How to remove data Overfitting Issue in my training model

I am working on project of facial recognisation of endangered species and I am getting mini batch accuracy of 100% , I am using AlexNet for training. The number of images of endangered species I am using is African elephant with count of 470. Even after do argumantation mini batch accuracy is still same. Help me to remove this overfitting.

Dataset Link: Dataset

Data Training Code:

net = alexnet;

layers = [imageInputLayer([227 227 3])

net(2:end-3)

fullyConnectedLayer(1)

softmaxLayer

classificationLayer()

];

opt = trainingOptions('sgdm', 'MaxEpochs', 100, 'InitialLearnRate', 0.0001,'Plots','training-progress');

training = trainNetwork(trainData,layers,opt);

Answer 1

[Shreshth]

Hi Naitik,

I found similar question in the community -

https://www.mathworks.com/matlabcentral/answers/1950373-overfitting-deep-neural-network

Also, refer to the following MathWorks documentation for more information on how to avoid overfitting:

https://www.mathworks.com/discovery/overfitting.html

Hope this helps!