| |
|
|
| proto_model={'T_h1_m1';'S_h1_m1';'T_h1_m2';'S_h1_m2';'T_h2_m1';'S_h2_m1'}; |
| nmb_of_proto_models=length(proto_model); |
| featured_model={'model_1';'model_2';'model_3';'model_4';'model_5';'model_6'}; |
| [nmb_of_ft_models,~]=size(featured_model); |
| for ii=1:nmb_of_proto_models |
| md=char(proto_model(ii)); |
| |
| nmb_of_image_set=zeros(1,10); |
| pr_set=zeros(1,10); |
| top_1_set=zeros(1,10); |
| model_accuracy_comparison=zeros(2,nmb_of_ft_models); |
| for fm=1:nmb_of_ft_models |
| for imgnt1kdataset=1:10 |
| reportname1 = sprintf('Model_%s/Evaluation_Data/Model_Accuracy/training_data_batch_%d_feature_module_performance_%s_var.mat',... |
| md,imgnt1kdataset, md); |
| aa=sprintf('classification_data_%d',fm); |
| bb=load(reportname1,aa); |
| c_data=bb.(aa); |
| true_lab=c_data(:,1); |
| pred_lab=c_data(:,2); |
| likelyhood=c_data(:,3); |
| top_1_majority=c_data(1,3); |
| nmb_of_images=length(true_lab); |
| nmb_of_image_set(imgnt1kdataset)=nmb_of_images; |
| idx=(abs(true_lab-pred_lab)==0); |
| aa=sum(1*idx); |
| pr=aa/nmb_of_images*100; |
| pr_set(imgnt1kdataset)=pr; |
| |
| bb=likelyhood(idx); |
| cc=length(bb); |
| idx1=(bb==top_1_majority); |
| aa=sum(idx1*1); |
| top_1=aa/cc*100; |
| top_1_set(imgnt1kdataset)=top_1; |
| end |
| model_accuracy_comparison(1,fm)=nmb_of_image_set*pr_set'/sum(nmb_of_image_set); |
| model_accuracy_comparison(2,fm)=nmb_of_image_set*top_1_set'/sum(nmb_of_image_set); |
| |
| end |
| assignin('base',md, model_accuracy_comparison') |
| end |
| |
| %% |
| tb1=table(featured_model,T_h1_m1,S_h1_m1,T_h1_m2,S_h1_m2,T_h2_m1,S_h2_m1) |
| |
| %% |
| table(featured_model,T_h1_m1,T_h1_m2,T_h2_m1) |
| |
| table(featured_model,S_h1_m1,S_h1_m2,S_h2_m1) |
| |
| %% |
| for fm=1:nmb_of_ft_models |
| %%%%%%% |
| nmb_of_image_set=zeros(1,10); |
| pr_set=zeros(1,10); |
| top_1_set=zeros(1,10); |
| model_accuracy_comparison_2=zeros(2,nmb_of_ft_models); |
| for ii=1:nmb_of_proto_models |
| md=char(proto_model(ii)); |
| for imgnt1kdataset=1:10 |
| reportname1 = sprintf('Model_ |
| md,imgnt1kdataset, md); |
| aa=sprintf('classification_data_%d',fm); |
| bb=load(reportname1,aa); |
| c_data=bb.(aa); |
| true_lab=c_data(:,1); |
| pred_lab=c_data(:,2); |
| likelyhood=c_data(:,3); |
| top_1_majority=c_data(1,3); |
| nmb_of_images=length(true_lab); |
| nmb_of_image_set(imgnt1kdataset)=nmb_of_images; |
| idx=(abs(true_lab-pred_lab)==0); |
| aa=sum(1*idx); |
| pr=aa/nmb_of_images*100; |
| pr_set(imgnt1kdataset)=pr; |
| |
| bb=likelyhood(idx); |
| cc=length(bb); |
| idx1=(bb==top_1_majority); |
| aa=sum(idx1*1); |
| top_1=aa/cc*100; |
| top_1_set(imgnt1kdataset)=top_1; |
| end |
| model_accuracy_comparison_2(1,ii)=nmb_of_image_set*pr_set'/sum(nmb_of_image_set); |
| model_accuracy_comparison_2(2,ii)=nmb_of_image_set*top_1_set'/sum(nmb_of_image_set); |
| end |
| assignin('base',char(featured_model(fm)), round(model_accuracy_comparison_2,3)') |
| end |
| |
| tb2=table(proto_model,model_1,model_2,model_3,model_4,model_5,model_6) |
| |
