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CVPR2017 - an ultra-compact bilinear model for fine-grained classification

Home Page: https://www.cs.cmu.edu/~shuk/lr_bilinear.html

CMake 1.13% Makefile 1.43% HTML 2.06% CSS 0.60% C++ 35.60% C 17.28% Cuda 9.09% MATLAB 22.29% M 0.01% Python 6.28% Shell 0.78% Java 0.02% TeX 2.97% JavaScript 0.01% Clean 0.08% Roff 0.13% Objective-C 0.22%
visualization toolbox demo caffe fine-grained low-rank-factorization

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low-rank-bilinear-pooling's Issues

demo 4 trained on DTD not working

Hi @aimerykong

Thank you for sharing you code. I tried your demo 4 locally, switching the CUB dataset to DTD to see the performance. However, the objective is not decreasing after many epochs.

What I did is simply add these lines to your main100 and main101 code.

% main100_demo_PCA_conv53_forDimRed.m
%% prepare data
% dataset: 'CUB', 'MIT', 'DTD', 'aircrafts', 'cars'
if strcmp(dataset, 'CUB')
    num_classes = 200;
    imdbFile = fullfile('imdbFolder', dataset, [lower(dataset) '-seed-01'], 'imdb-seed-1.mat');
elseif strcmp(dataset, 'DTD')
    num_classes = 47;
    imdbFile = fullfile('imdbFolder', dataset, [lower(dataset) '-seed-01'], 'imdb-seed-1.mat');
end
% ... omitting lines between the dataset and the last save line.
save([dataset,'_bisvmEpoch102'], 'Gram', 'U', 's', 'S');
% main101_...
if strcmp(dataset, 'CUB')
    % omit since it is the same as your code.
elseif strcmp(dataset, 'DTD')
    num_classes = 47;
    dataDir = './data/dtd';
    imdbFile = fullfile('imdbFolder', dataset, [lower(dataset) '-seed-01'], 'imdb-seed-1.mat');
%     imdbFile = fullfile('imdbFolder', dataset, [lower(dataset) '-seed-01'], 'imdb-seed-2_flipAug.mat');
    if ~exist(imdbFile, 'file')        
        imdb = dtd_get_database(dataDir);
        
        imdbDir=fileparts(imdbFile);
        if ~isdir(imdbDir)
            mkdir(imdbDir);
        end
        save(imdbFile, '-struct', 'imdb') ;
    end

% omit lines 
% make sure I was loading the init-weights saved by main100.
Pparam = load([dataset,'_bisvmEpoch102']);

Everything else is not touched.
And this is the learning curve returned by Matlab.

dtd_vgg_16_svm_bilinear_448_net-train

Would you mind to share how to correctly use your code to train on other datasets? Thank you in advance!

LRBL accuracy issues

I see from the demo 4 that the final accuracy can't reach 84.21% as claimed in the paper?
But the caffe-20160312 reports 84.98% for the compact bilinear model already.
Could you please explain the difference?

what is the demo 1 mean?

so you did not implement the Low-Rank-Bilinear-Pooling model in the paper(cvpr 2017) using Caffe?

so,i have i problem, what is the demo 1 mean?

no SignedSqrt layer?

I haven't found the SignedSqrt layer in the version of caffe-20160312 int the caffe.proto???

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