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# DPN-68 | |
name: "DPN-68" | |
layer { | |
name: "train-data" | |
type: "Data" | |
top: "data" | |
top: "label" | |
transform_param { | |
mirror: true | |
crop_size: 224 |
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#Xception50 | |
name: "Xception50" | |
layer { | |
name: "train-data" | |
type: "Data" | |
top: "data" | |
top: "label" | |
transform_param { | |
mirror: true | |
crop_size: 224 |
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# Inception V4 | |
name: "Inception V4" | |
layer { | |
name: "train-data" | |
type: "Data" | |
top: "data" | |
top: "label" | |
transform_param { | |
mirror: true | |
crop_size: 299 |
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%%%%%%%%%%%%%%%%%%%%%%%%%% READ THE FOLDER PATH %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% | |
Dir = 'Directory Path'; | |
%%%%%%%%%%%%%%%%%%%% LIST ALL IMAGES IN THE FOLDER %%%%%%%%%%%%%%%%%%%%%%%%%%%% | |
S = dir(fullfile(Dir, '*.jpg')); | |
for K = 1:numel(S) | |
F = fullfile(Dir, S(K).name); | |
%%%%%%%%%%%%%%%%%%%%%%%%%%%%% READ AN IMAGE %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% | |
I = imread(F); | |
%%%%%%%%%%%%%%%%%%%%%%%%% RESIZE IMAGE [300 x 300] %%%%%%%%%%%%%%%%%%%%%%%%%%%% | |
I = imresize(I, [300, 300]); |
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%.................................................................... | |
% // Uniform Random Noise in The Range [0, 1] Scaled by k = 0.3. | | |
% // Rayleigh Noise with its [A = 0, B = 1] Scaled by k = 0.3. | | |
% // Exponential Noise Scaled By 0.2 Where The Value of A = 1. | | |
% // Gamma (Erlang) Noise where [A, B ] = [2, 5] and SF 'K' = 0.1. | | |
% // imnoise2.m File Function Is Called Inside This Program. | | |
%...................................................................| | |
%// Read an Image from The Given Path. | |
img = imread('input/img.bmp'); |
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%.................................................................... | |
% // Calculate Mean and Variance of An Input Image [M x N]. | | |
% // Compute Local Variance of Size [M x N] from The above Image. | | |
% // Use Mean and Variance to Introduce Gaussian Noise. | | |
% // Use Local Variance to Introduce Localvar Noise in The Image. | | |
%...................................................................| | |
%// Read an Image from The Given Path | |
img = imread('input/img.bmp'); |
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%...................................................................... | |
% PERFORMANCE METICES | | |
%.....................................................................| | |
% ENTROPY | PEAK SIGNAL TO NOISE RATIO (PSNR) | SIGNAL TO NOISE RATIO | | |
% MEAN ABSOLUTE ERROR (MAE) | MEAN SQUARE ERROR (MSE)| CORRELATION | | |
% ROOT MEAN SQUARE ERROR (RMSE) | STRUCTURAL SIMILARITY INDEX (SSIM) | | |
% STANDARD DEVIATION (SD) | PERCENTAGE FIT ERROR (PFE) | | |
%.....................................................................| | |
%%%%%%%%%%%%%%%%%%% START OF THE PROGRAM %%%%%%%%%%%%%%%%%%%%%%%%% |
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# ResNeXt50 | |
name: "ResNeXt50" | |
layer { | |
name: "train-data" | |
type: "Data" | |
top: "data" | |
top: "label" | |
transform_param { | |
mirror: true | |
crop_size: 224 |
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#ResNet50 | |
name: "ResNet50" | |
layer { | |
name: "train-data" | |
type: "Data" | |
top: "data" | |
top: "label" | |
transform_param { | |
mirror: true | |
crop_size: 224 |
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# ResNet34 | |
name: "ResNet34" | |
layer { | |
name: "train-data" | |
type: "Data" | |
top: "data" | |
top: "label" | |
transform_param { | |
mirror: true | |
crop_size: 224 |
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