A simple classifier to classify Cars and aeroplanes with CNN(Part 1)

Today we will build a simple supervised algorithm with keras to classify cars and aeroplanes.  We will implement a simple CNN(convolution Neural Network), which we will train with the dataset, after the model is generated we can easily classify the images. Here we are using only two classes, but you can classify as many classes as you want.

I am using a small dataset. For training 200 images of cars and 200 images of planes. And for testing 50 images from each class. You can use your own dataset with different classes if you want.

The dataset contains lot of information or features of the images we provide. The model learns the distinguishable features from the data-set in the training process. With that information we can classify the images.  So let’s get started.

We will divide this tutorial in two parts, in part1 we will learn how to train the data-set and generate the model file and in part2 we will use this model file to do inference and real classification.

Step 1: Preparing Data-set

You can download the data from my github here: gitHub

Once you have the data-set we need to organize our data before we start actual training code. Below image shows the structure of folders for the data.

Photos of Cars:

Photos of Planes:

Step 2: Installing required Packages

• -          Tensorflow > 1.13
• -          Numpy
• -          Keras

Step 3: Implementation 

Frist we will import the required libraries 

Read the data-set

Initialize the CNN and writing the layer… we will have one convolution layer followed by an 

activation function and a pooling. And we will repeat the same.

Flattering, dense layer, dropouts and activation at the end.

Compiling the CNN we shall use the ‘rmsprop’ optimisation method, binary cross entropy loss function

Now we have feed the images to the CNN we just created

Finally the classifier, model will be saves as ‘model.h5'

If you run the above code the result should look something like this-

  After 10 epoch is done the model will be save with an accuracy of 96%.

You can download the whole code from my git repository here: gitHub

Stay tuned for the inference part. Do share your feedback in the comment section. See you soon. Regards.