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{ | |
"cells": [ | |
{ | |
"cell_type": "code", | |
"execution_count": 6, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [ | |
{ |
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{ | |
"cells": [ | |
{ | |
"cell_type": "code", | |
"execution_count": 3, | |
"metadata": { | |
"collapsed": true | |
}, | |
"outputs": [], | |
"source": [ |
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name: "segnet" | |
layer { | |
name: "data" | |
type: "DenseImageData" | |
top: "data" | |
top: "label" | |
dense_image_data_param { | |
source: "/SegNet/CamVid/test.txt" # Change this to the absolute path to your data file | |
batch_size: 8 # Change this to be the number of Monte Carlo Dropout samples you wish to make | |
} |
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name: "VGG_ILSVRC_16_layer" | |
layer { | |
name: "data" | |
type: "DenseImageData" | |
top: "data" | |
top: "label" | |
dense_image_data_param { | |
source: "/SegNet/CamVid/test.txt" # Change this to the absolute path to your data file | |
batch_size: 4 # Change this to be the number of Monte Carlo Dropout samples you wish to make | |
} |
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name: "segnet" | |
layer { | |
name: "data" | |
type: "DenseImageData" | |
top: "data" | |
top: "label" | |
dense_image_data_param { | |
source: "/SegNet/CamVid/test.txt" # Change this to the absolute path to your data file | |
batch_size: 1 | |
} |
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name: "segnet" | |
input:"data" | |
input_dim: 1 | |
input_dim: 3 | |
input_dim: 224 | |
input_dim: 224 | |
layer { | |
name: "norm" | |
type: "LRN" | |
bottom: "data" |
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# | |
input: "data" | |
input_dim: 1 | |
input_dim: 3 | |
input_dim: 473 | |
input_dim: 473 | |
layer { | |
name: "conv1_1_3x3_s2" | |
type: "Convolution" |
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# | |
input: "data" | |
input_dim: 1 | |
input_dim: 3 | |
input_dim: 1024 | |
input_dim: 2048 | |
layer { | |
name: "data_sub1" | |
type: "Scale" |
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name: "VOC-fcn8" | |
input:"data" | |
input_dim: 1 | |
input_dim: 3 | |
input_dim: 500 | |
input_dim: 500 | |
layer { | |
name: "conv1_1" | |
type: "Convolution" |
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name: "VOC-fcn32" | |
input:"data" | |
input_dim: 1 | |
input_dim: 3 | |
input_dim: 500 | |
input_dim: 500 | |
layer { | |
name: "conv1_1" | |
type: "Convolution" |