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% Demonstrates the use of the EVALUATION() functions.
% choose a task-set combination
split = 'variant_test' ;
%split = 'variant_trainval' ;
%split = 'family_test' ;
%split = 'manufacturer_test' ;
switch 1
case 1
% Example 1: the evaluation set contains exactly one image-label pair
images = {'0900914'} ;
labels = {'747-400'} ;
scores = 1 ;
case 2
% Example 2: the evaluation set contains exactly all the ground truth image-label pairs (perfect
% performance).
[images, labels] = textread(fullfile('data', ['images_' split '.txt']), '%7s%*1s%s', 'delimiter', '\n', 'whitespace', '') ;
scores = ones(size(labels)) ;
case 3
% Example 3: the evaluation set contains all the possible
% image-label pair and random scores. Numeric inputs are used
% for efficiency.
[images0, labels0] = textread(fullfile('data', ['images_' split '.txt']), '%7s%*1s%s', 'delimiter', '\n', 'whitespace', '') ;
n = numel(images0) ;
clear images labels scores ;
for ci = 1:100
images{ci} = 1:n ;
labels{ci} = repmat(ci,1,n) ;
scores{ci} = randn(1,n) ;
end
images = [images{:}] ;
labels = [labels{:}] ;
scores = [scores{:}] ;
end
[confusion, results] = evaluation('data', split, images, labels, scores) ;
figure(1) ; clf ;
imagesc(confusion) ; axis tight equal ;
xlabel('predicted') ;
ylabel('ground truth') ;
title(sprintf('mean accuracy: %.2f %%\n', mean(diag(confusion))*100)) ;
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