Problems in transfering SlowFast gesture recognition model to my own task.
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When I transfered SlowFast model to my own task, some problems happened.
Firstly, I can successfully run the case titled by "Gesture Recognition using Videos and Deep Learning" provided by Matlab.
Then, I want transfer the model to my own task. I prepared my own training set as follows:
![](https://www.mathworks.com/matlabcentral/answers/uploaded_files/1347074/image.jpeg)
Compared with default 4 classes (clapping, noAction, somethingElse,wavingHello), there are 6 classes in my task(One, Two, Three, Four, Five, Snap).
Seondly, I began to train the model with my own training set. Everything is ok until the function of createMiniBatchQueue() is called. Specifically, when I run the following code section.
params.ModelFilename = "slowFastPretrained_fourClasses.mat";
if doTraining
epoch = 1;
bestLoss = realmax;
accTrain = [];
lossTrain = [];
iteration = 1;
start = tic;
trainTime = start;
shuffled = shuffleTrainDs(dsTrain);
% Number of outputs is two: One for RGB frames, and one for ground truth labels.
numOutputs = 2;
mbq = createMiniBatchQueue(shuffled, numOutputs, params);
% Use the initializeTrainingProgressPlot and initializeVerboseOutput
% supporting functions, listed at the end of the example, to initialize
% the training progress plot and verbose output to display the training
% loss, training accuracy, and validation accuracy.
plotters = initializeTrainingProgressPlot(params);
initializeVerboseOutput(params);
while iteration <= params.NumIterations
% Iterate through the data set.
[dlX1,dlY] = next(mbq);
% Evaluate the model gradients and loss using dlfeval.
[gradients,loss,acc,state] = ...
dlfeval(@modelGradients,slowFast,dlX1,dlY);
% Accumulate the loss and accuracies.
lossTrain = [lossTrain, loss];
accTrain = [accTrain, acc];
% Update the network state.
slowFast.State = state;
% Update the gradients and parameters for the video classifier
% using the SGDM optimizer.
[slowFast,params.Velocity,learnRate] = ...
updateLearnables(slowFast,gradients,params,params.Velocity,iteration);
if ~hasdata(mbq) || iteration == params.NumIterations
% Current epoch is complete. Do validation and update progress.
trainTime = toc(trainTime);
accTrain = mean(accTrain);
lossTrain = mean(lossTrain);
% Update the training progress.
displayVerboseOutputEveryEpoch(params,start,learnRate,epoch,iteration,...
accTrain,lossTrain,trainTime);
updateProgressPlot(params,plotters,epoch,iteration,start,lossTrain,accTrain);
% Save the trained video classifier and the parameters, that gave
% the best training loss so far. Use the saveData supporting function,
% listed at the end of this example.
bestLoss = saveData(slowFast,bestLoss,iteration,lossTrain,params);
end
if ~hasdata(mbq) && iteration < params.NumIterations
% Current epoch is complete. Initialize the training loss, accuracy
% values, and minibatchqueue for the next epoch.
accTrain = [];
lossTrain = [];
epoch = epoch + 1;
trainTime = tic;
shuffled = shuffleTrainDs(dsTrain);
mbq = createMiniBatchQueue(shuffled, numOutputs, params);
end
iteration = iteration + 1;
end
% Display a message when training is complete.
endVerboseOutput(params);
disp("Model saved to: " + params.ModelFilename);
end
Something happened here and I get the following response.
mbq = createMiniBatchQueue(shuffled, numOutputs, params); % error happened in this line.
![](https://www.mathworks.com/matlabcentral/answers/uploaded_files/1347079/image.jpeg)
For those familiar with this case, please provide some assistance. Thank you.
)
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