 3
stride: 2
pad: 1
bias_term: false
weight_filler {
type: "msra"
}
}
}
layer {
name: "shuffle1"
type: "ShuffleChannel"
bottom: "concat1"
top: "shuffle1"
shuffle_channel_param {
group: 2
}
}
layer {
name: "loss"
type: "WeightedSoftmaxWithLoss"
bottom: "fc_end"
bottom: "label"
top: "loss"
softmax_param {
pos_cid: 1
pos_mult: 2.0
}
}
- Axpy (SENet)
layer {
name: "conv2_1"
type: "Axpy"
bottom: "conv2_1_1x1_up"
bottom: "conv2_1_1x1_increase"
bottom: "conv2_1_1x1_proj"
top: "conv2_1"
}
- MarginInnerProduct (Angular-Triplet-Loss based A-Softmax-Loss)
layer {
name: "fc6"
type: "MarginInnerProduct"
bottom: "fc5"
bottom: "label"
top: "fc6"
top: "lambda"
param {
lr_mult: 1
decay_mult: 1
}
margin_inner_product_param {
num_output: 10572
type: SINGLE
weight_filler {
type: "xavier"
}
base: 0
gamma: 0.12
power: 1
lambda_min: 3
iteration: 0
triplet: true
semihard: true
}
}
layer {
name: "fc6"
type: "MarginInnerProduct"
bottom: "fc5"
bottom: "label"
top: "fc6"
top: "lambda"
param {
name: "fc6_w"
lr_mult: 1
decay_mult: 1
}
margin_inner_product_param {
num_output: 10572
type: QUADRUPLE
weight_filler {
type: "xavier"
}
base: 1000
gamma: 0.12
power: 1
lambda_min: 5
iteration: 2000
}
}
layer {
name: "inter_class_dist_output"
type: "InterClass"
bottom: "fc5"
bottom: "label"
top: "maximize_inter_class_dist"
param {
name: "fc6_w"
lr_mult: 1
decay_mult: 1
}
inter_class_param {
num_output: 10572
type: AMONG
iteration: 16000
alpha_start_iter: 16001
alpha_start_value: 10 #among
}
}
layer {
name: "softmax_loss"
type: "SoftmaxWithLoss"
bottom: "fc6"
bottom: "label"
top: "softmax_loss"
}
New layer examples are here.
- ImageData
train.txt
image/00001.jpg 0 5 1
image/00002.jpg 0 9 -1
image/00003.jpg 1 9 2
image/00004.jpg 1 -1 0
image/00005.jpg 1 0 2train.prototxt
layer {
name: "data"
type: "ImageData"
top: "data"
top: "label"
include {
phase: TRAIN
}
transform_param {
scale: 0.00390625
mirror: true
}
image_data_param {
source: "/your/path/train.txt"
root_folder: "/your/image/data/path"
new_height: 224
new_width: 224
batch_size: 64
is_color: true
shuffle: true
label_dim: 3
}
}
layer {
name: "slice"
type: "Slice"
bottom: "label"
top: "label_1"
top: "label_2"
top: "label_3"
slice_param {
axis: 1
slice_point:1
slice_point:2
}
}
layer {
name: "fc7_1"
type: "InnerProduct"
bottom: "conv7_1"
top: "fc7_1"
param {
lr_mult: 1
}
param {
lr_mult: 2
}
inner_product_param {
num_output: 5
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
}
}
}
layer {
name: "fc7_2"
type: "InnerProduct"
bottom: "conv7_2"
top: "fc7_2"
param {
lr_mult: 1
}
param {
lr_mult: 2
}
inner_product_param {
num_output: 10
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
}
}
}
layer {
name: "fc7_3"
type: "InnerProduct"
bottom: "conv7_3"
top: "fc7_3"
param {
lr_mult: 1
}
param {
lr_mult: 2
}
inner_product_param {
num_output: 3
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
}
}
}
layer {
name: "loss1"
type: "SoftmaxWithLoss"
bottom: "fc7_1"
bottom: "label_1"
loss_param {
ignore_label: -1
}
}
layer {
name: "loss2"
type: "SoftmaxWithLoss"
bottom: "fc7_2"
bottom: "label_2"
loss_param {
ignore_label: -1
}
}
layer {
name: "loss3"
type: "SoftmaxWithLoss"
bottom: "fc7_3"
bottom: "label_3"
loss_param {
ignore_label: -1
}
}
The implementation for global average pooling on GPU supported by cuDNN and BVLC/caffe is less efficient. In this regard, the author re-implement the operation which achieves significant acceleration.
layer {
name: "conv2_1_global_pool"
type: "Pooling"
bottom: "conv2_1_1x1_increase"
top: "conv2_1_global_pool"
pooling_param {
pool: AVE
engine: CAFFE
global_pooling: true
}
}
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
- DIY Deep Learning for Vision with Caffe
- Tutorial Documentation
- BAIR reference models and the community model zoo
- Installation instructions
and step-by-step examples.
- Intel Caffe (Optimized for CPU and support for multi-node), in particular Xeon processors (HSW, BDW, SKX, Xeon Phi).
- OpenCL Caffe e.g. for AMD or Intel devices.
- Windows Caffe
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!
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}
}