A simple classifier to classify Cars and aeroplanes with CNN(Part 2: inference)

Hello there, hope you are doing well. This is a sequential post of classifier with CNN. In our earlier post we learned how collect the data, organize them and train a model for classification. In this post we will learn how we can use the trained model and actually classify the Cars and Planes. When I was starting to train a CNN and learn, I had a difficult time to learn how to use the model and actually see the result. All the article or blogs I was following only talks about how to train the network but no one was actually talking about how we can see the classification results. Enough talking lets start :

If you followed my previous post, the model file(model.h5) was created with 96% accuracy and save in the models folder. Now we will use that model for inference. 

Step1: We will start by importing the required libraries as we did for the training.

Step2: In the test.py code we will specify where the model and the test images are. We will load the model and the weights. Specify the image size we dealing with.

Step3: Now we will define a function for prediction which will take the test image as input and return the prediction output accordingly. As we have only two classes(cars and areoplanes), we will get the probability of two classes as output. We will read that probability and show the output result.
You can clone the whole project from github here. Do let me know if you have any feedback or suggestions. Hope you enjoyed coding with me. Wish you all a very happy new year 2020 in advace.

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.