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GoogLeNet_cars
Raw
readme.md
name caffemodel caffemodel_url license caffe_commit gist_id
GoogLeNet_cars
googlenet_finetune_web_car_iter_10000.caffemodel
non-commercial
737ea5e936821b5c69f9c3952d72693ae5843370
b90eb88e31cd745525ae

##Description

This is the GoogLeNet model pre-trained on ImageNet classification task and fine-tuned on 431 car models in CompCars dataset. It is described in the technical report. The correspondence between the output nodes of the network and the 431 car models can be viewed at link. Please cite the following work if the model is useful for you.

A Large-Scale Car Dataset for Fine-Grained Categorization and Verification
L. Yang, P. Luo, C. C. Loy, X. Tang, arXiv:1506.08959, 2015

The bundled model is the iteration 10,000 snapshot. This model obtains a top-1 accuracy 91.2% and a top-5 accuracy 98.1% on the testing set, using only the center crop.

How to use it

First, you need to download our CompCars dataset. To download, first you need to fill out an agreement according to http://mmlab.ie.cuhk.edu.hk/datasets/comp_cars/index.html.

You will also find our train/test split files for vehicle classification on http://mmlab.ie.cuhk.edu.hk/datasets/comp_cars/index.html. Reformulate it to any format that Caffe can read (image and label list, lmdb etc).

Then you can use the prototxt files and the model here to train, test, and extract features with the data. It uses the standard mean file of ImageNet data which can be downloaded from Caffe (try running /data/ilsvrc12/get_ilsvrc_aux.sh in Caffe).

This model uses an old version of Caffe: https://github.com/BVLC/caffe/releases/tag/rc. Please take care.

License

The data used to train this model comes from the ImageNet project and the CompCars dataset, which distribute their databases to researchers who agree to a following term of access: "Researcher shall use the Database only for non-commercial research and educational purposes." Accordingly, this model is distributed under a non-commercial license.

Raw
deploy.prototxt
name: "GoogleNet"
input: "data"
input_dim: 10
input_dim: 3
input_dim: 224
input_dim: 224
# hierarchy 1
# conv -> relu -> pool -> lrn
# ===========================
layers {
bottom: "data"
top: "conv1"
name: "conv1"
type: CONVOLUTION
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
num_output: 64
pad: 3
kernel_size: 7
stride: 2
weight_filler {
type: "xavier"
std: 0.1
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layers {
bottom: "conv1"
top: "conv1"
name: "relu1"
type: RELU
}
layers {
bottom: "conv1"
top: "pool1"
name: "pool1"
type: POOLING
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layers {
bottom: "pool1"
top: "norm1"
name: "norm1"
type: LRN
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
}
}
# hierarchy 2
# conv -> relu -> conv -> relu -> lrn -> pool
# ===========================================
layers {
bottom: "norm1"
top: "conv2_1x1"
name: "conv2_1x1"
type: CONVOLUTION
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
num_output: 64
kernel_size: 1
weight_filler {
type: "xavier"
std: 0.1
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layers {
bottom: "conv2_1x1"
top: "conv2_1x1"
name: "relu_conv2_1x1"
type: RELU
}
layers {
bottom: "conv2_1x1"
top: "conv2_3x3"
name: "conv2_3x3"
type: CONVOLUTION
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
num_output: 192
pad: 1
kernel_size: 3
weight_filler {
type: "xavier"
std: 0.03
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layers {
bottom: "conv2_3x3"
top: "conv2_3x3"
name: "relu2_3x3"
type: RELU
}
layers {
bottom: "conv2_3x3"
top: "norm2"
name: "norm2"
type: LRN
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
}
}
layers {
bottom: "norm2"
top: "pool2"
name: "pool2"
type: POOLING
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
# ================== hierarchy 3(a) ==================
# ====================================================
# conv 1 x 1
# ----------
layers {
bottom: "pool2"
top: "inception_3a_1x1"
name: "inception_3a_1x1"
type: CONVOLUTION
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
num_output: 64
kernel_size: 1
weight_filler {
type: "xavier"
std: 0.03
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layers {
bottom: "inception_3a_1x1"
top: "inception_3a_1x1"
name: "relu_inception_3a_1x1"
type: RELU
}
# conv 3 x 3
# ----------
layers {
bottom: "pool2"
top: "inception_3a_3x3_reduce"
name: "inception_3a_3x3_reduce"
type: CONVOLUTION
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
num_output: 96
kernel_size: 1
weight_filler {
type: "xavier"
std: 0.09
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layers {
bottom: "inception_3a_3x3_reduce"
top: "inception_3a_3x3_reduce"
name: "reulu_inception_3a_3x3_reduce"
type: RELU
}
layers {
bottom: "inception_3a_3x3_reduce"
top: "inception_3a_3x3"
name: "inception_3a_3x3"
type: CONVOLUTION
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
num_output: 128
pad: 1
kernel_size: 3
weight_filler {
type: "xavier"
std: 0.03
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layers {
bottom: "inception_3a_3x3"
top: "inception_3a_3x3"
name: "relu_inception_3a_3x3"
type: RELU
}
# conv 5 x 5
# ----------
layers {
bottom: "pool2"
top: "inception_3a_5x5_reduce"
name: "inception_3a_5x5_reduce"
type: CONVOLUTION
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
num_output: 16
kernel_size: 1
weight_filler {
type: "xavier"
std: 0.2
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layers {
bottom: "inception_3a_5x5_reduce"
top: "inception_3a_5x5_reduce"
name: "relu_inception_3a_5x5_reduce"
type: RELU
}
layers {
bottom: "inception_3a_5x5_reduce"
top: "inception_3a_5x5"
name: "inception_3a_5x5"
type: CONVOLUTION
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
num_output: 32
pad: 2
kernel_size: 5
weight_filler {
type: "xavier"
std: 0.03
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layers {
bottom: "inception_3a_5x5"
top: "inception_3a_5x5"
name: "relu_inception_3a_5x5"
type: RELU
}
# pool 3 x 3
# ----------
layers {
bottom: "pool2"
top: "inception_3a_pool"
name: "inception_3a_pool"
type: POOLING
pooling_param {
pool: MAX
kernel_size: 3
stride: 1
pad: 1
}
}
layers {
bottom: "inception_3a_pool"
top: "inception_3a_pool_proj"
name: "inception_3a_pool_proj"
type: CONVOLUTION
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
num_output: 32
kernel_size: 1
weight_filler {
type: "xavier"
std: 0.1
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layers {
bottom: "inception_3a_pool_proj"
top: "inception_3a_pool_proj"
name: "relu_inception_3a_pool_proj"
type: RELU
}
# concatenate all
# ---------------
layers {
bottom: "inception_3a_1x1"
bottom: "inception_3a_3x3"
bottom: "inception_3a_5x5"
bottom: "inception_3a_pool_proj"
top: "inception_3a_output"
name: "inception_3a_output"
type: CONCAT
}
# ================== hierarchy 3(b) ==================
# ====================================================
# conv 1 x 1
# ----------
layers {
bottom: "inception_3a_output"
top: "inception_3b_1x1"
name: "inception_3b_1x1"
type: CONVOLUTION
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
num_output: 128
kernel_size: 1
weight_filler {
type: "xavier"
std: 0.03
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layers {
bottom: "inception_3b_1x1"
top: "inception_3b_1x1"
name: "relu_inception_3b_1x1"
type: RELU
}
# conv 3 x 3
# ----------
layers {
bottom: "inception_3a_output"
top: "inception_3b_3x3_reduce"
name: "inception_3b_3x3_reduce"
type: CONVOLUTION
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
num_output: 128
kernel_size: 1
weight_filler {
type: "xavier"
std: 0.09
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layers {
bottom: "inception_3b_3x3_reduce"
top: "inception_3b_3x3_reduce"
name: "relu_inception_3b_3x3_reduce"
type: RELU
}
layers {
bottom: "inception_3b_3x3_reduce"
top: "inception_3b_3x3"
name: "inception_3b_3x3"
type: CONVOLUTION
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
num_output: 192
pad: 1
kernel_size: 3
weight_filler {
type: "xavier"
std: 0.03
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layers {
bottom: "inception_3b_3x3"
top: "inception_3b_3x3"
name: "relu_inception_3b_3x3"
type: RELU
}
# conv 5 x 5
# ----------
layers {
bottom: "inception_3a_output"
top: "inception_3b_5x5_reduce"
name: "inception_3b_5x5_reduce"
type: CONVOLUTION
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
num_output: 32
kernel_size: 1
weight_filler {
type: "xavier"
std: 0.2
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layers {
bottom: "inception_3b_5x5_reduce"
top: "inception_3b_5x5_reduce"
name: "relu_inception_3b_5x5_reduce"
type: RELU
}
layers {
bottom: "inception_3b_5x5_reduce"
top: "inception_3b_5x5"
name: "inception_3b_5x5"
type: CONVOLUTION
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
num_output: 96
pad: 2
kernel_size: 5
weight_filler {
type: "xavier"
std: 0.03
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layers {
bottom: "inception_3b_5x5"
top: "inception_3b_5x5"
name: "relu_inception_3b_5x5"
type: RELU
}
# pool 3 x 3
# ----------
layers {
bottom: "inception_3a_output"
top: "inception_3b_pool"
name: "inception_3b_pool"
type: POOLING
pooling_param {
pool: MAX
kernel_size: 3
stride: 1
pad: 1
}
}
layers {
bottom: "inception_3b_pool"
top: "inception_3b_pool_proj"
name: "inception_3b_pool_proj"
type: CONVOLUTION
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
num_output: 64
kernel_size: 1
weight_filler {
type: "xavier"
std: 0.1
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layers {
bottom: "inception_3b_pool_proj"
top: "inception_3b_pool_proj"
name: "relu_inception_3b_pool_proj"
type: RELU
}
# concatenate all
# ---------------
layers {
bottom: "inception_3b_1x1"
bottom: "inception_3b_3x3"
bottom: "inception_3b_5x5"
bottom: "inception_3b_pool_proj"
top: "inception_3b_output"
name: "inception_3b_output"
type: CONCAT
}
layers {
bottom: "inception_3b_output"
top: "pool3"
name: "pool3"
type: POOLING
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
# ================== hierarchy 4(a) ==================
# ====================================================
# conv 1 x 1
# ----------
layers {
bottom: "pool3"
top: "inception_4a_1x1"
name: "inception_4a_1x1"
type: CONVOLUTION
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
num_output: 192
kernel_size: 1
weight_filler {
type: "xavier"
std: 0.03
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layers {
bottom: "inception_4a_1x1"
top: "inception_4a_1x1"
name: "relu_inception_4a_1x1"
type: RELU
}
# conv 3 x 3
# ----------
layers {
bottom: "pool3"
top: "inception_4a_3x3_reduce"
name: "inception_4a_3x3_reduce"
type: CONVOLUTION
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
num_output: 96
kernel_size: 1
weight_filler {
type: "xavier"
std: 0.09
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layers {
bottom: "inception_4a_3x3_reduce"
top: "inception_4a_3x3_reduce"
name: "relu_inception_4a_3x3_reduce"
type: RELU
}
layers {
bottom: "inception_4a_3x3_reduce"
top: "inception_4a_3x3"
name: "inception_4a_3x3"
type: CONVOLUTION
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
num_output: 208
pad: 1
kernel_size: 3
weight_filler {
type: "xavier"
std: 0.03
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layers {
bottom: "inception_4a_3x3"
top: "inception_4a_3x3"
name: "relu_inception_4a_3x3"
type: RELU
}
# conv 5 x 5
# ----------
layers {
bottom: "pool3"
top: "inception_4a_5x5_reduce"
name: "inception_4a_5x5_reduce"
type: CONVOLUTION
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
num_output: 16
kernel_size: 1
weight_filler {
type: "xavier"
std: 0.2
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layers {
bottom: "inception_4a_5x5_reduce"
top: "inception_4a_5x5_reduce"
name: "relu_inception_4a_5x5_reduce"
type: RELU
}
layers {
bottom: "inception_4a_5x5_reduce"
top: "inception_4a_5x5"
name: "inception_4a_5x5"
type: CONVOLUTION
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
num_output: 48
pad: 2
kernel_size: 5
weight_filler {
type: "xavier"
std: 0.03
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layers {
bottom: "inception_4a_5x5"
top: "inception_4a_5x5"
name: "relu_inception_4a_5x5"
type: RELU
}
# pool 3 x 3
# ----------
layers {
bottom: "pool3"
top: "inception_4a_pool"
name: "inception_4a_pool"
type: POOLING
pooling_param {
pool: MAX
kernel_size: 3
stride: 1
pad: 1
}
}
layers {
bottom: "inception_4a_pool"
top: "inception_4a_pool_proj"
name: "inception_4a_pool_proj"
type: CONVOLUTION
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
num_output: 64
kernel_size: 1
weight_filler {
type: "xavier"
std: 0.1
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layers {
bottom: "inception_4a_pool_proj"
top: "inception_4a_pool_proj"
name: "relu_inception_4a_pool_proj"
type: RELU
}
# concatenate all
# ---------------
layers {
bottom: "inception_4a_1x1"
bottom: "inception_4a_3x3"
bottom: "inception_4a_5x5"
bottom: "inception_4a_pool_proj"
top: "inception_4a_output"
name: "inception_4a_output"
type: CONCAT
}
# ================== hierarchy 4(b) ==================
# ====================================================
# conv 1 x 1
# ----------
layers {
bottom: "inception_4a_output"
top: "inception_4b_1x1"
name: "inception_4b_1x1"
type: CONVOLUTION
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
num_output: 160
kernel_size: 1
weight_filler {
type: "xavier"
std: 0.03
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layers {
bottom: "inception_4b_1x1"
top: "inception_4b_1x1"
name: "inception_4b_relu_1x1"
type: RELU
}
# conv 3 x 3
# ----------
layers {
bottom: "inception_4a_output"
top: "inception_4b_3x3_reduce"
name: "inception_4b_3x3_reduce"
type: CONVOLUTION
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
num_output: 112
kernel_size: 1
weight_filler {
type: "xavier"
std: 0.09
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layers {
bottom: "inception_4b_3x3_reduce"
top: "inception_4b_3x3_reduce"
name: "inception_4b_relu_3x3_reduce"
type: RELU
}
layers {
bottom: "inception_4b_3x3_reduce"
top: "inception_4b_3x3"
name: "inception_4b_3x3"
type: CONVOLUTION
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
num_output: 224
pad: 1
kernel_size: 3
weight_filler {
type: "xavier"
std: 0.03
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layers {
bottom: "inception_4b_3x3"
top: "inception_4b_3x3"
name: "inception_4b_relu_3x3"
type: RELU
}
# conv 5 x 5
# ----------
layers {
bottom: "inception_4a_output"
top: "inception_4b_5x5_reduce"
name: "inception_4b_5x5_reduce"
type: CONVOLUTION
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
num_output: 24
kernel_size: 1
weight_filler {
type: "xavier"
std: 0.2
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layers {
bottom: "inception_4b_5x5_reduce"
top: "inception_4b_5x5_reduce"
name: "inception_4b_relu_5x5_reduce"
type: RELU
}
layers {
bottom: "inception_4b_5x5_reduce"
top: "inception_4b_5x5"
name: "inception_4b_5x5"
type: CONVOLUTION
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
num_output: 64
pad: 2
kernel_size: 5
weight_filler {
type: "xavier"
std: 0.03
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layers {
bottom: "inception_4b_5x5"
top: "inception_4b_5x5"
name: "inception_4b_relu_5x5"
type: RELU
}
# pool 3 x 3
# ----------
layers {
bottom: "inception_4a_output"
top: "inception_4b_pool"
name: "inception_4b_pool"
type: POOLING
pooling_param {
pool: MAX
kernel_size: 3
stride: 1
pad: 1
}
}
layers {
bottom: "inception_4b_pool"
top: "inception_4b_pool_proj"
name: "inception_4b_pool_proj"
type: CONVOLUTION
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
num_output: 64
kernel_size: 1
weight_filler {
type: "xavier"
std: 0.1
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layers {
bottom: "inception_4b_pool_proj"
top: "inception_4b_pool_proj"
name: "inception_4b_relu_pool_proj"
type: RELU
}
# concatenate all
# ---------------
layers {
bottom: "inception_4b_1x1"
bottom: "inception_4b_3x3"
bottom: "inception_4b_5x5"
bottom: "inception_4b_pool_proj"
top: "inception_4b_output"
name: "inception_4b_output"
type: CONCAT
}
# ================== hierarchy 4(c) ==================
# ====================================================
# conv 1 x 1
# ----------
layers {
bottom: "inception_4b_output"
top: "inception_4c_1x1"
name: "inception_4c_1x1"
type: CONVOLUTION
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
num_output: 128
kernel_size: 1
weight_filler {
type: "xavier"
std: 0.03
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layers {
bottom: "inception_4c_1x1"
top: "inception_4c_1x1"
name: "inception_4c_relu_1x1"
type: RELU
}
# conv 3 x 3
# ----------
layers {
bottom: "inception_4b_output"
top: "inception_4c_3x3_reduce"
name: "inception_4c_3x3_reduce"
type: CONVOLUTION
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
num_output: 128
kernel_size: 1
weight_filler {
type: "xavier"
std: 0.09
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layers {
bottom: "inception_4c_3x3_reduce"
top: "inception_4c_3x3_reduce"
name: "inception_4c_relu_3x3_reduce"
type: RELU
}
layers {
bottom: "inception_4c_3x3_reduce"
top: "inception_4c_3x3"
name: "inception_4c_3x3"
type: CONVOLUTION
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
num_output: 256
pad: 1
kernel_size: 3
weight_filler {
type: "xavier"
std: 0.03
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layers {
bottom: "inception_4c_3x3"
top: "inception_4c_3x3"
name: "inception_4c_relu_3x3"
type: RELU
}
# conv 5 x 5
# ----------
layers {
bottom: "inception_4b_output"
top: "inception_4c_5x5_reduce"
name: "inception_4c_5x5_reduce"
type: CONVOLUTION
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
num_output: 24
kernel_size: 1
weight_filler {
type: "xavier"
std: 0.2
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layers {
bottom: "inception_4c_5x5_reduce"
top: "inception_4c_5x5_reduce"
name: "inception_4c_relu_5x5_reduce"
type: RELU
}
layers {
bottom: "inception_4c_5x5_reduce"
top: "inception_4c_5x5"
name: "inception_4c_5x5"
type: CONVOLUTION
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
num_output: 64
pad: 2
kernel_size: 5
weight_filler {
type: "xavier"
std: 0.03
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layers {
bottom: "inception_4c_5x5"
top: "inception_4c_5x5"
name: "inception_4c_relu_5x5"
type: RELU
}
# pool 3 x 3
# ----------
layers {
bottom: "inception_4b_output"
top: "inception_4c_pool"
name: "inception_4c_pool"
type: POOLING
pooling_param {
pool: MAX
kernel_size: 3
stride: 1
pad: 1
}
}
layers {
bottom: "inception_4c_pool"
top: "inception_4c_pool_proj"
name: "inception_4c_pool_proj"
type: CONVOLUTION
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
num_output: 64
kernel_size: 1
weight_filler {
type: "xavier"
std: 0.1
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layers {
bottom: "inception_4c_pool_proj"
top: "inception_4c_pool_proj"
name: "inception_4c_relu_pool_proj"
type: RELU
}
# concatenate them all
# --------------------
layers {
bottom: "inception_4c_1x1"
bottom: "inception_4c_3x3"
bottom: "inception_4c_5x5"
bottom: "inception_4c_pool_proj"
top: "inception_4c_output"
name: "inception_4c_output"
type: CONCAT
}
# ================== hierarchy 4(d) ==================
# ====================================================
# conv 1 x 1
# ----------
layers {
bottom: "inception_4c_output"
top: "inception_4d_1x1"
name: "inception_4d_1x1"
type: CONVOLUTION
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
num_output: 112
kernel_size: 1
weight_filler {
type: "xavier"
std: 0.03
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layers {
bottom: "inception_4d_1x1"
top: "inception_4d_1x1"
name: "inception_4d_relu_1x1"
type: RELU
}
# conv 3 x 3
# ----------
layers {
bottom: "inception_4c_output"
top: "inception_4d_3x3_reduce"
name: "inception_4d_3x3_reduce"
type: CONVOLUTION
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
num_output: 144
kernel_size: 1
weight_filler {
type: "xavier"
std: 0.09
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layers {
bottom: "inception_4d_3x3_reduce"
top: "inception_4d_3x3_reduce"
name: "inception_4d_relu_3x3_reduce"
type: RELU
}
layers {
bottom: "inception_4d_3x3_reduce"
top: "inception_4d_3x3"
name: "inception_4d_3x3"
type: CONVOLUTION
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
num_output: 288
pad: 1
kernel_size: 3
weight_filler {
type: "xavier"
std: 0.03
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layers {
bottom: "inception_4d_3x3"
top: "inception_4d_3x3"
name: "inception_4d_relu_3x3"
type: RELU
}
# conv 5 x 5
# ----------
layers {
bottom: "inception_4c_output"
top: "inception_4d_5x5_reduce"
name: "inception_4d_5x5_reduce"
type: CONVOLUTION
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
num_output: 32
kernel_size: 1
weight_filler {
type: "xavier"
std: 0.2
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layers {
bottom: "inception_4d_5x5_reduce"
top: "inception_4d_5x5_reduce"
name: "inception_4d_relu_5x5_reduce"
type: RELU
}
layers {
bottom: "inception_4d_5x5_reduce"
top: "inception_4d_5x5"
name: "inception_4d_5x5"
type: CONVOLUTION
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
num_output: 64
pad: 2
kernel_size: 5
weight_filler {
type: "xavier"
std: 0.03
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layers {
bottom: "inception_4d_5x5"
top: "inception_4d_5x5"
name: "inception_4d_relu_5x5"
type: RELU
}
# pool 3 x 3
# ----------
layers {
bottom: "inception_4c_output"
top: "inception_4d_pool"
name: "inception_4d_pool"
type: POOLING
pooling_param {
pool: MAX
kernel_size: 3
stride: 1
pad: 1
}
}
layers {
bottom: "inception_4d_pool"
top: "inception_4d_pool_proj"
name: "inception_4d_pool_proj"
type: CONVOLUTION
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
num_output: 64
kernel_size: 1
weight_filler {
type: "xavier"
std: 0.1
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layers {
bottom: "inception_4d_pool_proj"
top: "inception_4d_pool_proj"
name: "inception_4d_relu_pool_proj"
type: RELU
}
# concatenate them all
# --------------------
layers {
bottom: "inception_4d_1x1"
bottom: "inception_4d_3x3"
bottom: "inception_4d_5x5"
bottom: "inception_4d_pool_proj"
top: "inception_4d_output"
name: "inception_4d_output"
type: CONCAT
}
# ================== hierarchy 4(e) ==================
# ====================================================
# conv 1 x 1
# ----------
layers {
bottom: "inception_4d_output"
top: "inception_4e_1x1"
name: "inception_4e_1x1"
type: CONVOLUTION
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
num_output: 256
kernel_size: 1
weight_filler {
type: "xavier"
std: 0.03
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layers {
bottom: "inception_4e_1x1"
top: "inception_4e_1x1"
name: "inception_4e_relu_1x1"
type: RELU
}
# conv 3 x 3
# ----------
layers {
bottom: "inception_4d_output"
top: "inception_4e_3x3_reduce"
name: "inception_4e_3x3_reduce"
type: CONVOLUTION
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
num_output: 160
kernel_size: 1
weight_filler {
type: "xavier"
std: 0.09
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layers {
bottom: "inception_4e_3x3_reduce"
top: "inception_4e_3x3_reduce"
name: "inception_4e_relu_3x3_reduce"
type: RELU
}
layers {
bottom: "inception_4e_3x3_reduce"
top: "inception_4e_3x3"
name: "inception_4e_3x3"
type: CONVOLUTION
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
num_output: 320
pad: 1
kernel_size: 3
weight_filler {
type: "xavier"
std: 0.03
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layers {
bottom: "inception_4e_3x3"
top: "inception_4e_3x3"
name: "inception_4e_relu_3x3"
type: RELU
}
# conv 5 x 5
# ----------
layers {
bottom: "inception_4d_output"
top: "inception_4e_5x5_reduce"
name: "inception_4e_5x5_reduce"
type: CONVOLUTION
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
num_output: 32
kernel_size: 1
weight_filler {
type: "xavier"
std: 0.2
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layers {
bottom: "inception_4e_5x5_reduce"
top: "inception_4e_5x5_reduce"
name: "inception_4e_relu_5x5_reduce"
type: RELU
}
layers {
bottom: "inception_4e_5x5_reduce"
top: "inception_4e_5x5"
name: "inception_4e_5x5"
type: CONVOLUTION
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
num_output: 128
pad: 2
kernel_size: 5
weight_filler {
type: "xavier"
std: 0.03
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layers {
bottom: "inception_4e_5x5"
top: "inception_4e_5x5"
name: "inception_4e_relu_5x5"
type: RELU
}
# pool 3 x 3
# ----------
layers {
bottom: "inception_4d_output"
top: "inception_4e_pool"
name: "inception_4e_pool"
type: POOLING
pooling_param {
pool: MAX
kernel_size: 3
stride: 1
pad: 1
}
}
layers {
bottom: "inception_4e_pool"
top: "inception_4e_pool_proj"
name: "inception_4e_pool_proj"
type: CONVOLUTION
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
num_output: 128
kernel_size: 1
weight_filler {
type: "xavier"
std: 0.1
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layers {
bottom: "inception_4e_pool_proj"
top: "inception_4e_pool_proj"
name: "inception_4e_relu_pool_proj"
type: RELU
}
# concatenate them all
# --------------------
layers {
bottom: "inception_4e_1x1"
bottom: "inception_4e_3x3"
bottom: "inception_4e_5x5"
bottom: "inception_4e_pool_proj"
top: "inception_4e_output"
name: "inception_4e_output"
type: CONCAT
}
layers {
bottom: "inception_4e_output"
top: "pool4"
name: "pool4"
type: POOLING
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
# ================== hierarchy 5(a) ==================
# ====================================================
# conv 1 x 1
# ----------
layers {
bottom: "pool4"
top: "inception_5a_1x1"
name: "inception_5a_1x1"
type: CONVOLUTION
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
num_output: 256
kernel_size: 1
weight_filler {
type: "xavier"
std: 0.03
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layers {
bottom: "inception_5a_1x1"
top: "inception_5a_1x1"
name: "inception_5a_relu_1x1"
type: RELU
}
# conv 3 x 3
# ----------
layers {
bottom: "pool4"
top: "inception_5a_3x3_reduce"
name: "inception_5a_3x3_reduce"
type: CONVOLUTION
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
num_output: 160
kernel_size: 1
weight_filler {
type: "xavier"
std: 0.09
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layers {
bottom: "inception_5a_3x3_reduce"
top: "inception_5a_3x3_reduce"
name: "inception_5a_relu_3x3_reduce"
type: RELU
}
layers {
bottom: "inception_5a_3x3_reduce"
top: "inception_5a_3x3"
name: "inception_5a_3x3"
type: CONVOLUTION
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
num_output: 320
pad: 1
kernel_size: 3
weight_filler {
type: "xavier"
std: 0.03
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layers {
bottom: "inception_5a_3x3"
top: "inception_5a_3x3"
name: "inception_5a_relu_3x3"
type: RELU
}
# conv 5 x 5
# ----------
layers {
bottom: "pool4"
top: "inception_5a_5x5_reduce"
name: "inception_5a_5x5_reduce"
type: CONVOLUTION
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
num_output: 32
kernel_size: 1
weight_filler {
type: "xavier"
std: 0.2
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layers {
bottom: "inception_5a_5x5_reduce"
top: "inception_5a_5x5_reduce"
name: "inception_5a_relu_5x5_reduce"
type: RELU
}
layers {
bottom: "inception_5a_5x5_reduce"
top: "inception_5a_5x5"
name: "inception_5a_5x5"
type: CONVOLUTION
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
num_output: 128
pad: 2
kernel_size: 5
weight_filler {
type: "xavier"
std: 0.03
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layers {
bottom: "inception_5a_5x5"
top: "inception_5a_5x5"
name: "inception_5a_relu_5x5"
type: RELU
}
# pool 3 x 3
# ----------
layers {
bottom: "pool4"
top: "inception_5a_pool"
name: "inception_5a_pool"
type: POOLING
pooling_param {
pool: MAX
kernel_size: 3
stride: 1
pad: 1
}
}
layers {
bottom: "inception_5a_pool"
top: "inception_5a_pool_proj"
name: "inception_5a_pool_proj"
type: CONVOLUTION
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
num_output: 128
kernel_size: 1
weight_filler {
type: "xavier"
std: 0.1
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layers {
bottom: "inception_5a_pool_proj"
top: "inception_5a_pool_proj"
name: "inception_5a_relu_pool_proj"
type: RELU
}
# concatenate them all
# --------------------
layers {
bottom: "inception_5a_1x1"
bottom: "inception_5a_3x3"
bottom: "inception_5a_5x5"
bottom: "inception_5a_pool_proj"
top: "inception_5a_output"
name: "inception_5a_output"
type: CONCAT
}
# ================== hierarchy 5(b) ==================
# ====================================================
# conv 1 x 1
# ----------
layers {
bottom: "inception_5a_output"
top: "inception_5b_1x1"
name: "inception_5b_1x1"
type: CONVOLUTION
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
num_output: 384
kernel_size: 1
weight_filler {
type: "xavier"
std: 0.03
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layers {
bottom: "inception_5b_1x1"
top: "inception_5b_1x1"
name: "inception_5b_relu_1x1"
type: RELU
}
# conv 3 x 3
# ----------
layers {
bottom: "inception_5a_output"
top: "inception_5b_3x3_reduce"
name: "inception_5b_3x3_reduce"
type: CONVOLUTION
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
num_output: 192
kernel_size: 1
weight_filler {
type: "xavier"
std: 0.09
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layers {
bottom: "inception_5b_3x3_reduce"
top: "inception_5b_3x3_reduce"
name: "inception_5b_relu_3x3_reduce"
type: RELU
}
layers {
bottom: "inception_5b_3x3_reduce"
top: "inception_5b_3x3"
name: "inception_5b_3x3"
type: CONVOLUTION
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
num_output: 384
pad: 1
kernel_size: 3
weight_filler {
type: "xavier"
std: 0.03
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layers {
bottom: "inception_5b_3x3"
top: "inception_5b_3x3"
name: "inception_5b_relu_3x3"
type: RELU
}
# conv 5 x 5
# ----------
layers {
bottom: "inception_5a_output"
top: "inception_5b_5x5_reduce"
name: "inception_5b_5x5_reduce"
type: CONVOLUTION
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
num_output: 48
kernel_size: 1
weight_filler {
type: "xavier"
std: 0.2
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layers {
bottom: "inception_5b_5x5_reduce"
top: "inception_5b_5x5_reduce"
name: "inception_5b_relu_5x5_reduce"
type: RELU
}
layers {
bottom: "inception_5b_5x5_reduce"
top: "inception_5b_5x5"
name: "inception_5b_5x5"
type: CONVOLUTION
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
num_output: 128
pad: 2
kernel_size: 5
weight_filler {
type: "xavier"
std: 0.03
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layers {
bottom: "inception_5b_5x5"
top: "inception_5b_5x5"
name: "inception_5b_relu_5x5"
type: RELU
}
# pool 3 x 3
# ----------
layers {
bottom: "inception_5a_output"
top: "inception_5b_pool"
name: "inception_5b_pool"
type: POOLING
pooling_param {
pool: MAX
kernel_size: 3
stride: 1
pad: 1
}
}
layers {
bottom: "inception_5b_pool"
top: "inception_5b_pool_proj"
name: "inception_5b_pool_proj"
type: CONVOLUTION
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
num_output: 128
kernel_size: 1
weight_filler {
type: "xavier"
std: 0.1
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layers {
bottom: "inception_5b_pool_proj"
top: "inception_5b_pool_proj"
name: "inception_5b_relu_pool_proj"
type: RELU
}
# concatenate them all
# --------------------
layers {
bottom: "inception_5b_1x1"
bottom: "inception_5b_3x3"
bottom: "inception_5b_5x5"
bottom: "inception_5b_pool_proj"
top: "inception_5b_output"
name: "inception_5b_output"
type: CONCAT
}
# ===================== softmax 2 ====================
# ====================================================
# ave_pool -> droput -> fc -> loss, accuracy
layers {
bottom: "inception_5b_output"
top: "pool5"
name: "pool5"
type: POOLING
pooling_param {
pool: AVE
kernel_size: 7
stride: 1
}
}
layers {
bottom: "pool5"
top: "pool5"
name: "pool5_drop"
type: DROPOUT
dropout_param {
dropout_ratio: 0.4
}
}
layers {
bottom: "pool5"
top: "loss3_classifier_model"
name: "loss3_classifier_model"
type: INNER_PRODUCT
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
inner_product_param {
num_output: 431
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0
}
}
}
layers {
name: "prob"
type: SOFTMAX
bottom: "loss3_classifier_model"
top: "prob"
}
Raw
solver_googlenet.prototxt
net: "./train_val_googlenet.prototxt"
test_iter: 500
test_interval: 500
test_initialization: false
max_iter: 10000
base_lr: 0.001
lr_policy: "step"
gamma: 0.1
stepsize: 5000
momentum: 0.9
weight_decay: 0.0002
display: 20
snapshot: 5000
snapshot_prefix: "googlenet_finetune_web_car"
solver_mode: GPU
device_id: 0
random_seed: 34234562302122
Raw
train_val_googlenet.prototxt
name: "GoogleNet"
# train data
layers {
top: "data"
top: "label"
name: "data"
type: IMAGE_DATA
image_data_param {
source: "classification_train"
batch_size: 256
shuffle: true
new_width: 256
new_height: 256
}
transform_param {
mirror: true
crop_size: 224
mean_file: "imagenet_mean.binaryproto"
}
include {
phase: TRAIN
}
}
# test data
layers {
top: "data"
top: "label"
name: "data"
type: IMAGE_DATA
image_data_param {
source: "classification_test"
batch_size: 10
new_width: 256
new_height: 256
}
transform_param {
mirror: false
crop_size: 224
mean_file: "imagenet_mean.binaryproto"
}
include {
phase: TEST
}
}
# hierarchy 1
# conv -> relu -> pool -> lrn
# ===========================
layers {
bottom: "data"
top: "conv1"
name: "conv1"
type: CONVOLUTION
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
num_output: 64
pad: 3
kernel_size: 7
stride: 2
weight_filler {
type: "xavier"
std: 0.1
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layers {
bottom: "conv1"
top: "conv1"
name: "relu1"
type: RELU
}
layers {
bottom: "conv1"
top: "pool1"
name: "pool1"
type: POOLING
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layers {
bottom: "pool1"
top: "norm1"
name: "norm1"
type: LRN
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
}
}
# hierarchy 2
# conv -> relu -> conv -> relu -> lrn -> pool
# ===========================================
layers {
bottom: "norm1"
top: "conv2_1x1"
name: "conv2_1x1"
type: CONVOLUTION
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
num_output: 64
kernel_size: 1
weight_filler {
type: "xavier"
std: 0.1
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layers {
bottom: "conv2_1x1"
top: "conv2_1x1"
name: "relu_conv2_1x1"
type: RELU
}
layers {
bottom: "conv2_1x1"
top: "conv2_3x3"
name: "conv2_3x3"
type: CONVOLUTION
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
num_output: 192
pad: 1
kernel_size: 3
weight_filler {
type: "xavier"
std: 0.03
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layers {
bottom: "conv2_3x3"
top: "conv2_3x3"
name: "relu2_3x3"
type: RELU
}
layers {
bottom: "conv2_3x3"
top: "norm2"
name: "norm2"
type: LRN
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
}
}
layers {
bottom: "norm2"
top: "pool2"
name: "pool2"
type: POOLING
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
# ================== hierarchy 3(a) ==================
# ====================================================
# conv 1 x 1
# ----------
layers {
bottom: "pool2"
top: "inception_3a_1x1"
name: "inception_3a_1x1"
type: CONVOLUTION
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
num_output: 64
kernel_size: 1
weight_filler {
type: "xavier"
std: 0.03
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layers {
bottom: "inception_3a_1x1"
top: "inception_3a_1x1"
name: "relu_inception_3a_1x1"
type: RELU
}
# conv 3 x 3
# ----------
layers {
bottom: "pool2"
top: "inception_3a_3x3_reduce"
name: "inception_3a_3x3_reduce"
type: CONVOLUTION
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
num_output: 96
kernel_size: 1
weight_filler {
type: "xavier"
std: 0.09
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layers {
bottom: "inception_3a_3x3_reduce"
top: "inception_3a_3x3_reduce"
name: "reulu_inception_3a_3x3_reduce"
type: RELU
}
layers {
bottom: "inception_3a_3x3_reduce"
top: "inception_3a_3x3"
name: "inception_3a_3x3"
type: CONVOLUTION
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
num_output: 128
pad: 1
kernel_size: 3
weight_filler {
type: "xavier"
std: 0.03
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layers {
bottom: "inception_3a_3x3"
top: "inception_3a_3x3"
name: "relu_inception_3a_3x3"
type: RELU
}
# conv 5 x 5
# ----------
layers {
bottom: "pool2"
top: "inception_3a_5x5_reduce"
name: "inception_3a_5x5_reduce"
type: CONVOLUTION
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
num_output: 16
kernel_size: 1
weight_filler {
type: "xavier"
std: 0.2
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layers {
bottom: "inception_3a_5x5_reduce"
top: "inception_3a_5x5_reduce"
name: "relu_inception_3a_5x5_reduce"
type: RELU
}
layers {
bottom: "inception_3a_5x5_reduce"
top: "inception_3a_5x5"
name: "inception_3a_5x5"
type: CONVOLUTION
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
num_output: 32
pad: 2
kernel_size: 5
weight_filler {
type: "xavier"
std: 0.03
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layers {
bottom: "inception_3a_5x5"
top: "inception_3a_5x5"
name: "relu_inception_3a_5x5"
type: RELU
}
# pool 3 x 3
# ----------
layers {
bottom: "pool2"
top: "inception_3a_pool"
name: "inception_3a_pool"
type: POOLING
pooling_param {
pool: MAX
kernel_size: 3
stride: 1
pad: 1
}
}
layers {
bottom: "inception_3a_pool"
top: "inception_3a_pool_proj"
name: "inception_3a_pool_proj"
type: CONVOLUTION
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
num_output: 32
kernel_size: 1
weight_filler {
type: "xavier"
std: 0.1
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layers {
bottom: "inception_3a_pool_proj"
top: "inception_3a_pool_proj"
name: "relu_inception_3a_pool_proj"
type: RELU
}
# concatenate all
# ---------------
layers {
bottom: "inception_3a_1x1"
bottom: "inception_3a_3x3"
bottom: "inception_3a_5x5"
bottom: "inception_3a_pool_proj"
top: "inception_3a_output"
name: "inception_3a_output"
type: CONCAT
}
# ================== hierarchy 3(b) ==================
# ====================================================
# conv 1 x 1
# ----------
layers {
bottom: "inception_3a_output"
top: "inception_3b_1x1"
name: "inception_3b_1x1"
type: CONVOLUTION
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
num_output: 128
kernel_size: 1
weight_filler {
type: "xavier"
std: 0.03
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layers {
bottom: "inception_3b_1x1"
top: "inception_3b_1x1"
name: "relu_inception_3b_1x1"
type: RELU
}
# conv 3 x 3
# ----------
layers {
bottom: "inception_3a_output"
top: "inception_3b_3x3_reduce"
name: "inception_3b_3x3_reduce"
type: CONVOLUTION
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
num_output: 128
kernel_size: 1
weight_filler {
type: "xavier"
std: 0.09
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layers {
bottom: "inception_3b_3x3_reduce"
top: "inception_3b_3x3_reduce"
name: "relu_inception_3b_3x3_reduce"
type: RELU
}
layers {
bottom: "inception_3b_3x3_reduce"
top: "inception_3b_3x3"
name: "inception_3b_3x3"
type: CONVOLUTION
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
num_output: 192
pad: 1
kernel_size: 3
weight_filler {
type: "xavier"
std: 0.03
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layers {
bottom: "inception_3b_3x3"
top: "inception_3b_3x3"
name: "relu_inception_3b_3x3"
type: RELU
}
# conv 5 x 5
# ----------
layers {
bottom: "inception_3a_output"
top: "inception_3b_5x5_reduce"
name: "inception_3b_5x5_reduce"
type: CONVOLUTION
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
num_output: 32
kernel_size: 1
weight_filler {
type: "xavier"
std: 0.2
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layers {
bottom: "inception_3b_5x5_reduce"
top: "inception_3b_5x5_reduce"
name: "relu_inception_3b_5x5_reduce"
type: RELU
}
layers {
bottom: "inception_3b_5x5_reduce"
top: "inception_3b_5x5"
name: "inception_3b_5x5"
type: CONVOLUTION
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
num_output: 96
pad: 2
kernel_size: 5
weight_filler {
type: "xavier"
std: 0.03
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layers {
bottom: "inception_3b_5x5"
top: "inception_3b_5x5"
name: "relu_inception_3b_5x5"
type: RELU
}
# pool 3 x 3
# ----------
layers {
bottom: "inception_3a_output"
top: "inception_3b_pool"
name: "inception_3b_pool"
type: POOLING
pooling_param {
pool: MAX
kernel_size: 3
stride: 1
pad: 1
}
}
layers {
bottom: "inception_3b_pool"
top: "inception_3b_pool_proj"
name: "inception_3b_pool_proj"
type: CONVOLUTION
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
num_output: 64
kernel_size: 1
weight_filler {
type: "xavier"
std: 0.1
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layers {
bottom: "inception_3b_pool_proj"
top: "inception_3b_pool_proj"
name: "relu_inception_3b_pool_proj"
type: RELU
}
# concatenate all
# ---------------
layers {
bottom: "inception_3b_1x1"
bottom: "inception_3b_3x3"
bottom: "inception_3b_5x5"
bottom: "inception_3b_pool_proj"
top: "inception_3b_output"
name: "inception_3b_output"
type: CONCAT
}
layers {
bottom: "inception_3b_output"
top: "pool3"
name: "pool3"
type: POOLING
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
# ================== hierarchy 4(a) ==================
# ====================================================
# conv 1 x 1
# ----------
layers {
bottom: "pool3"
top: "inception_4a_1x1"
name: "inception_4a_1x1"
type: CONVOLUTION
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
num_output: 192
kernel_size: 1
weight_filler {
type: "xavier"
std: 0.03
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layers {
bottom: "inception_4a_1x1"
top: "inception_4a_1x1"
name: "relu_inception_4a_1x1"
type: RELU
}
# conv 3 x 3
# ----------
layers {
bottom: "pool3"
top: "inception_4a_3x3_reduce"
name: "inception_4a_3x3_reduce"
type: CONVOLUTION
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
num_output: 96
kernel_size: 1
weight_filler {
type: "xavier"
std: 0.09
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layers {
bottom: "inception_4a_3x3_reduce"
top: "inception_4a_3x3_reduce"
name: "relu_inception_4a_3x3_reduce"
type: RELU
}
layers {
bottom: "inception_4a_3x3_reduce"
top: "inception_4a_3x3"
name: "inception_4a_3x3"
type: CONVOLUTION
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
num_output: 208
pad: 1
kernel_size: 3
weight_filler {
type: "xavier"
std: 0.03
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layers {
bottom: "inception_4a_3x3"
top: "inception_4a_3x3"
name: "relu_inception_4a_3x3"
type: RELU
}
# conv 5 x 5
# ----------
layers {
bottom: "pool3"
top: "inception_4a_5x5_reduce"
name: "inception_4a_5x5_reduce"
type: CONVOLUTION
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
num_output: 16
kernel_size: 1
weight_filler {
type: "xavier"
std: 0.2
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layers {
bottom: "inception_4a_5x5_reduce"
top: "inception_4a_5x5_reduce"
name: "relu_inception_4a_5x5_reduce"
type: RELU
}
layers {
bottom: "inception_4a_5x5_reduce"
top: "inception_4a_5x5"
name: "inception_4a_5x5"
type: CONVOLUTION
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
num_output: 48
pad: 2
kernel_size: 5
weight_filler {
type: "xavier"
std: 0.03
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layers {
bottom: "inception_4a_5x5"
top: "inception_4a_5x5"
name: "relu_inception_4a_5x5"
type: RELU
}
# pool 3 x 3
# ----------
layers {
bottom: "pool3"
top: "inception_4a_pool"
name: "inception_4a_pool"
type: POOLING
pooling_param {
pool: MAX
kernel_size: 3
stride: 1
pad: 1
}
}
layers {
bottom: "inception_4a_pool"
top: "inception_4a_pool_proj"
name: "inception_4a_pool_proj"
type: CONVOLUTION
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
num_output: 64
kernel_size: 1
weight_filler {
type: "xavier"
std: 0.1
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layers {
bottom: "inception_4a_pool_proj"
top: "inception_4a_pool_proj"
name: "relu_inception_4a_pool_proj"
type: RELU
}
# concatenate all
# ---------------
layers {
bottom: "inception_4a_1x1"
bottom: "inception_4a_3x3"
bottom: "inception_4a_5x5"
bottom: "inception_4a_pool_proj"
top: "inception_4a_output"
name: "inception_4a_output"
type: CONCAT
}
# ===================== softmax 0 ====================
# ====================================================
# from this point, name of "RELU" in each layer name
# follows default setting.
# ave pool
layers {
bottom: "inception_4a_output"
top: "loss1_ave_pool"
name: "loss1_ave_pool"
type: POOLING
pooling_param {
pool: AVE
kernel_size: 5
stride: 3
}
}
# conv 1 x 1
layers {
bottom: "loss1_ave_pool"
top: "loss1_conv"
name: "loss1_conv"
type: CONVOLUTION
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
num_output: 128
kernel_size: 1
weight_filler {
type: "xavier"
std: 0.08
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layers {
bottom: "loss1_conv"
top: "loss1_conv"
name: "loss1_relu_conv"
type: RELU
}
# fc7-like
layers {
bottom: "loss1_conv"
top: "loss1_fc"
name: "loss1_fc"
type: INNER_PRODUCT
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
inner_product_param {
num_output: 1024
weight_filler {
type: "xavier"
std: 0.02
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layers {
bottom: "loss1_fc"
top: "loss1_fc"
name: "loss1_relu_fc"
type: RELU
}
layers {
bottom: "loss1_fc"
top: "loss1_fc"
name: "loss1_drop_fc"
type: DROPOUT
dropout_param {
dropout_ratio: 0.7
}
}
# fc8-like
layers {
bottom: "loss1_fc"
top: "loss1_classifier_model"
name: "loss1_classifier_model"
type: INNER_PRODUCT
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
inner_product_param {
num_output: 431
weight_filler {
type: "xavier"
std: 0.0009765625
}
bias_filler {
type: "constant"
value: 0
}
}
}
# loss
layers {
bottom: "loss1_classifier_model"
bottom: "label"
top: "loss1"
name: "loss1"
type: SOFTMAX_LOSS
loss_weight: 0.3
}
# accuracy, top1 and top5
layers {
bottom: "loss1_classifier_model"
bottom: "label"
top: "loss1_top-1"
name: "loss1_top-1"
type: ACCURACY
include {
phase: TEST
}
}
layers {
bottom: "loss1_classifier_model"
bottom: "label"
top: "loss1_top-5"
name: "loss1_top-5"
type: ACCURACY
accuracy_param {
top_k: 5
}
include {
phase: TEST
}
}
# ================== hierarchy 4(b) ==================
# ====================================================
# conv 1 x 1
# ----------
layers {
bottom: "inception_4a_output"
top: "inception_4b_1x1"
name: "inception_4b_1x1"
type: CONVOLUTION
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
num_output: 160
kernel_size: 1
weight_filler {
type: "xavier"
std: 0.03
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layers {
bottom: "inception_4b_1x1"
top: "inception_4b_1x1"
name: "inception_4b_relu_1x1"
type: RELU
}
# conv 3 x 3
# ----------
layers {
bottom: "inception_4a_output"
top: "inception_4b_3x3_reduce"
name: "inception_4b_3x3_reduce"
type: CONVOLUTION
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
num_output: 112
kernel_size: 1
weight_filler {
type: "xavier"
std: 0.09
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layers {
bottom: "inception_4b_3x3_reduce"
top: "inception_4b_3x3_reduce"
name: "inception_4b_relu_3x3_reduce"
type: RELU
}
layers {
bottom: "inception_4b_3x3_reduce"
top: "inception_4b_3x3"
name: "inception_4b_3x3"
type: CONVOLUTION
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
num_output: 224
pad: 1
kernel_size: 3
weight_filler {
type: "xavier"
std: 0.03
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layers {
bottom: "inception_4b_3x3"
top: "inception_4b_3x3"
name: "inception_4b_relu_3x3"
type: RELU
}
# conv 5 x 5
# ----------
layers {
bottom: "inception_4a_output"
top: "inception_4b_5x5_reduce"
name: "inception_4b_5x5_reduce"
type: CONVOLUTION
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
num_output: 24
kernel_size: 1
weight_filler {
type: "xavier"
std: 0.2
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layers {
bottom: "inception_4b_5x5_reduce"
top: "inception_4b_5x5_reduce"
name: "inception_4b_relu_5x5_reduce"
type: RELU
}
layers {
bottom: "inception_4b_5x5_reduce"
top: "inception_4b_5x5"
name: "inception_4b_5x5"
type: CONVOLUTION
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
num_output: 64
pad: 2
kernel_size: 5
weight_filler {
type: "xavier"
std: 0.03
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layers {
bottom: "inception_4b_5x5"
top: "inception_4b_5x5"
name: "inception_4b_relu_5x5"
type: RELU
}
# pool 3 x 3
# ----------
layers {
bottom: "inception_4a_output"
top: "inception_4b_pool"
name: "inception_4b_pool"
type: POOLING
pooling_param {
pool: MAX
kernel_size: 3
stride: 1
pad: 1
}
}
layers {
bottom: "inception_4b_pool"
top: "inception_4b_pool_proj"
name: "inception_4b_pool_proj"
type: CONVOLUTION
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
num_output: 64
kernel_size: 1
weight_filler {
type: "xavier"
std: 0.1
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layers {
bottom: "inception_4b_pool_proj"
top: "inception_4b_pool_proj"
name: "inception_4b_relu_pool_proj"
type: RELU
}
# concatenate all
# ---------------
layers {
bottom: "inception_4b_1x1"
bottom: "inception_4b_3x3"
bottom: "inception_4b_5x5"
bottom: "inception_4b_pool_proj"
top: "inception_4b_output"
name: "inception_4b_output"
type: CONCAT
}
# ================== hierarchy 4(c) ==================
# ====================================================
# conv 1 x 1
# ----------
layers {
bottom: "inception_4b_output"
top: "inception_4c_1x1"
name: "inception_4c_1x1"
type: CONVOLUTION
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
num_output: 128
kernel_size: 1
weight_filler {
type: "xavier"
std: 0.03
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layers {
bottom: "inception_4c_1x1"
top: "inception_4c_1x1"
name: "inception_4c_relu_1x1"
type: RELU
}
# conv 3 x 3
# ----------
layers {
bottom: "inception_4b_output"
top: "inception_4c_3x3_reduce"
name: "inception_4c_3x3_reduce"
type: CONVOLUTION
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
num_output: 128
kernel_size: 1
weight_filler {
type: "xavier"
std: 0.09
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layers {
bottom: "inception_4c_3x3_reduce"
top: "inception_4c_3x3_reduce"
name: "inception_4c_relu_3x3_reduce"
type: RELU
}
layers {
bottom: "inception_4c_3x3_reduce"
top: "inception_4c_3x3"
name: "inception_4c_3x3"
type: CONVOLUTION
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
num_output: 256
pad: 1
kernel_size: 3
weight_filler {
type: "xavier"
std: 0.03
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layers {
bottom: "inception_4c_3x3"
top: "inception_4c_3x3"
name: "inception_4c_relu_3x3"
type: RELU
}
# conv 5 x 5
# ----------
layers {
bottom: "inception_4b_output"
top: "inception_4c_5x5_reduce"
name: "inception_4c_5x5_reduce"
type: CONVOLUTION
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
num_output: 24
kernel_size: 1
weight_filler {
type: "xavier"
std: 0.2
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layers {
bottom: "inception_4c_5x5_reduce"
top: "inception_4c_5x5_reduce"
name: "inception_4c_relu_5x5_reduce"
type: RELU
}
layers {
bottom: "inception_4c_5x5_reduce"
top: "inception_4c_5x5"
name: "inception_4c_5x5"
type: CONVOLUTION
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
num_output: 64
pad: 2
kernel_size: 5
weight_filler {
type: "xavier"
std: 0.03
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layers {
bottom: "inception_4c_5x5"
top: "inception_4c_5x5"
name: "inception_4c_relu_5x5"
type: RELU
}
# pool 3 x 3
# ----------
layers {
bottom: "inception_4b_output"
top: "inception_4c_pool"
name: "inception_4c_pool"
type: POOLING
pooling_param {
pool: MAX
kernel_size: 3
stride: 1
pad: 1
}
}
layers {
bottom: "inception_4c_pool"
top: "inception_4c_pool_proj"
name: "inception_4c_pool_proj"
type: CONVOLUTION
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
num_output: 64
kernel_size: 1
weight_filler {
type: "xavier"
std: 0.1
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layers {
bottom: "inception_4c_pool_proj"
top: "inception_4c_pool_proj"
name: "inception_4c_relu_pool_proj"
type: RELU
}
# concatenate them all
# --------------------
layers {
bottom: "inception_4c_1x1"
bottom: "inception_4c_3x3"
bottom: "inception_4c_5x5"
bottom: "inception_4c_pool_proj"
top: "inception_4c_output"
name: "inception_4c_output"
type: CONCAT
}
# ================== hierarchy 4(d) ==================
# ====================================================
# conv 1 x 1
# ----------
layers {
bottom: "inception_4c_output"
top: "inception_4d_1x1"
name: "inception_4d_1x1"
type: CONVOLUTION
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
num_output: 112
kernel_size: 1
weight_filler {
type: "xavier"
std: 0.03
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layers {
bottom: "inception_4d_1x1"
top: "inception_4d_1x1"
name: "inception_4d_relu_1x1"
type: RELU
}
# conv 3 x 3
# ----------
layers {
bottom: "inception_4c_output"
top: "inception_4d_3x3_reduce"
name: "inception_4d_3x3_reduce"
type: CONVOLUTION
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
num_output: 144
kernel_size: 1
weight_filler {
type: "xavier"
std: 0.09
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layers {
bottom: "inception_4d_3x3_reduce"
top: "inception_4d_3x3_reduce"
name: "inception_4d_relu_3x3_reduce"
type: RELU
}
layers {
bottom: "inception_4d_3x3_reduce"
top: "inception_4d_3x3"
name: "inception_4d_3x3"
type: CONVOLUTION
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
num_output: 288
pad: 1
kernel_size: 3
weight_filler {
type: "xavier"
std: 0.03
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layers {
bottom: "inception_4d_3x3"
top: "inception_4d_3x3"
name: "inception_4d_relu_3x3"
type: RELU
}
# conv 5 x 5
# ----------
layers {
bottom: "inception_4c_output"
top: "inception_4d_5x5_reduce"
name: "inception_4d_5x5_reduce"
type: CONVOLUTION
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
num_output: 32
kernel_size: 1
weight_filler {
type: "xavier"
std: 0.2
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layers {
bottom: "inception_4d_5x5_reduce"
top: "inception_4d_5x5_reduce"
name: "inception_4d_relu_5x5_reduce"
type: RELU
}
layers {
bottom: "inception_4d_5x5_reduce"
top: "inception_4d_5x5"
name: "inception_4d_5x5"
type: CONVOLUTION
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
num_output: 64
pad: 2
kernel_size: 5
weight_filler {
type: "xavier"
std: 0.03
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layers {
bottom: "inception_4d_5x5"
top: "inception_4d_5x5"
name: "inception_4d_relu_5x5"
type: RELU
}
# pool 3 x 3
# ----------
layers {
bottom: "inception_4c_output"
top: "inception_4d_pool"
name: "inception_4d_pool"
type: POOLING
pooling_param {
pool: MAX
kernel_size: 3
stride: 1
pad: 1
}
}
layers {
bottom: "inception_4d_pool"
top: "inception_4d_pool_proj"
name: "inception_4d_pool_proj"
type: CONVOLUTION
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
num_output: 64
kernel_size: 1
weight_filler {
type: "xavier"
std: 0.1
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layers {
bottom: "inception_4d_pool_proj"
top: "inception_4d_pool_proj"
name: "inception_4d_relu_pool_proj"
type: RELU
}
# concatenate them all
# --------------------
layers {
bottom: "inception_4d_1x1"
bottom: "inception_4d_3x3"
bottom: "inception_4d_5x5"
bottom: "inception_4d_pool_proj"
top: "inception_4d_output"
name: "inception_4d_output"
type: CONCAT
}
# ===================== softmax 1 ====================
# ====================================================
# ave_pool -> conv(relu) -> fc -> fc -> loss, accuracy
layers {
bottom: "inception_4d_output"
top: "loss2_ave_pool"
name: "loss2_ave_pool"
type: POOLING
pooling_param {
pool: AVE
kernel_size: 5
stride: 3
}
}
layers {
bottom: "loss2_ave_pool"
top: "loss2_conv"
name: "loss2_conv"
type: CONVOLUTION
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
num_output: 128
kernel_size: 1
weight_filler {
type: "xavier"
std: 0.08
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layers {
bottom: "loss2_conv"
top: "loss2_conv"
name: "loss2_relu_conv"
type: RELU
}
layers {
bottom: "loss2_conv"
top: "loss2_fc"
name: "loss2_fc"
type: INNER_PRODUCT
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
inner_product_param {
num_output: 1024
weight_filler {
type: "xavier"
std: 0.02
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layers {
bottom: "loss2_fc"
top: "loss2_fc"
name: "loss2_relu_fc"
type: RELU
}
layers {
bottom: "loss2_fc"
top: "loss2_fc"
name: "loss2_drop_fc"
type: DROPOUT
dropout_param {
dropout_ratio: 0.7
}
}
layers {
bottom: "loss2_fc"
top: "loss2_classifier_model"
name: "loss2_classifier_model"
type: INNER_PRODUCT
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
inner_product_param {
num_output: 431
weight_filler {
type: "xavier"
std: 0.0009765625
}
bias_filler {
type: "constant"
value: 0
}
}
}
# loss
layers {
bottom: "loss2_classifier_model"
bottom: "label"
top: "loss2"
name: "loss2"
type: SOFTMAX_LOSS
loss_weight: 0.3
}
# accuracy
layers {
bottom: "loss2_classifier_model"
bottom: "label"
top: "loss2_top-1"
name: "loss2_top-1"
type: ACCURACY
include {
phase: TEST
}
}
layers {
bottom: "loss2_classifier_model"
bottom: "label"
top: "loss2_top-5"
name: "loss2_top-5"
type: ACCURACY
accuracy_param {
top_k: 5
}
include {
phase: TEST
}
}
# ================== hierarchy 4(e) ==================
# ====================================================
# conv 1 x 1
# ----------
layers {
bottom: "inception_4d_output"
top: "inception_4e_1x1"
name: "inception_4e_1x1"
type: CONVOLUTION
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
num_output: 256
kernel_size: 1
weight_filler {
type: "xavier"
std: 0.03
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layers {
bottom: "inception_4e_1x1"
top: "inception_4e_1x1"
name: "inception_4e_relu_1x1"
type: RELU
}
# conv 3 x 3
# ----------
layers {
bottom: "inception_4d_output"
top: "inception_4e_3x3_reduce"
name: "inception_4e_3x3_reduce"
type: CONVOLUTION
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
num_output: 160
kernel_size: 1
weight_filler {
type: "xavier"
std: 0.09
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layers {
bottom: "inception_4e_3x3_reduce"
top: "inception_4e_3x3_reduce"
name: "inception_4e_relu_3x3_reduce"
type: RELU
}
layers {
bottom: "inception_4e_3x3_reduce"
top: "inception_4e_3x3"
name: "inception_4e_3x3"
type: CONVOLUTION
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
num_output: 320
pad: 1
kernel_size: 3
weight_filler {
type: "xavier"
std: 0.03
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layers {
bottom: "inception_4e_3x3"
top: "inception_4e_3x3"
name: "inception_4e_relu_3x3"
type: RELU
}
# conv 5 x 5
# ----------
layers {
bottom: "inception_4d_output"
top: "inception_4e_5x5_reduce"
name: "inception_4e_5x5_reduce"
type: CONVOLUTION
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
num_output: 32
kernel_size: 1
weight_filler {
type: "xavier"
std: 0.2
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layers {
bottom: "inception_4e_5x5_reduce"
top: "inception_4e_5x5_reduce"
name: "inception_4e_relu_5x5_reduce"
type: RELU
}
layers {
bottom: "inception_4e_5x5_reduce"
top: "inception_4e_5x5"
name: "inception_4e_5x5"
type: CONVOLUTION
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
num_output: 128
pad: 2
kernel_size: 5
weight_filler {
type: "xavier"
std: 0.03
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layers {
bottom: "inception_4e_5x5"
top: "inception_4e_5x5"
name: "inception_4e_relu_5x5"
type: RELU
}
# pool 3 x 3
# ----------
layers {
bottom: "inception_4d_output"
top: "inception_4e_pool"
name: "inception_4e_pool"
type: POOLING
pooling_param {
pool: MAX
kernel_size: 3
stride: 1
pad: 1
}
}
layers {
bottom: "inception_4e_pool"
top: "inception_4e_pool_proj"
name: "inception_4e_pool_proj"
type: CONVOLUTION
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
num_output: 128
kernel_size: 1
weight_filler {
type: "xavier"
std: 0.1
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layers {
bottom: "inception_4e_pool_proj"
top: "inception_4e_pool_proj"
name: "inception_4e_relu_pool_proj"
type: RELU
}
# concatenate them all
# --------------------
layers {
bottom: "inception_4e_1x1"
bottom: "inception_4e_3x3"
bottom: "inception_4e_5x5"
bottom: "inception_4e_pool_proj"
top: "inception_4e_output"
name: "inception_4e_output"
type: CONCAT
}
layers {
bottom: "inception_4e_output"
top: "pool4"
name: "pool4"
type: POOLING
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
# ================== hierarchy 5(a) ==================
# ====================================================
# conv 1 x 1
# ----------
layers {
bottom: "pool4"
top: "inception_5a_1x1"
name: "inception_5a_1x1"
type: CONVOLUTION
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
num_output: 256
kernel_size: 1
weight_filler {
type: "xavier"
std: 0.03
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layers {
bottom: "inception_5a_1x1"
top: "inception_5a_1x1"
name: "inception_5a_relu_1x1"
type: RELU
}
# conv 3 x 3
# ----------
layers {
bottom: "pool4"
top: "inception_5a_3x3_reduce"
name: "inception_5a_3x3_reduce"
type: CONVOLUTION
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
num_output: 160
kernel_size: 1
weight_filler {
type: "xavier"
std: 0.09
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layers {
bottom: "inception_5a_3x3_reduce"
top: "inception_5a_3x3_reduce"
name: "inception_5a_relu_3x3_reduce"
type: RELU
}
layers {
bottom: "inception_5a_3x3_reduce"
top: "inception_5a_3x3"
name: "inception_5a_3x3"
type: CONVOLUTION
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
num_output: 320
pad: 1
kernel_size: 3
weight_filler {
type: "xavier"
std: 0.03
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layers {
bottom: "inception_5a_3x3"
top: "inception_5a_3x3"
name: "inception_5a_relu_3x3"
type: RELU
}
# conv 5 x 5
# ----------
layers {
bottom: "pool4"
top: "inception_5a_5x5_reduce"
name: "inception_5a_5x5_reduce"
type: CONVOLUTION
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
num_output: 32
kernel_size: 1
weight_filler {
type: "xavier"
std: 0.2
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layers {
bottom: "inception_5a_5x5_reduce"
top: "inception_5a_5x5_reduce"
name: "inception_5a_relu_5x5_reduce"
type: RELU
}
layers {
bottom: "inception_5a_5x5_reduce"
top: "inception_5a_5x5"
name: "inception_5a_5x5"
type: CONVOLUTION
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
num_output: 128
pad: 2
kernel_size: 5
weight_filler {
type: "xavier"
std: 0.03
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layers {
bottom: "inception_5a_5x5"
top: "inception_5a_5x5"
name: "inception_5a_relu_5x5"
type: RELU
}
# pool 3 x 3
# ----------
layers {
bottom: "pool4"
top: "inception_5a_pool"
name: "inception_5a_pool"
type: POOLING
pooling_param {
pool: MAX
kernel_size: 3
stride: 1
pad: 1
}
}
layers {
bottom: "inception_5a_pool"
top: "inception_5a_pool_proj"
name: "inception_5a_pool_proj"
type: CONVOLUTION
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
num_output: 128
kernel_size: 1
weight_filler {
type: "xavier"
std: 0.1
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layers {
bottom: "inception_5a_pool_proj"
top: "inception_5a_pool_proj"
name: "inception_5a_relu_pool_proj"
type: RELU
}
# concatenate them all
# --------------------
layers {
bottom: "inception_5a_1x1"
bottom: "inception_5a_3x3"
bottom: "inception_5a_5x5"
bottom: "inception_5a_pool_proj"
top: "inception_5a_output"
name: "inception_5a_output"
type: CONCAT
}
# ================== hierarchy 5(b) ==================
# ====================================================
# conv 1 x 1
# ----------
layers {
bottom: "inception_5a_output"
top: "inception_5b_1x1"
name: "inception_5b_1x1"
type: CONVOLUTION
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
num_output: 384
kernel_size: 1
weight_filler {
type: "xavier"
std: 0.03
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layers {
bottom: "inception_5b_1x1"
top: "inception_5b_1x1"
name: "inception_5b_relu_1x1"
type: RELU
}
# conv 3 x 3
# ----------
layers {
bottom: "inception_5a_output"
top: "inception_5b_3x3_reduce"
name: "inception_5b_3x3_reduce"
type: CONVOLUTION
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
num_output: 192
kernel_size: 1
weight_filler {
type: "xavier"
std: 0.09
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layers {
bottom: "inception_5b_3x3_reduce"
top: "inception_5b_3x3_reduce"
name: "inception_5b_relu_3x3_reduce"
type: RELU
}
layers {
bottom: "inception_5b_3x3_reduce"
top: "inception_5b_3x3"
name: "inception_5b_3x3"
type: CONVOLUTION
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
num_output: 384
pad: 1
kernel_size: 3
weight_filler {
type: "xavier"
std: 0.03
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layers {
bottom: "inception_5b_3x3"
top: "inception_5b_3x3"
name: "inception_5b_relu_3x3"
type: RELU
}
# conv 5 x 5
# ----------
layers {
bottom: "inception_5a_output"
top: "inception_5b_5x5_reduce"
name: "inception_5b_5x5_reduce"
type: CONVOLUTION
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
num_output: 48
kernel_size: 1
weight_filler {
type: "xavier"
std: 0.2
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layers {
bottom: "inception_5b_5x5_reduce"
top: "inception_5b_5x5_reduce"
name: "inception_5b_relu_5x5_reduce"
type: RELU
}
layers {
bottom: "inception_5b_5x5_reduce"
top: "inception_5b_5x5"
name: "inception_5b_5x5"
type: CONVOLUTION
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
num_output: 128
pad: 2
kernel_size: 5
weight_filler {
type: "xavier"
std: 0.03
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layers {
bottom: "inception_5b_5x5"
top: "inception_5b_5x5"
name: "inception_5b_relu_5x5"
type: RELU
}
# pool 3 x 3
# ----------
layers {
bottom: "inception_5a_output"
top: "inception_5b_pool"
name: "inception_5b_pool"
type: POOLING
pooling_param {
pool: MAX
kernel_size: 3
stride: 1
pad: 1
}
}
layers {
bottom: "inception_5b_pool"
top: "inception_5b_pool_proj"
name: "inception_5b_pool_proj"
type: CONVOLUTION
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
num_output: 128
kernel_size: 1
weight_filler {
type: "xavier"
std: 0.1
}
bias_filler {
type: "constant"
value: 0.2
}
}
}
layers {
bottom: "inception_5b_pool_proj"
top: "inception_5b_pool_proj"
name: "inception_5b_relu_pool_proj"
type: RELU
}
# concatenate them all
# --------------------
layers {
bottom: "inception_5b_1x1"
bottom: "inception_5b_3x3"
bottom: "inception_5b_5x5"
bottom: "inception_5b_pool_proj"
top: "inception_5b_output"
name: "inception_5b_output"
type: CONCAT
}
# ===================== softmax 2 ====================
# ====================================================
# ave_pool -> droput -> fc -> loss, accuracy
layers {
bottom: "inception_5b_output"
top: "pool5"
name: "pool5"
type: POOLING
pooling_param {
pool: AVE
kernel_size: 7
stride: 1
}
}
layers {
bottom: "pool5"
top: "pool5"
name: "pool5_drop"
type: DROPOUT
dropout_param {
dropout_ratio: 0.4
}
}
layers {
bottom: "pool5"
top: "loss3_classifier_model"
name: "loss3_classifier_model"
type: INNER_PRODUCT
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
inner_product_param {
num_output: 431
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0
}
}
}
# loss
layers {
bottom: "loss3_classifier_model"
bottom: "label"
top: "loss3"
name: "loss3"
type: SOFTMAX_LOSS
loss_weight: 1
}
# accuracy
layers {
bottom: "loss3_classifier_model"
bottom: "label"
top: "loss3_top-1"
name: "loss3_top-1"
type: ACCURACY
include {
phase: TEST
}
}
layers {
bottom: "loss3_classifier_model"
bottom: "label"
top: "loss3_top-5"
name: "loss3_top-5"
type: ACCURACY
accuracy_param {
top_k: 5
}
include {
phase: TEST
}
}
@Darwin2011
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Could you share your database's label name? I just wanna to take a look at the classification class and won't use it for business purpose.

Best Regards

@bogger
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Author

bogger commented Aug 3, 2015

@Darwin2011 You can download the full CompCars dataset and the label names are included.

@mundhenk
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mundhenk commented Aug 4, 2015

For those of us new to Caffe, can you add some directions for running (training/testing) this out of the box? Thanks.

@bogger
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bogger commented Sep 4, 2015

@mundhenk, I have updated some running advice. Please check out. :)

@YumornZhang
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hi, I have download the dataset and label.txt but your dataset can't match with the label, How you reach it?
your picture just like this:00001.jpg,00002.jpg,00003.jpg etc in cars_train dir
and the classification_train.txt/classification_test.txt just like this:78/1/2010/071c428717ceca.jpg
78/1/2010/09085b4e3c1674.jpg
78/1/2010/4c119af3fdb787.jpg
78/1/2010/4fb4b93e934b43.jpg
78/1/2010/523b3697369829.jpg
78/1/2010/646f6a9d893258.jpg
78/1/2010/6f64cf12a569dc.jpg
78/1/2010/83a76db0ac43af.jpg
78/1/2010/8e64bfdacafd49.jpg
78/1/2010/a0e9c888590e5e.jpg

i will appreciate your answer .
thank you very much

@Soccertrash
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Hello,

do you also have the deploy.prototxt and train_val.prototxt for the finetuned execution?
The above definitions look like you have used them for a full training.

Thank you

@pavel-mayer
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"The correspondence between the output nodes of the network and the 431 car models can be viewed at link. " <-- link is broken (404)

@imhafeez
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imhafeez commented Aug 9, 2017

@bogger where is database's label name? i couldn't find.

@jruizvar
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The attempt to acquire the model weights fails because the readme file does not include the sha1 field.

./scripts/download_model_binary.py models/b90eb88e31cd745525ae
ERROR: 
usage: download_model_binary.py [-h] dirname
download_model_binary.py: error: argument dirname: Must be valid Caffe model directory with a correct readme.md

@bogger, could you please include the sha1 key in the readme file?

@sidpethe
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Hey,
I'm very new to all this. Could you please tell me what each of the files are and what they do?
I'm looking to implement the pretrained version in MATLAB.

@tensor-flower
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@imhafeez label file is here https://github.com/bogger/caffe-multigpu/blob/multigpu/models/finetune_web_car/matlab/make_model_names_cls.mat

@lynnwilliam
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@imhafeez label file is here https://github.com/bogger/caffe-multigpu/blob/multigpu/models/finetune_web_car/matlab/make_model_names_cls.mat

I downloaded and opened that file, its 5400 rows of names of .jpg images.
It doesn't look like label images to me.
I have tried for hours to get this make_model project working and I just can't find any of the labels.
Its frustrating because this is probably a great project, but not all the files are available to confirm.

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