Image Classification is a key area of Artificial Intelligence that deals with classifying objects in an image into one of multiple categories. It can be used in many applications such as Automatic Number Plate Recognition (ANPR) systems, self-driving cars, drones, robots, and more.
To perform Image Classification, there are various Machine Learning algorithms that can be used. One of the most popular approaches is to use Convolutional Neural Networks (CNNs), where networks learn hierarchical representations of images, and use these representations to classify the objects.
In this blog post, we will discuss how to implement Image Classification using a Convolutional Neural Network (CNN) in Python. We will start by exploring the theory behind CNNs, and then move on to training a CNN to classify images from the CIFAR-10 dataset.
Convolutional Neural Networks (CNNs) are deep neural networks that use a hierarchy of layers to learn sophisticated representations of images, and classify them into categories. At the lowest layer of the network, individual neurons are trained to detect basic features in an image- such as shapes, textures, and lines. Gradually, higher layers of the network identify more complex features such as eyes, nose, and face parts, and finally, the network is ready to classify the entire image as belonging to a particular category.
CNNs achieve such high accuracy by being able to learn latent representations of images that are not available to humans. For example, a human may be able to tell that a particular image shows a person, but a CNN can learn to tell that it is a particular person, and perhaps even the name of the person.
Now that we understand the basics of CNNs and the theory behind them, let’s move on to the practical application of training a CNN to classify images from the CIFAR-10 dataset. We will be using the TensorFlow 2.0 library for our CNN implementation.
First, we will import the required libraries and define our model. We will start by defining a Sequential model:
import tensorflow as tf from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dropout, Dense # define the model model = Sequential()
Next, we will add the Convolutional layers to the model. We will start with a Conv2D layer which will take our input images as input, and will transform them using 32 3x3 kernels. This can be done using the Conv2D layer of TensorFlow:
# add convolutional layers model.add(Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=(32, 32, 3)))
We will also add a MaxPooling2D layer for downsampling. This layer will take the input from the Conv2D layer and half the size of the image in each dimension. This will reduce the size of the image and make it more computationally efficient to process. This can be done by adding a MaxPooling2D layer to the model:
# add max pooling layer model.add(MaxPooling2D(pool_size=(2, 2)))
Then, we will add a few more Conv2D and MaxPooling2D layers to the model, followed by a Flatten layer. The Flatten layer will take the output of the previous layers and convert it into a one-dimensional vector, which can then be fed into a simple neural network. This can be done by adding the following layers to the model:
# add more convolutional and max pooling layers model.add(Conv2D(64, (3, 3), activation='relu')) model.add(MaxPooling2D(pool_size=(2, 2))) # add flattening layer model.add(Flatten())
Finally, we will add a fully connected neural network to the model, followed by the output layer. This can be done by adding a few Dense layers and a Dropout layer to the model. The Dropout layer is added to prevent overfitting, and the Dense layers will contain the neurons which will be used to classify our input images into the 10 separate classes of the CIFAR-10 dataset. This can be done by adding the following layers to the model:
# add dense layers model.add(Dense(128, activation='relu')) model.add(Dropout(0.2)) model.add(Dense(10, activation='softmax'))
Now that our model is defined, we can compile it and start training it on our dataset. To compile the model, we will use the standard model.compile function. We will use the Adam optimizer, and will specify the metrics that we want to track as ‘accuracy’. This can be done using the following code:
# compile the model model.compile(optimizer='Adam', loss='categorical_crossentropy', metrics=['accuracy'])
Finally, we will train the model. Once the model is compiled, we can use the fit function to start the training process. During training, we will specify the batch size, number of epochs, and the validation split. This can be done using the following code:
# train the model model.fit(X_train, y_train, batch_size=64, epochs=10, validation_split=0.2)
Once the model has finished training, we can use it to classify images from the CIFAR-10 dataset. To do this, we will first need to predict the labels for each of our input images, which can be done using the predict function. We will then compare the predicted labels to the actual labels, and calculate the accuracy.
By implementing Image Classification using a Convolutional Neural Network (CNN) in Python, we can achieve very high accuracy in classifying images from the CIFAR-10 dataset. The CNN will learn sophisticated patterns from the images, and use these patterns to accurately classify the input images.