Classify images with alexnet into 2 classes and calculate performance

Hi everyone ,I want to use alexnet to classify my image dataset into 2 classes and evaluate the performances (Accuracy, Sensitivity, Sensibiliity...) using the confusion matrix after the classification.I am beginner in matlab can anyone post a guide or code wich i can follow it. and Thanks.

Answer 1

[Gagan Agarwal]

Hi His

You can refer to the following steps to classify the image dataset into 2 classes and evaluating the model's performance

1. Loading and Preprocessing the Data: Begin by loading your dataset and conducting necessary preprocessing. Ensure that each data point is labeled with a result field indicating one of the two desired classes.
2. Splitting the Data into Training and Testing Sets: Divide your dataset into two parts: one for training the model (training set) and the other for evaluating the model's performance (testing set).
3. Preparing AlexNet for Binary Classification: Load AlexNet and adjust it as needed to make it suitable for binary classification tasks.
4. Specifying Training Options and Training the Network: Choose appropriate training options for your model. Proceed to train the network using the training set prepared in the previous steps.
5. Evaluating the Network: After training, evaluate the performance of your network using the testing set. For calculating 'accuracy', you may refer to below documentation.

https://www.mathworks.com/help/deeplearning/ug/create-simple-deep-learning-network-for-classification.html#TrainABasicConvolutionalNeuralNetworkForClassificationExample-6

I hope it helps!