X and Y do not have the same number of observations.

setup_folders;

sadtrdinfo = dir(sad1train);  % Returns both folders and files
% sad = dir(pwd);  % Returns both folders and files
sorted_cell_array_of_train_folder_names = setdiff({sadtrdinfo([sadtrdinfo.isdir]).name},{'..','.'});  % Select folder names
% cell_array_of_train_folder_names( strncmp( cell_array_of_train_folder_names, ".", 1 ) ) = []; % Remove '.' and '..'
%sorted_cell_array_of_train_folder_names = sort_nat( cell_array_of_train_folder_names );
% sorted_cell_array_of_train_folder_names = cell_array_of_train_folder_names; % if you don't have sort_nat
whos sorted_cell_array_of_train_folder_names
%----------------

np = numel(sorted_cell_array_of_train_folder_names); % number of subfolders
train_cats = categorical(sorted_cell_array_of_train_folder_names);

Group_Train1 = cell(np,1);

for i = 1 : np
	SFN = sorted_cell_array_of_train_folder_names{i};% extract his name
    traintifpattern = fullfile(sad1train, SFN, tiffwild);
	tifListdinfo = dir(traintifpattern); % list all jpg files
    tifList = fullfile({tifListdinfo.folder}, {tifListdinfo.name});
	
    ms1 = numel(tifList); % ms = number of image files found
    Group_Train1{i} = repmat(train_cats(i), ms1, 1);
end

Group_Train1 = vertcat(Group_Train1{:});

save('Group_Train','Group_Train1');

>> HOG2

Error using classreg.learning.FullClassificationRegressionModel.prepareDataCR (line 231)

X and Y do not have the same number of observations.

Error in ClassificationSVM.prepareData (line 632)

classreg.learning.FullClassificationRegressionModel.prepareDataCR(...

Error in classreg.learning.FitTemplate/fit (line 217)

this.PrepareData(X,Y,this.BaseFitObjectArgs{:});

Error in ClassificationSVM.fit (line 240)

this = fit(temp,X,Y);

Error in fitcsvm (line 334)

obj = ClassificationSVM.fit(X,Y,RemainingArgs{:});

Error in multisvm (line 29)

models{k} =

fitcsvm(TrainingSet,G1vAll,'KernelFunction','polynomial','polynomialorder',3,'Solver','ISDA','Verbose',0,'Standardize',true);

Error in HOG2 (line 37)

result1= multisvm(Feat1,Group_Train1,Feat2,Group_Test1);

>>

Note that the actual number of photos is 26770

[Walter Roberson]

We do not know how you read in the files to create Feat1 .

In particular, we do not know that you re-created both Feat1 and Group_Train1 after any change to the directory.

Answer 1

[yanqi liu]

sir， may be use libsvm toolbox can much faster

[Walter Roberson]

Unfortunately that will not solve problems with the number of samples being wrong.