Hi!
I am following the instructions for the DetectNet Tutorial (https://github.com/dusty-nv/jetson-inference#creating-detectnet-model-with-digits) and I am having a big problem.
On the DIGITS Server I get the following error, when I want to create the Example DetectNet Model:
Layer’s types are Ftype:FLOAT Btype:FLOAT Fmath:FLOAT Bmath:FLOAT
Importing Python module ‘caffe.layers.detectnet.clustering’
DIGITS version:
6.1.1
Caffe version:
0.16.6
Caffe flavor:
NVIDIA
Any solution? :/
Hi J0eBl4ck, are you able to run this DIGITS example with your setup, or do you get a similar error?
[url]https://github.com/NVIDIA/DIGITS/tree/master/examples/object-detection[/url]
You may want to try downgrading to DIGITS5 + nvcaffe-0.15, which is what my tutorial follows.
I will try this and keep you informed!
I get the same error. I will now downgrade to nvcaffe-0.15
Hi,
Sorry that we are not clear about the error you met.
Could you share more information about it?
Do you meet the ImportError of module ‘caffe.layers.detectnet.clustering’?
Or the system hang when creating the DetectNet model?
Thanks.
DIGITS simply crashes immediately with this error when running the job.
I downgraded now to nvcaffe-0.15 and I took a deeper look into the logs:
The machine is complaining about protobuf. Installed is 2.6.1, but he expects 3.5.
According to the command line 3.5 is installed, but I think there is somewhere a reference missing or so.
Any idea how to install protobuf correctly?
Looks like I need to set up the Host all over again :/
All because of protobuf (protoc)
Hi,
The expected protobuf version of master DIGITs is 3.
You can find this information in the Caffe building page:
-----------------------------------------------------------------
Before Caffe can be build, Protobuf 3 needs to be build. Follow this guide to build Protobuf 3
-----------------------------------------------------------------
Here is the installation guide to build protobuf 3 for your reference:
Thanks.
Hi,
i have exactly same problem.
did you solve your issue?
digits 6.1.1
nvcaffe 0.16
protobuf 3.11.2
Yes it is working. You can find the solution here:
https://devtalk.nvidia.com/default/topic/1036175/jetson-tx2/detectnet-tutorial-problem-opencv-3-/1
Basically I upgraded python and openCV.
Hi, having an issue getting the second class to show in DetectNet.Using DIGITS6. I have done the following tweaks to the two-class detectnet network
Put BBox to false
Set Detectnet Augmentation Parameters to zero
Reset Python Cluster parameters to
param_str : ‘1248, 352, 16, 0.2, 2, 0.01, 22’
Used GoogleNet as a Pre-trained Model
Please see the following Network setup:
name: “DetectNet”
layer {
name: “train_data”
type: “Data”
top: “data”
data_param {
backend: LMDB
source: “examples/kitti/kitti_train_images.lmdb”
batch_size: 10
}
include: { phase: TRAIN }
}
layer {
name: “train_label”
type: “Data”
top: “label”
data_param {
backend: LMDB
source: “examples/kitti/kitti_train_labels.lmdb”
batch_size: 10
}
include: { phase: TRAIN }
}
layer {
name: “val_data”
type: “Data”
top: “data”
data_param {
backend: LMDB
source: “examples/kitti/kitti_test_images.lmdb”
batch_size: 6
}
include: { phase: TEST stage: “val” }
}
layer {
name: “val_label”
type: “Data”
top: “label”
data_param {
backend: LMDB
source: “examples/kitti/kitti_test_labels.lmdb”
batch_size: 6
}
include: { phase: TEST stage: “val” }
}
layer {
name: “deploy_data”
type: “Input”
top: “data”
input_param {
shape {
dim: 1
dim: 3
dim: 384
dim: 1248
}
}
include: { phase: TEST not_stage: “val” }
}
layer {
name: “train_transform”
type: “DetectNetTransformation”
bottom: “data”
bottom: “label”
top: “transformed_data”
top: “transformed_label”
detectnet_groundtruth_param: {
stride: 16
scale_cvg: 0.4
gridbox_type: GRIDBOX_MIN
coverage_type: RECTANGULAR
min_cvg_len: 20
obj_norm: true
image_size_x: 1248
image_size_y: 384
crop_bboxes: false
object_class: { src: 1 dst: 0} # cars → 0
object_class: { src: 8 dst: 1} # pedestrians → 1
}
detectnet_augmentation_param: {
crop_prob: 1
shift_x: 32
shift_y: 32
flip_prob: 0.0
rotation_prob: 0
max_rotate_degree: 5
scale_prob: 0.0
scale_min: 0.8
scale_max: 1.2
hue_rotation_prob: 0.0
hue_rotation: 30
desaturation_prob: 0.0
desaturation_max: 0.0
}
transform_param: {
mean_value: 127
}
include: { phase: TRAIN }
}
layer {
name: “val_transform”
type: “DetectNetTransformation”
bottom: “data”
bottom: “label”
top: “transformed_data”
top: “transformed_label”
detectnet_groundtruth_param: {
stride: 16
scale_cvg: 0.4
gridbox_type: GRIDBOX_MIN
coverage_type: RECTANGULAR
min_cvg_len: 20
obj_norm: true
image_size_x: 1248
image_size_y: 384
crop_bboxes: false
object_class: { src: 1 dst: 0} # cars → 0
object_class: { src: 8 dst: 1} # pedestrians → 1
}
transform_param: {
mean_value: 127
}
include: { phase: TEST stage: “val” }
}
layer {
name: “deploy_transform”
type: “Power”
bottom: “data”
top: “transformed_data”
power_param {
shift: -127
}
include: { phase: TEST not_stage: “val” }
}
layer {
name: “slice-label”
type: “Slice”
bottom: “transformed_label”
top: “foreground-label”
top: “bbox-label”
top: “size-label”
top: “obj-label”
top: “coverage-label”
slice_param {
slice_dim: 1
slice_point: 1
slice_point: 5
slice_point: 7
slice_point: 8
}
include { phase: TRAIN }
include { phase: TEST stage: “val” }
}
layer {
name: “coverage-block”
type: “Concat”
bottom: “foreground-label”
bottom: “foreground-label”
bottom: “foreground-label”
bottom: “foreground-label”
top: “coverage-block”
concat_param {
concat_dim: 1
}
include { phase: TRAIN }
include { phase: TEST stage: “val” }
}
layer {
name: “size-block”
type: “Concat”
bottom: “size-label”
bottom: “size-label”
top: “size-block”
concat_param {
concat_dim: 1
}
include { phase: TRAIN }
include { phase: TEST stage: “val” }
}
layer {
name: “obj-block”
type: “Concat”
bottom: “obj-label”
bottom: “obj-label”
bottom: “obj-label”
bottom: “obj-label”
top: “obj-block”
concat_param {
concat_dim: 1
}
include { phase: TRAIN }
include { phase: TEST stage: “val” }
}
layer {
name: “bb-label-norm”
type: “Eltwise”
bottom: “bbox-label”
bottom: “size-block”
top: “bbox-label-norm”
eltwise_param {
operation: PROD
}
include { phase: TRAIN }
include { phase: TEST stage: “val” }
}
layer {
name: “bb-obj-norm”
type: “Eltwise”
bottom: “bbox-label-norm”
bottom: “obj-block”
top: “bbox-obj-label-norm”
eltwise_param {
operation: PROD
}
include { phase: TRAIN }
include { phase: TEST stage: “val” }
}
######################################################################
######################################################################
layer {
name: “conv1/7x7_s2”
type: “Convolution”
bottom: “transformed_data”
top: “conv1/7x7_s2”
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 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
}
}
}
layer {
name: “conv1/relu_7x7”
type: “ReLU”
bottom: “conv1/7x7_s2”
top: “conv1/7x7_s2”
}
layer {
name: “pool1/3x3_s2”
type: “Pooling”
bottom: “conv1/7x7_s2”
top: “pool1/3x3_s2”
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: “pool1/norm1”
type: “LRN”
bottom: “pool1/3x3_s2”
top: “pool1/norm1”
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
}
}
layer {
name: “conv2/3x3_reduce”
type: “Convolution”
bottom: “pool1/norm1”
top: “conv2/3x3_reduce”
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 64
kernel_size: 1
weight_filler {
type: “xavier”
std: 0.1
}
bias_filler {
type: “constant”
value: 0.2
}
}
}
layer {
name: “conv2/relu_3x3_reduce”
type: “ReLU”
bottom: “conv2/3x3_reduce”
top: “conv2/3x3_reduce”
}
layer {
name: “conv2/3x3”
type: “Convolution”
bottom: “conv2/3x3_reduce”
top: “conv2/3x3”
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 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
}
}
}
layer {
name: “conv2/relu_3x3”
type: “ReLU”
bottom: “conv2/3x3”
top: “conv2/3x3”
}
layer {
name: “conv2/norm2”
type: “LRN”
bottom: “conv2/3x3”
top: “conv2/norm2”
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
}
}
layer {
name: “pool2/3x3_s2”
type: “Pooling”
bottom: “conv2/norm2”
top: “pool2/3x3_s2”
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: “inception_3a/1x1”
type: “Convolution”
bottom: “pool2/3x3_s2”
top: “inception_3a/1x1”
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 64
kernel_size: 1
weight_filler {
type: “xavier”
std: 0.03
}
bias_filler {
type: “constant”
value: 0.2
}
}
}
layer {
name: “inception_3a/relu_1x1”
type: “ReLU”
bottom: “inception_3a/1x1”
top: “inception_3a/1x1”
}
layer {
name: “inception_3a/3x3_reduce”
type: “Convolution”
bottom: “pool2/3x3_s2”
top: “inception_3a/3x3_reduce”
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 96
kernel_size: 1
weight_filler {
type: “xavier”
std: 0.09
}
bias_filler {
type: “constant”
value: 0.2
}
}
}
layer {
name: “inception_3a/relu_3x3_reduce”
type: “ReLU”
bottom: “inception_3a/3x3_reduce”
top: “inception_3a/3x3_reduce”
}
layer {
name: “inception_3a/3x3”
type: “Convolution”
bottom: “inception_3a/3x3_reduce”
top: “inception_3a/3x3”
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 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
}
}
}
layer {
name: “inception_3a/relu_3x3”
type: “ReLU”
bottom: “inception_3a/3x3”
top: “inception_3a/3x3”
}
layer {
name: “inception_3a/5x5_reduce”
type: “Convolution”
bottom: “pool2/3x3_s2”
top: “inception_3a/5x5_reduce”
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 16
kernel_size: 1
weight_filler {
type: “xavier”
std: 0.2
}
bias_filler {
type: “constant”
value: 0.2
}
}
}
layer {
name: “inception_3a/relu_5x5_reduce”
type: “ReLU”
bottom: “inception_3a/5x5_reduce”
top: “inception_3a/5x5_reduce”
}
layer {
name: “inception_3a/5x5”
type: “Convolution”
bottom: “inception_3a/5x5_reduce”
top: “inception_3a/5x5”
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 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
}
}
}
layer {
name: “inception_3a/relu_5x5”
type: “ReLU”
bottom: “inception_3a/5x5”
top: “inception_3a/5x5”
}
layer {
name: “inception_3a/pool”
type: “Pooling”
bottom: “pool2/3x3_s2”
top: “inception_3a/pool”
pooling_param {
pool: MAX
kernel_size: 3
stride: 1
pad: 1
}
}
layer {
name: “inception_3a/pool_proj”
type: “Convolution”
bottom: “inception_3a/pool”
top: “inception_3a/pool_proj”
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 32
kernel_size: 1
weight_filler {
type: “xavier”
std: 0.1
}
bias_filler {
type: “constant”
value: 0.2
}
}
}
layer {
name: “inception_3a/relu_pool_proj”
type: “ReLU”
bottom: “inception_3a/pool_proj”
top: “inception_3a/pool_proj”
}
layer {
name: “inception_3a/output”
type: “Concat”
bottom: “inception_3a/1x1”
bottom: “inception_3a/3x3”
bottom: “inception_3a/5x5”
bottom: “inception_3a/pool_proj”
top: “inception_3a/output”
}
layer {
name: “inception_3b/1x1”
type: “Convolution”
bottom: “inception_3a/output”
top: “inception_3b/1x1”
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 128
kernel_size: 1
weight_filler {
type: “xavier”
std: 0.03
}
bias_filler {
type: “constant”
value: 0.2
}
}
}
layer {
name: “inception_3b/relu_1x1”
type: “ReLU”
bottom: “inception_3b/1x1”
top: “inception_3b/1x1”
}
layer {
name: “inception_3b/3x3_reduce”
type: “Convolution”
bottom: “inception_3a/output”
top: “inception_3b/3x3_reduce”
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 128
kernel_size: 1
weight_filler {
type: “xavier”
std: 0.09
}
bias_filler {
type: “constant”
value: 0.2
}
}
}
layer {
name: “inception_3b/relu_3x3_reduce”
type: “ReLU”
bottom: “inception_3b/3x3_reduce”
top: “inception_3b/3x3_reduce”
}
layer {
name: “inception_3b/3x3”
type: “Convolution”
bottom: “inception_3b/3x3_reduce”
top: “inception_3b/3x3”
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 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
}
}
}
layer {
name: “inception_3b/relu_3x3”
type: “ReLU”
bottom: “inception_3b/3x3”
top: “inception_3b/3x3”
}
layer {
name: “inception_3b/5x5_reduce”
type: “Convolution”
bottom: “inception_3a/output”
top: “inception_3b/5x5_reduce”
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 32
kernel_size: 1
weight_filler {
type: “xavier”
std: 0.2
}
bias_filler {
type: “constant”
value: 0.2
}
}
}
layer {
name: “inception_3b/relu_5x5_reduce”
type: “ReLU”
bottom: “inception_3b/5x5_reduce”
top: “inception_3b/5x5_reduce”
}
layer {
name: “inception_3b/5x5”
type: “Convolution”
bottom: “inception_3b/5x5_reduce”
top: “inception_3b/5x5”
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 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
}
}
}
layer {
name: “inception_3b/relu_5x5”
type: “ReLU”
bottom: “inception_3b/5x5”
top: “inception_3b/5x5”
}
layer {
name: “inception_3b/pool”
type: “Pooling”
bottom: “inception_3a/output”
top: “inception_3b/pool”
pooling_param {
pool: MAX
kernel_size: 3
stride: 1
pad: 1
}
}
layer {
name: “inception_3b/pool_proj”
type: “Convolution”
bottom: “inception_3b/pool”
top: “inception_3b/pool_proj”
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 64
kernel_size: 1
weight_filler {
type: “xavier”
std: 0.1
}
bias_filler {
type: “constant”
value: 0.2
}
}
}
layer {
name: “inception_3b/relu_pool_proj”
type: “ReLU”
bottom: “inception_3b/pool_proj”
top: “inception_3b/pool_proj”
}
layer {
name: “inception_3b/output”
type: “Concat”
bottom: “inception_3b/1x1”
bottom: “inception_3b/3x3”
bottom: “inception_3b/5x5”
bottom: “inception_3b/pool_proj”
top: “inception_3b/output”
}
layer {
name: “pool3/3x3_s2”
type: “Pooling”
bottom: “inception_3b/output”
top: “pool3/3x3_s2”
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: “inception_4a/1x1”
type: “Convolution”
bottom: “pool3/3x3_s2”
top: “inception_4a/1x1”
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 192
kernel_size: 1
weight_filler {
type: “xavier”
std: 0.03
}
bias_filler {
type: “constant”
value: 0.2
}
}
}
layer {
name: “inception_4a/relu_1x1”
type: “ReLU”
bottom: “inception_4a/1x1”
top: “inception_4a/1x1”
}
layer {
name: “inception_4a/3x3_reduce”
type: “Convolution”
bottom: “pool3/3x3_s2”
top: “inception_4a/3x3_reduce”
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 96
kernel_size: 1
weight_filler {
type: “xavier”
std: 0.09
}
bias_filler {
type: “constant”
value: 0.2
}
}
}
layer {
name: “inception_4a/relu_3x3_reduce”
type: “ReLU”
bottom: “inception_4a/3x3_reduce”
top: “inception_4a/3x3_reduce”
}
layer {
name: “inception_4a/3x3”
type: “Convolution”
bottom: “inception_4a/3x3_reduce”
top: “inception_4a/3x3”
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 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
}
}
}
layer {
name: “inception_4a/relu_3x3”
type: “ReLU”
bottom: “inception_4a/3x3”
top: “inception_4a/3x3”
}
layer {
name: “inception_4a/5x5_reduce”
type: “Convolution”
bottom: “pool3/3x3_s2”
top: “inception_4a/5x5_reduce”
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 16
kernel_size: 1
weight_filler {
type: “xavier”
std: 0.2
}
bias_filler {
type: “constant”
value: 0.2
}
}
}
layer {
name: “inception_4a/relu_5x5_reduce”
type: “ReLU”
bottom: “inception_4a/5x5_reduce”
top: “inception_4a/5x5_reduce”
}
layer {
name: “inception_4a/5x5”
type: “Convolution”
bottom: “inception_4a/5x5_reduce”
top: “inception_4a/5x5”
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 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
}
}
}
layer {
name: “inception_4a/relu_5x5”
type: “ReLU”
bottom: “inception_4a/5x5”
top: “inception_4a/5x5”
}
layer {
name: “inception_4a/pool”
type: “Pooling”
bottom: “pool3/3x3_s2”
top: “inception_4a/pool”
pooling_param {
pool: MAX
kernel_size: 3
stride: 1
pad: 1
}
}
layer {
name: “inception_4a/pool_proj”
type: “Convolution”
bottom: “inception_4a/pool”
top: “inception_4a/pool_proj”
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 64
kernel_size: 1
weight_filler {
type: “xavier”
std: 0.1
}
bias_filler {
type: “constant”
value: 0.2
}
}
}
layer {
name: “inception_4a/relu_pool_proj”
type: “ReLU”
bottom: “inception_4a/pool_proj”
top: “inception_4a/pool_proj”
}
layer {
name: “inception_4a/output”
type: “Concat”
bottom: “inception_4a/1x1”
bottom: “inception_4a/3x3”
bottom: “inception_4a/5x5”
bottom: “inception_4a/pool_proj”
top: “inception_4a/output”
}
layer {
name: “inception_4b/1x1”
type: “Convolution”
bottom: “inception_4a/output”
top: “inception_4b/1x1”
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 160
kernel_size: 1
weight_filler {
type: “xavier”
std: 0.03
}
bias_filler {
type: “constant”
value: 0.2
}
}
}
layer {
name: “inception_4b/relu_1x1”
type: “ReLU”
bottom: “inception_4b/1x1”
top: “inception_4b/1x1”
}
layer {
name: “inception_4b/3x3_reduce”
type: “Convolution”
bottom: “inception_4a/output”
top: “inception_4b/3x3_reduce”
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 112
kernel_size: 1
weight_filler {
type: “xavier”
std: 0.09
}
bias_filler {
type: “constant”
value: 0.2
}
}
}
layer {
name: “inception_4b/relu_3x3_reduce”
type: “ReLU”
bottom: “inception_4b/3x3_reduce”
top: “inception_4b/3x3_reduce”
}
layer {
name: “inception_4b/3x3”
type: “Convolution”
bottom: “inception_4b/3x3_reduce”
top: “inception_4b/3x3”
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 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
}
}
}
layer {
name: “inception_4b/relu_3x3”
type: “ReLU”
bottom: “inception_4b/3x3”
top: “inception_4b/3x3”
}
layer {
name: “inception_4b/5x5_reduce”
type: “Convolution”
bottom: “inception_4a/output”
top: “inception_4b/5x5_reduce”
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 24
kernel_size: 1
weight_filler {
type: “xavier”
std: 0.2
}
bias_filler {
type: “constant”
value: 0.2
}
}
}
layer {
name: “inception_4b/relu_5x5_reduce”
type: “ReLU”
bottom: “inception_4b/5x5_reduce”
top: “inception_4b/5x5_reduce”
}
layer {
name: “inception_4b/5x5”
type: “Convolution”
bottom: “inception_4b/5x5_reduce”
top: “inception_4b/5x5”
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 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
}
}
}
layer {
name: “inception_4b/relu_5x5”
type: “ReLU”
bottom: “inception_4b/5x5”
top: “inception_4b/5x5”
}
layer {
name: “inception_4b/pool”
type: “Pooling”
bottom: “inception_4a/output”
top: “inception_4b/pool”
pooling_param {
pool: MAX
kernel_size: 3
stride: 1
pad: 1
}
}
layer {
name: “inception_4b/pool_proj”
type: “Convolution”
bottom: “inception_4b/pool”
top: “inception_4b/pool_proj”
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 64
kernel_size: 1
weight_filler {
type: “xavier”
std: 0.1
}
bias_filler {
type: “constant”
value: 0.2
}
}
}
layer {
name: “inception_4b/relu_pool_proj”
type: “ReLU”
bottom: “inception_4b/pool_proj”
top: “inception_4b/pool_proj”
}
layer {
name: “inception_4b/output”
type: “Concat”
bottom: “inception_4b/1x1”
bottom: “inception_4b/3x3”
bottom: “inception_4b/5x5”
bottom: “inception_4b/pool_proj”
top: “inception_4b/output”
}
layer {
name: “inception_4c/1x1”
type: “Convolution”
bottom: “inception_4b/output”
top: “inception_4c/1x1”
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 128
kernel_size: 1
weight_filler {
type: “xavier”
std: 0.03
}
bias_filler {
type: “constant”
value: 0.2
}
}
}
layer {
name: “inception_4c/relu_1x1”
type: “ReLU”
bottom: “inception_4c/1x1”
top: “inception_4c/1x1”
}
layer {
name: “inception_4c/3x3_reduce”
type: “Convolution”
bottom: “inception_4b/output”
top: “inception_4c/3x3_reduce”
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 128
kernel_size: 1
weight_filler {
type: “xavier”
std: 0.09
}
bias_filler {
type: “constant”
value: 0.2
}
}
}
layer {
name: “inception_4c/relu_3x3_reduce”
type: “ReLU”
bottom: “inception_4c/3x3_reduce”
top: “inception_4c/3x3_reduce”
}
layer {
name: “inception_4c/3x3”
type: “Convolution”
bottom: “inception_4c/3x3_reduce”
top: “inception_4c/3x3”
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 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
}
}
}
layer {
name: “inception_4c/relu_3x3”
type: “ReLU”
bottom: “inception_4c/3x3”
top: “inception_4c/3x3”
}
layer {
name: “inception_4c/5x5_reduce”
type: “Convolution”
bottom: “inception_4b/output”
top: “inception_4c/5x5_reduce”
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 24
kernel_size: 1
weight_filler {
type: “xavier”
std: 0.2
}
bias_filler {
type: “constant”
value: 0.2
}
}
}
layer {
name: “inception_4c/relu_5x5_reduce”
type: “ReLU”
bottom: “inception_4c/5x5_reduce”
top: “inception_4c/5x5_reduce”
}
layer {
name: “inception_4c/5x5”
type: “Convolution”
bottom: “inception_4c/5x5_reduce”
top: “inception_4c/5x5”
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 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
}
}
}
layer {
name: “inception_4c/relu_5x5”
type: “ReLU”
bottom: “inception_4c/5x5”
top: “inception_4c/5x5”
}
layer {
name: “inception_4c/pool”
type: “Pooling”
bottom: “inception_4b/output”
top: “inception_4c/pool”
pooling_param {
pool: MAX
kernel_size: 3
stride: 1
pad: 1
}
}
layer {
name: “inception_4c/pool_proj”
type: “Convolution”
bottom: “inception_4c/pool”
top: “inception_4c/pool_proj”
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 64
kernel_size: 1
weight_filler {
type: “xavier”
std: 0.1
}
bias_filler {
type: “constant”
value: 0.2
}
}
}
layer {
name: “inception_4c/relu_pool_proj”
type: “ReLU”
bottom: “inception_4c/pool_proj”
top: “inception_4c/pool_proj”
}
layer {
name: “inception_4c/output”
type: “Concat”
bottom: “inception_4c/1x1”
bottom: “inception_4c/3x3”
bottom: “inception_4c/5x5”
bottom: “inception_4c/pool_proj”
top: “inception_4c/output”
}
layer {
name: “inception_4d/1x1”
type: “Convolution”
bottom: “inception_4c/output”
top: “inception_4d/1x1”
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 112
kernel_size: 1
weight_filler {
type: “xavier”
std: 0.1
}
bias_filler {
type: “constant”
value: 0.2
}
}
}
layer {
name: “inception_4d/relu_1x1”
type: “ReLU”
bottom: “inception_4d/1x1”
top: “inception_4d/1x1”
}
layer {
name: “inception_4d/3x3_reduce”
type: “Convolution”
bottom: “inception_4c/output”
top: “inception_4d/3x3_reduce”
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 144
kernel_size: 1
weight_filler {
type: “xavier”
std: 0.1
}
bias_filler {
type: “constant”
value: 0.2
}
}
}
layer {
name: “inception_4d/relu_3x3_reduce”
type: “ReLU”
bottom: “inception_4d/3x3_reduce”
top: “inception_4d/3x3_reduce”
}
layer {
name: “inception_4d/3x3”
type: “Convolution”
bottom: “inception_4d/3x3_reduce”
top: “inception_4d/3x3”
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 288
pad: 1
kernel_size: 3
weight_filler {
type: “xavier”
std: 0.1
}
bias_filler {
type: “constant”
value: 0.2
}
}
}
layer {
name: “inception_4d/relu_3x3”
type: “ReLU”
bottom: “inception_4d/3x3”
top: “inception_4d/3x3”
}
layer {
name: “inception_4d/5x5_reduce”
type: “Convolution”
bottom: “inception_4c/output”
top: “inception_4d/5x5_reduce”
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 32
kernel_size: 1
weight_filler {
type: “xavier”
std: 0.1
}
bias_filler {
type: “constant”
value: 0.2
}
}
}
layer {
name: “inception_4d/relu_5x5_reduce”
type: “ReLU”
bottom: “inception_4d/5x5_reduce”
top: “inception_4d/5x5_reduce”
}
layer {
name: “inception_4d/5x5”
type: “Convolution”
bottom: “inception_4d/5x5_reduce”
top: “inception_4d/5x5”
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 64
pad: 2
kernel_size: 5
weight_filler {
type: “xavier”
std: 0.1
}
bias_filler {
type: “constant”
value: 0.2
}
}
}
layer {
name: “inception_4d/relu_5x5”
type: “ReLU”
bottom: “inception_4d/5x5”
top: “inception_4d/5x5”
}
layer {
name: “inception_4d/pool”
type: “Pooling”
bottom: “inception_4c/output”
top: “inception_4d/pool”
pooling_param {
pool: MAX
kernel_size: 3
stride: 1
pad: 1
}
}
layer {
name: “inception_4d/pool_proj”
type: “Convolution”
bottom: “inception_4d/pool”
top: “inception_4d/pool_proj”
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 64
kernel_size: 1
weight_filler {
type: “xavier”
std: 0.1
}
bias_filler {
type: “constant”
value: 0.2
}
}
}
layer {
name: “inception_4d/relu_pool_proj”
type: “ReLU”
bottom: “inception_4d/pool_proj”
top: “inception_4d/pool_proj”
}
layer {
name: “inception_4d/output”
type: “Concat”
bottom: “inception_4d/1x1”
bottom: “inception_4d/3x3”
bottom: “inception_4d/5x5”
bottom: “inception_4d/pool_proj”
top: “inception_4d/output”
}
layer {
name: “inception_4e/1x1”
type: “Convolution”
bottom: “inception_4d/output”
top: “inception_4e/1x1”
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 256
kernel_size: 1
weight_filler {
type: “xavier”
std: 0.03
}
bias_filler {
type: “constant”
value: 0.2
}
}
}
layer {
name: “inception_4e/relu_1x1”
type: “ReLU”
bottom: “inception_4e/1x1”
top: “inception_4e/1x1”
}
layer {
name: “inception_4e/3x3_reduce”
type: “Convolution”
bottom: “inception_4d/output”
top: “inception_4e/3x3_reduce”
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 160
kernel_size: 1
weight_filler {
type: “xavier”
std: 0.09
}
bias_filler {
type: “constant”
value: 0.2
}
}
}
layer {
name: “inception_4e/relu_3x3_reduce”
type: “ReLU”
bottom: “inception_4e/3x3_reduce”
top: “inception_4e/3x3_reduce”
}
layer {
name: “inception_4e/3x3”
type: “Convolution”
bottom: “inception_4e/3x3_reduce”
top: “inception_4e/3x3”
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 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
}
}
}
layer {
name: “inception_4e/relu_3x3”
type: “ReLU”
bottom: “inception_4e/3x3”
top: “inception_4e/3x3”
}
layer {
name: “inception_4e/5x5_reduce”
type: “Convolution”
bottom: “inception_4d/output”
top: “inception_4e/5x5_reduce”
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 32
kernel_size: 1
weight_filler {
type: “xavier”
std: 0.2
}
bias_filler {
type: “constant”
value: 0.2
}
}
}
layer {
name: “inception_4e/relu_5x5_reduce”
type: “ReLU”
bottom: “inception_4e/5x5_reduce”
top: “inception_4e/5x5_reduce”
}
layer {
name: “inception_4e/5x5”
type: “Convolution”
bottom: “inception_4e/5x5_reduce”
top: “inception_4e/5x5”
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 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
}
}
}
layer {
name: “inception_4e/relu_5x5”
type: “ReLU”
bottom: “inception_4e/5x5”
top: “inception_4e/5x5”
}
layer {
name: “inception_4e/pool”
type: “Pooling”
bottom: “inception_4d/output”
top: “inception_4e/pool”
pooling_param {
pool: MAX
kernel_size: 3
stride: 1
pad: 1
}
}
layer {
name: “inception_4e/pool_proj”
type: “Convolution”
bottom: “inception_4e/pool”
top: “inception_4e/pool_proj”
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 128
kernel_size: 1
weight_filler {
type: “xavier”
std: 0.1
}
bias_filler {
type: “constant”
value: 0.2
}
}
}
layer {
name: “inception_4e/relu_pool_proj”
type: “ReLU”
bottom: “inception_4e/pool_proj”
top: “inception_4e/pool_proj”
}
layer {
name: “inception_4e/output”
type: “Concat”
bottom: “inception_4e/1x1”
bottom: “inception_4e/3x3”
bottom: “inception_4e/5x5”
bottom: “inception_4e/pool_proj”
top: “inception_4e/output”
}
layer {
name: “inception_5a/1x1”
type: “Convolution”
bottom: “inception_4e/output”
top: “inception_5a/1x1”
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 256
kernel_size: 1
weight_filler {
type: “xavier”
std: 0.03
}
bias_filler {
type: “constant”
value: 0.2
}
}
}
layer {
name: “inception_5a/relu_1x1”
type: “ReLU”
bottom: “inception_5a/1x1”
top: “inception_5a/1x1”
}
layer {
name: “inception_5a/3x3_reduce”
type: “Convolution”
bottom: “inception_4e/output”
top: “inception_5a/3x3_reduce”
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 160
kernel_size: 1
weight_filler {
type: “xavier”
std: 0.09
}
bias_filler {
type: “constant”
value: 0.2
}
}
}
layer {
name: “inception_5a/relu_3x3_reduce”
type: “ReLU”
bottom: “inception_5a/3x3_reduce”
top: “inception_5a/3x3_reduce”
}
layer {
name: “inception_5a/3x3”
type: “Convolution”
bottom: “inception_5a/3x3_reduce”
top: “inception_5a/3x3”
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 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
}
}
}
layer {
name: “inception_5a/relu_3x3”
type: “ReLU”
bottom: “inception_5a/3x3”
top: “inception_5a/3x3”
}
layer {
name: “inception_5a/5x5_reduce”
type: “Convolution”
bottom: “inception_4e/output”
top: “inception_5a/5x5_reduce”
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 32
kernel_size: 1
weight_filler {
type: “xavier”
std: 0.2
}
bias_filler {
type: “constant”
value: 0.2
}
}
}
layer {
name: “inception_5a/relu_5x5_reduce”
type: “ReLU”
bottom: “inception_5a/5x5_reduce”
top: “inception_5a/5x5_reduce”
}
layer {
name: “inception_5a/5x5”
type: “Convolution”
bottom: “inception_5a/5x5_reduce”
top: “inception_5a/5x5”
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 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
}
}
}
layer {
name: “inception_5a/relu_5x5”
type: “ReLU”
bottom: “inception_5a/5x5”
top: “inception_5a/5x5”
}
layer {
name: “inception_5a/pool”
type: “Pooling”
bottom: “inception_4e/output”
top: “inception_5a/pool”
pooling_param {
pool: MAX
kernel_size: 3
stride: 1
pad: 1
}
}
layer {
name: “inception_5a/pool_proj”
type: “Convolution”
bottom: “inception_5a/pool”
top: “inception_5a/pool_proj”
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 128
kernel_size: 1
weight_filler {
type: “xavier”
std: 0.1
}
bias_filler {
type: “constant”
value: 0.2
}
}
}
layer {
name: “inception_5a/relu_pool_proj”
type: “ReLU”
bottom: “inception_5a/pool_proj”
top: “inception_5a/pool_proj”
}
layer {
name: “inception_5a/output”
type: “Concat”
bottom: “inception_5a/1x1”
bottom: “inception_5a/3x3”
bottom: “inception_5a/5x5”
bottom: “inception_5a/pool_proj”
top: “inception_5a/output”
}
layer {
name: “inception_5b/1x1”
type: “Convolution”
bottom: “inception_5a/output”
top: “inception_5b/1x1”
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 384
kernel_size: 1
weight_filler {
type: “xavier”
std: 0.1
}
bias_filler {
type: “constant”
value: 0.2
}
}
}
layer {
name: “inception_5b/relu_1x1”
type: “ReLU”
bottom: “inception_5b/1x1”
top: “inception_5b/1x1”
}
layer {
name: “inception_5b/3x3_reduce”
type: “Convolution”
bottom: “inception_5a/output”
top: “inception_5b/3x3_reduce”
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 1
decay_mult: 0
}
convolution_param {
num_output: 192
kernel_size: 1
weight_filler {
type: “xavier”
std: 0.1
}
bias_filler {
type: “constant”
value: 0.2
}
}
}
layer {
name: “inception_5b/relu_3x3_reduce”
type: “ReLU”
bottom: “inception_5b/3x3_reduce”
top: “inception_5b/3x3_reduce”
}
layer {
name: “inception_5b/3x3”
type: “Convolution”
bottom: “inception_5b/3x3_reduce”
top: “inception_5b/3x3”
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 384
pad: 1
kernel_size: 3
weight_filler {
type: “xavier”
std: 0.1
}
bias_filler {
type: “constant”
value: 0.2
}
}
}
layer {
name: “inception_5b/relu_3x3”
type: “ReLU”
bottom: “inception_5b/3x3”
top: “inception_5b/3x3”
}
layer {
name: “inception_5b/5x5_reduce”
type: “Convolution”
bottom: “inception_5a/output”
top: “inception_5b/5x5_reduce”
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 48
kernel_size: 1
weight_filler {
type: “xavier”
std: 0.1
}
bias_filler {
type: “constant”
value: 0.2
}
}
}
layer {
name: “inception_5b/relu_5x5_reduce”
type: “ReLU”
bottom: “inception_5b/5x5_reduce”
top: “inception_5b/5x5_reduce”
}
layer {
name: “inception_5b/5x5”
type: “Convolution”
bottom: “inception_5b/5x5_reduce”
top: “inception_5b/5x5”
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 128
pad: 2
kernel_size: 5
weight_filler {
type: “xavier”
std: 0.1
}
bias_filler {
type: “constant”
value: 0.2
}
}
}
layer {
name: “inception_5b/relu_5x5”
type: “ReLU”
bottom: “inception_5b/5x5”
top: “inception_5b/5x5”
}
layer {
name: “inception_5b/pool”
type: “Pooling”
bottom: “inception_5a/output”
top: “inception_5b/pool”
pooling_param {
pool: MAX
kernel_size: 3
stride: 1
pad: 1
}
}
layer {
name: “inception_5b/pool_proj”
type: “Convolution”
bottom: “inception_5b/pool”
top: “inception_5b/pool_proj”
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 128
kernel_size: 1
weight_filler {
type: “xavier”
std: 0.1
}
bias_filler {
type: “constant”
value: 0.2
}
}
}
layer {
name: “inception_5b/relu_pool_proj”
type: “ReLU”
bottom: “inception_5b/pool_proj”
top: “inception_5b/pool_proj”
}
layer {
name: “inception_5b/output”
type: “Concat”
bottom: “inception_5b/1x1”
bottom: “inception_5b/3x3”
bottom: “inception_5b/5x5”
bottom: “inception_5b/pool_proj”
top: “inception_5b/output”
}
layer {
name: “pool5/drop_s1”
type: “Dropout”
bottom: “inception_5b/output”
top: “pool5/drop_s1”
dropout_param {
dropout_ratio: 0.4
}
}
layer {
name: “cvg/classifier”
type: “Convolution”
bottom: “pool5/drop_s1”
top: “cvg/classifier”
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 2
kernel_size: 1
weight_filler {
type: “xavier”
std: 0.03
}
bias_filler {
type: “constant”
value: 0.
}
}
}
layer {
name: “coverage/sig”
type: “Sigmoid”
bottom: “cvg/classifier”
top: “coverage”
}
layer {
name: “bbox/regressor”
type: “Convolution”
bottom: “pool5/drop_s1”
top: “bboxes”
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 4
kernel_size: 1
weight_filler {
type: “xavier”
std: 0.03
}
bias_filler {
type: “constant”
value: 0.
}
}
}
######################################################################
######################################################################
layer {
name: “bbox_mask”
type: “Eltwise”
bottom: “bboxes”
bottom: “coverage-block”
top: “bboxes-masked”
eltwise_param {
operation: PROD
}
include { phase: TRAIN }
include { phase: TEST stage: “val” }
}
layer {
name: “bbox-norm”
type: “Eltwise”
bottom: “bboxes-masked”
bottom: “size-block”
top: “bboxes-masked-norm”
eltwise_param {
operation: PROD
}
include { phase: TRAIN }
include { phase: TEST stage: “val” }
}
layer {
name: “bbox-obj-norm”
type: “Eltwise”
bottom: “bboxes-masked-norm”
bottom: “obj-block”
top: “bboxes-obj-masked-norm”
eltwise_param {
operation: PROD
}
include { phase: TRAIN }
include { phase: TEST stage: “val” }
}
layer {
name: “bbox_loss”
type: “L1Loss”
bottom: “bboxes-obj-masked-norm”
bottom: “bbox-obj-label-norm”
top: “loss_bbox”
loss_weight: 2
include { phase: TRAIN }
include { phase: TEST stage: “val” }
}
layer {
name: “coverage_loss”
type: “EuclideanLoss”
bottom: “coverage”
bottom: “coverage-label”
top: “loss_coverage”
include { phase: TRAIN }
include { phase: TEST stage: “val” }
}
layer {
type: ‘Python’
name: ‘cluster’
bottom: ‘coverage’
bottom: ‘bboxes’
top: ‘bbox-list-class0’
top: ‘bbox-list-class1’
python_param {
module: ‘caffe.layers.detectnet.clustering’
layer: ‘ClusterDetections’
param_str : ‘1248, 384, 16, 0.2, 1.5, 0.01, 22, 2’
}
include: { phase: TEST }
}
layer {
type: ‘Python’
name: ‘cluster_gt’
bottom: ‘coverage-label’
bottom: ‘bbox-label’
top: ‘bbox-list-label-class0’
top: ‘bbox-list-label-class1’
python_param {
module: ‘caffe.layers.detectnet.clustering’
layer: ‘ClusterGroundtruth’
param_str : ‘1248, 384, 16, 2’
}
include: { phase: TEST stage: “val” }
}
layer {
type: ‘Python’
name: ‘score-class0’
bottom: ‘bbox-list-label-class0’
bottom: ‘bbox-list-class0’
top: ‘bbox-list-scored-class0’
python_param {
module: ‘caffe.layers.detectnet.mean_ap’
layer: ‘ScoreDetections’
}
include: { phase: TEST stage: “val” }
}
layer {
type: ‘Python’
name: ‘mAP-class0’
bottom: ‘bbox-list-scored-class0’
top: ‘mAP-class0’
top: ‘precision-class0’
top: ‘recall-class0’
python_param {
module: ‘caffe.layers.detectnet.mean_ap’
layer: ‘mAP’
param_str : ‘1248, 384, 16’
}
include: { phase: TEST stage: “val” }
}
layer {
type: ‘Python’
name: ‘score-class1’
bottom: ‘bbox-list-label-class1’
bottom: ‘bbox-list-class1’
top: ‘bbox-list-scored-class1’
python_param {
module: ‘caffe.layers.detectnet.mean_ap’
layer: ‘ScoreDetections’
}
include: { phase: TEST stage: “val” }
}
layer {
type: ‘Python’
name: ‘mAP-class1’
bottom: ‘bbox-list-scored-class1’
top: ‘mAP-class1’
top: ‘precision-class1’
top: ‘recall-class1’
python_param {
module: ‘caffe.layers.detectnet.mean_ap’
layer: ‘mAP’
param_str : ‘1248, 384, 16’
}
include: { phase: TEST stage: “val” }
}
Please open a new topic for your issue. Thanks