Image Classification Using CNN | Step-wise Tutorial

Image classification using Convolutional Neural Networks (CNN) has revolutionized computer vision tasks by enabling automated and accurate recognition of objects within images. CNN-based image classification algorithms have gained immense popularity due to their ability to learn and extract intricate features from raw image data automatically. This article will explore the principles, techniques, and applications of image classification using CNNs. We will delve into the architecture, training process, and CNN image classification evaluation metrics. By understanding the workings of CNNs for image classification, we can unlock many possibilities for object recognition, scene understanding, and visual data analysis.

This article was published as a part of the Data Science Blogathon.

Why CNN for Image Classification?

Image classification using CNN involves the extraction of features from the image to observe some patterns in the dataset. Using an ANN for the purpose of image classification would end up being very costly in terms of computation since the trainable parameters become extremely large.

For example, if we have a 50 X 50 image of a cat, and we want to train our traditional ANN on that image to classify it into a dog or a cat the trainable parameters become –
(50*50) * 100 image pixels multiplied by hidden layer + 100 bias + 2 * 100 output neurons + 2 bias = 2,50,302

We use filters when using CNNs. Filters exist of many different types according to their purpose.

Filters help us exploit the spatial locality of a particular image by enforcing a local connectivity pattern between neurons.

Convolution basically means a pointwise multiplication of two functions to produce a third function. Here one function is our image pixels matrix and another is our filter. We slide the filter over the image and get the dot product of the two matrices. The resulting matrix is called an “Activation Map” or “Feature Map”.

How Are CNN Used Image Classification?

Image classification involves assigning labels or classes to input images. It is a supervised learning task where a model is trained on labeled image data to predict the class of unseen images. CNN are commonly used for image classification as they can learn hierarchical features like edges, textures, and shapes, enabling accurate object recognition in images. CNNs excel in this task because they can automatically extract meaningful spatial features from images. Here are different layers involved in the process:

Input Layer

The input layer of a CNN takes in the raw image data as input. The images are typically represented as matrices of pixel values. The dimensions of the input layer correspond to the size of the input images (e.g., height, width, and color channels).

Convolutional Layers

Convolutional layers are responsible for feature extraction. They consist of filters (also known as kernels) that are convolved with the input images to capture relevant patterns and features. These layers learn to detect edges, textures, shapes, and other important visual elements.

Pooling Layers

Pooling layers reduce the spatial dimensions of the feature maps produced by the convolutional layers. They perform downsampling operations (e.g., max pooling) to retain the most salient information while discarding unnecessary details. This helps in achieving translation invariance and reducing computational complexity.

Fully Connected Layers

The output of the last pooling layer is flattened and connected to one or more fully connected layers. These layers function as traditional neural network layers and classify the extracted features. The fully connected layers learn complex relationships between features and output class probabilities or predictions.

Output Layer

The output layer represents the final layer of the CNN. It consists of neurons equal to the number of distinct classes in the classification task. The output layer provides each class’s classification probabilities or predictions, indicating the likelihood of the input image belonging to a particular class.

Tutorial: CNN Image Classification with Keras and CIFAR-10

I will be working on Google Colab and I have connected the dataset through Google Drive, so the code provided by me should work if the same setup is being used. Remember to make appropriate changes according to your setup.

Step 1: Choose a Dataset

Choose a dataset of your interest or you can also create your own image dataset for solving your own image classification problem. An easy place to choose a dataset is on kaggle.com.

The dataset I’m going with can be found here.

This dataset contains 12,500 augmented images of blood cells (JPEG) with accompanying cell type labels (CSV). There are approximately 3,000 images for each of 4 different cell types grouped into 4 different folders (according to cell type). The cell types are Eosinophil, Lymphocyte, Monocyte, and Neutrophil.

Here are all the libraries that we would require and the code for importing them:

from keras.models import Sequential

import tensorflow as tf

import tensorflow_datasets as tfds

tf.enable_eager_execution()

from keras.layers.core import Dense, Activation, Dropout, Flatten
from keras.layers.convolutional import Convolution2D, MaxPooling2D
from keras.optimizers import SGD, RMSprop, adam
from keras.utils import np_utils
from sklearn.tree import DecisionTreeClassifier # Import Decision Tree Classifier
from sklearn import metricsfrom sklearn.utils import shuffle
from sklearn.model_selection import train_test_splitimport matplotlib.image as mpimg
import matplotlib.pyplot as plt
import numpy as np
import os
import cv2
import randomfrom numpy import *
from PIL import Image
import theano

Step 2: Prepare Dataset for Training

Preparing our dataset for training will involve assigning paths and creating categories(labels), resizing our images.

Resizing images into 200 X 200

path_test = "/content/drive/My Drive/semester 5 - ai ml/datasetHomeAssign/TRAIN"

CATEGORIES = ["EOSINOPHIL", "LYMPHOCYTE", "MONOCYTE", "NEUTROPHIL"]
print(img_array.shape)IMG_SIZE =200
new_array = cv2.resize(img_array, (IMG_SIZE, IMG_SIZE))

Step 3: Create Training Data

Training is an array that will contain image pixel values and the index at which the image in the CATEGORIES list.

training = []def createTrainingData():
  for category in CATEGORIES:
    path = os.path.join(path_test, category)
    class_num = CATEGORIES.index(category)
    for img in os.listdir(path):
      img_array = cv2.imread(os.path.join(path,img))
      new_array = cv2.resize(img_array, (IMG_SIZE, IMG_SIZE))
      training.append([new_array, class_num])createTrainingData()

Step 4: Shuffle the Dataset

random.shuffle(training)

Step 5: Assigning Labels and Features

This shape of both the lists will be used in Classification using the NEURAL NETWORKS.

X =[]
y =[]for features, label in training:
  X.append(features)
  y.append(label)
X = np.array(X).reshape(-1, IMG_SIZE, IMG_SIZE, 3)

Step 6: Normalising X and Converting Labels to Categorical Data

X = X.astype('float32')
X /= 255
from keras.utils import np_utils
Y = np_utils.to_categorical(y, 4)
print(Y[100])
print(shape(Y))

Step 7: Split X and Y for Use in CNN

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 4)

Step 8: Define, Compile and Train the CNN Model

batch_size = 16
nb_classes =4
nb_epochs = 5
img_rows, img_columns = 200, 200
img_channel = 3
nb_filters = 32
nb_pool = 2
nb_conv = 3

model = tf.keras.Sequential([
    tf.keras.layers.Conv2D(32, (3,3), padding='same', activation=tf.nn.relu,
                           input_shape=(200, 200, 3)),
    tf.keras.layers.MaxPooling2D((2, 2), strides=2),
    tf.keras.layers.Conv2D(32, (3,3), padding='same', activation=tf.nn.relu),
    tf.keras.layers.MaxPooling2D((2, 2), strides=2),
    tf.keras.layers.Dropout(0.5),
    tf.keras.layers.Flatten(),
    tf.keras.layers.Dense(128, activation=tf.nn.relu),
    tf.keras.layers.Dense(4,  activation=tf.nn.softmax)
])
model.compile(optimizer='adam',loss='sparse_categorical_crossentropy',metrics=['accuracy'])
model.fit(X_train, y_train, batch_size = batch_size, epochs = nb_epochs, verbose = 1, validation_data = (X_test, y_test))

Step 9: Accuracy and Score of Model

score = model.evaluate(X_test, y_test, verbose = 0 )
print("Test Score: ", score[0])
print("Test accuracy: ", score[1])

In these 9 simple steps, you would be ready to train your own Convolutional Neural Networks model and solve real-world problems using these skills. You can practice these skills on platforms like Analytics Vidhya and Kaggle. You can also play around by changing different parameters and discovering how you would get the best accuracy and score. Try changing the batch_size, the number of epochs or even adding/removing layers in the CNN model, and have fun!

Also Read: 20 Questions to Test your CNN Skills

Conclusion

In conclusion, image classification using CNN has revolutionized the field of computer vision, enabling accurate recognition of objects within images. With its ability to automatically learn and extract complex features, CNNs have become a powerful tool for various applications. To further enhance your understanding and skills in image classification using CNN and other advanced data science techniques, consider enrolling in our Blackbelt Program. This comprehensive program offers in-depth knowledge and practical experience, empowering you to become a proficient data scientist. Ready to take the next step? Explore the possibilities of our Blackbelt Program today!

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