Why when I train my detector in one go do I get better results than when I train it on the same images but the only difference is that I use checkpoint?
I use that code to create the detector:
inputLayer = imageInputLayer([128 128 3],'Name','Input','Normalization','none');
filterSize = [3 3];
middleLayers = [
convolution2dLayer(filterSize, 16, 'Padding', 1, 'name','conv_1',...
'WeightsInitializer', 'narrow-normal')
batchNormalizationLayer('Name','BN1')
reluLayer('Name','relu_1')
maxPooling2dLayer(2, 'stride',2,'Name','maxpool1')
convolution2dLayer(filterSize, 32, 'Padding', 1, 'name','conv_2',...
'WeightsInitializer', 'narrow-normal')
batchNormalizationLayer('Name','BN2')
reluLayer('Name','relu_2')
maxPooling2dLayer(2, 'stride',2,'Name','maxpool2')
convolution2dLayer(filterSize, 64, 'Padding', 1, 'name','conv_3',...
'WeightsInitializer', 'narrow-normal')
batchNormalizationLayer('Name','BN3')
reluLayer('Name','relu_3')
maxPooling2dLayer(2, 'stride',2,'Name','maxpool3')
convolution2dLayer(filterSize, 128, 'Padding', 1, 'name','conv_4',...
'WeightsInitializer', 'narrow-normal')
batchNormalizationLayer('Name','BN4')
reluLayer('Name','relu_4')
];
Igraph = layerGraph([inputLayer; middleLayers]);
numClasses = size(trainingDataTable,2)-1;
edit codeFiles\AnchorBoxes.m;
Anchors = [43 59
18 22
23 29
84 109];
Igraph = yolov2Layers([128 128 3],numClasses,Anchors,Igraph,'relu_4');
options = trainingOptions('sgdm', ...
'InitialLearnRate', 0.001, ...
'Verbose',true, 'MiniBatchSize',16,'MaxEpochs',30,...
'Shuffle','every-epoch', 'verboseFrequency',30, ...
'ExecutionEnvironment','parallel');
and when I train from checkpoint I use this code:
data = load('/checkpath/yolov2_checkpoint__216__2022_7_15__13_34_30.mat');
checkpoint = data.detector;
