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Black and white image colorization with OpenCV and Deep Learning

CIFAR-10 Image Classification in TensorFlow

Last Updated : 20 Sep, 2024

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Prerequisites:

Image Classification
Convolution Neural Networks including basic pooling, convolution layers with normalization in neural networks, and dropout.
Data Augmentation.
Neural Networks.
Numpy arrays.

In this article, we are going to discuss how to classify images using TensorFlow. Image Classification is a method to classify the images into their respective category classes. CIFAR-10 Dataset as it suggests has 10 different categories of images in it. There is a total of 60000 images of 10 different classes naming Airplane, Automobile, Bird, Cat, Deer, Dog, Frog, Horse, Ship, Truck. All the images are of size 32x32. There are in total 50000 train images and 10000 test images.

To build an image classifier we make use of tensorflow' s keras API to build our model. In order to build a model, it is recommended to have GPU support, or you may use the Google colab notebooks as well.

Stepwise Implementation:

The first step towards writing any code is to import all the required libraries and modules. This includes importing tensorflow and other modules like numpy. If the module is not present then you can download it using pip install tensorflow on the command prompt (for windows) or if you are using a jupyter notebook then simply type !pip install tensorflow in the cell and run it in order to download the module. Other modules can be imported similarly.

Python

import tensorflow as tf   

# Display the version
print(tf.__version__)     

# other imports
import numpy as np
import matplotlib.pyplot as plt
from tensorflow.keras.layers import Input, Conv2D, Dense, Flatten, Dropout
from tensorflow.keras.layers import GlobalMaxPooling2D, MaxPooling2D
from tensorflow.keras.layers import BatchNormalization
from tensorflow.keras.models import Model

Output:

2.4.1

The output of the above code should display the version of tensorflow you are using eg 2.4.1 or any other.

Now we have the required module support so let's load in our data. The dataset of CIFAR-10 is available on tensorflow keras API, and we can download it on our local machine using tensorflow.keras.datasets.cifar10 and then distribute it to train and test set using load_data() function.

Python

# Load in the data
cifar10 = tf.keras.datasets.cifar10

# Distribute it to train and test set
(x_train, y_train), (x_test, y_test) = cifar10.load_data()
print(x_train.shape, y_train.shape, x_test.shape, y_test.shape)

Output:

The output of the above code will display the shape of all four partitions and will look something like this

Here we can see we have 5000 training images and 1000 test images as specified above and all the images are of 32 by 32 size and have 3 color channels i.e. images are color images. As well as it is also visible that there is only a single label assigned with each image.

Until now, we have our data with us. But still, we cannot be sent it directly to our neural network. We need to process the data in order to send it to the network. The first thing in the process is to reduce the pixel values. Currently, all the image pixels are in a range from 1-256, and we need to reduce those values to a value ranging between 0 and 1. This enables our model to easily track trends and efficient training. We can do this simply by dividing all pixel values by 255.0.

Another thing we want to do is to flatten(in simple words rearrange them in form of a row) the label values using the flatten() function.

Python

# Reduce pixel values
x_train, x_test = x_train / 255.0, x_test / 255.0

# flatten the label values
y_train, y_test = y_train.flatten(), y_test.flatten()

Now is a good time to see few images of our dataset. We can visualize it in a subplot grid form. Since the image size is just 32x32 so don't expect much from the image. It would be a blurred one. We can do the visualization using the subplot() function from matplotlib and looping over the first 25 images from our training dataset portion.

Python

# visualize data by plotting images
fig, ax = plt.subplots(5, 5)
k = 0

for i in range(5):
    for j in range(5):
        ax[i][j].imshow(x_train[k], aspect='auto')
        k += 1

plt.show()

Output:

Though the images are not clear there are enough pixels for us to specify which object is there in those images.

After completing all the steps now is the time to built our model. We are going to use a Convolution Neural Network or CNN to train our model. It includes using a convolution layer in this which is Conv2d layer as well as pooling and normalization methods. Finally, we'll pass it into a dense layer and the final dense layer which is our output layer. We are using 'relu' activation function. The output layer uses a "softmax" function.

Python

# number of classes
K = len(set(y_train))

# calculate total number of classes 
# for output layer
print("number of classes:", K)

# Build the model using the functional API
# input layer
i = Input(shape=x_train[0].shape)
x = Conv2D(32, (3, 3), activation='relu', padding='same')(i)
x = BatchNormalization()(x)
x = Conv2D(32, (3, 3), activation='relu', padding='same')(x)
x = BatchNormalization()(x)
x = MaxPooling2D((2, 2))(x)

x = Conv2D(64, (3, 3), activation='relu', padding='same')(x)
x = BatchNormalization()(x)
x = Conv2D(64, (3, 3), activation='relu', padding='same')(x)
x = BatchNormalization()(x)
x = MaxPooling2D((2, 2))(x)

x = Conv2D(128, (3, 3), activation='relu', padding='same')(x)
x = BatchNormalization()(x)
x = Conv2D(128, (3, 3), activation='relu', padding='same')(x)
x = BatchNormalization()(x)
x = MaxPooling2D((2, 2))(x)

x = Flatten()(x)
x = Dropout(0.2)(x)

# Hidden layer
x = Dense(1024, activation='relu')(x)
x = Dropout(0.2)(x)

# last hidden layer i.e.. output layer
x = Dense(K, activation='softmax')(x)

model = Model(i, x)

# model description
model.summary()

Output:

Our model is now ready, it's time to compile it. We are using model.compile() function to compile our model. For the parameters, we are using

adam optimizer
sparse_categorical_crossentropy as the loss function
metrics=['accuracy']

Python

# Compile
model.compile(optimizer='adam',
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy'])

Now let's fit our model using model.fit() passing all our data to it. We are going to train our model till 50 epochs, it gives us a fair result though you can tweak it if you want.

Python

# Fit
r = model.fit(
  x_train, y_train, validation_data=(x_test, y_test), epochs=50)

Output:

The model will start training, and it will look something like this

After this, our model is trained. Though it will work fine but to make our model much more accurate we can add data augmentation on our data and then train it again. Calling model.fit() again on augmented data will continue training where it left off. We are going to fir our data on a batch size of 32 and we are going to shift the range of width and height by 0.1 and flip the images horizontally. Then call model.fit again for 50 epochs.

Python

# Fit with data augmentation
# Note: if you run this AFTER calling
# the previous model.fit()
# it will CONTINUE training where it left off
batch_size = 32
data_generator = tf.keras.preprocessing.image.ImageDataGenerator(
  width_shift_range=0.1, height_shift_range=0.1, horizontal_flip=True)

train_generator = data_generator.flow(x_train, y_train, batch_size)
steps_per_epoch = x_train.shape[0] // batch_size

r = model.fit(train_generator, validation_data=(x_test, y_test),
              steps_per_epoch=steps_per_epoch, epochs=50)

Output:

The model will start training for 50 epochs. Though it is running on GPU it will take at least 10 to 15 minutes.

Now we have trained our model, before making any predictions from it let's visualize the accuracy per iteration for better analysis. Though there are other methods that include confusion matrix for better analysis of the model.

Python

# Plot accuracy per iteration
plt.plot(r.history['accuracy'], label='acc', color='red')
plt.plot(r.history['val_accuracy'], label='val_acc', color='green')
plt.legend()

Output:

download — Plotting accuracy per iteration

Let's make a prediction over an image from our model using model.predict() function. Before sending the image to our model we need to again reduce the pixel values between 0 and 1 and change its shape to (1,32,32,3) as our model expects the input to be in this form only. To make things easy let us take an image from the dataset itself. It is already in reduced pixels format still we have to reshape it (1,32,32,3) using reshape() function. Since we are using data from the dataset we can compare the predicted output and original output.

Python

# label mapping

labels = '''airplane automobile bird cat deerdog frog horseship truck'''.split()

# select the image from our test dataset
image_number = 0

# display the image
plt.imshow(x_test[image_number])

# load the image in an array
n = np.array(x_test[image_number])

# reshape it
p = n.reshape(1, 32, 32, 3)

# pass in the network for prediction and 
# save the predicted label
predicted_label = labels[model.predict(p).argmax()]

# load the original label
original_label = labels[y_test[image_number]]

# display the result
print("Original label is {} and predicted label is {}".format(
    original_label, predicted_label))

Output:

1/1 ━━━━━━━━━━━━━━━━━━━━ 1s 928ms/step
Original label is cat and predicted label is cat

Now we have the output as Original label is cat and the predicted label is also cat.

Let's check it for some label which was misclassified by our model, e.g. for image number 5722 we receive something like this:

Finally, let's save our model using model.save() function as an h5 file. If you are using Google colab you can download your model from the files section.

Python

# save the model
model.save('geeksforgeeks.h5')

Hence, in this way, one can classify images using Tensorflow.

Get the complete notebook and dataset link here:

Notebook link : click here.
Dataset Link: click here

Black and white image colorization with OpenCV and Deep Learning

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Article Tags :

Deep Learning
AI-ML-DS
Image-Processing
Tensorflow

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A Django Based Web Application built for the purpose of detecting the presence of COVID-19 from Chest X-Ray images with multiple machine learning models trained on pre-built architectures. Three different machine learning models were used to build this project namely Xception, ResNet50, and VGG16. T

Image Segmentation Using TensorFlow

Image segmentation refers to the task of annotating a single class to different groups of pixels. While the input is an image, the output is a mask that draws the region of the shape in that image. Image segmentation has wide applications in domains such as medical image analysis, self-driving cars,

License Plate Recognition with OpenCV and Tesseract OCR

License Plate Recognition is widely used for automated identification of vehicle registration plates for security purpose and law enforcement. By combining computer vision techniques with Optical Character Recognition (OCR) we can extract license plate numbers from images enabling applications in ar

Detect and Recognize Car License Plate from a video in real time

Recognizing a Car License Plate is a very important task for a camera surveillance-based security system. We can extract the license plate from an image using some computer vision techniques and then we can use Optical Character Recognition to recognize the license number. Here I will guide you thro

Residual Networks (ResNet) - Deep Learning

After the first CNN-based architecture (AlexNet) that win the ImageNet 2012 competition, Every subsequent winning architecture uses more layers in a deep neural network to reduce the error rate. This works for less number of layers, but when we increase the number of layers, there is a common proble

Natural Language Processing Projects

Twitter Sentiment Analysis using Python

This article covers the sentiment analysis of any topic by parsing the tweets fetched from Twitter using Python. What is sentiment analysis? Sentiment Analysis is the process of 'computationally' determining whether a piece of writing is positive, negative or neutral. Itâ€™s also known as opinion mini

Facebook Sentiment Analysis using python

This article is a Facebook sentiment analysis using Vader, nowadays many government institutions and companies need to know their customers' feedback and comment on social media such as Facebook. What is sentiment analysis? Sentiment analysis is one of the best modern branches of machine learning, w

Next Sentence Prediction using BERT

Next Sentence Prediction is a pre-training task used in BERT to help the model understand the relationship between different sentences. It is widely used for tasks like question answering, summarization and dialogue systems. The goal is to determine whether a given second sentence logically follows

Hate Speech Detection using Deep Learning

There must be times when you have come across some social media post whose main aim is to spread hate and controversies or use abusive language on social media platforms. As the post consists of textual information to filter out such Hate Speeches NLP comes in handy. This is one of the main applicat

Image Caption Generator using Deep Learning on Flickr8K dataset

Generating a caption for a given image is a challenging problem in the deep learning domain. In this article we will use different computer vision and NLP techniques to recognize the context of an image and describe them in a natural language like English. We will build a working model of the image

Movie recommendation based on emotion in Python

Movies that effectively portray and explore emotions resonate deeply with audiences because they tap into our own emotional experiences and vulnerabilities. A well-crafted emotional movie can evoke empathy, understanding, and self-reflection, allowing viewers to connect with the characters and their

Speech Recognition in Python using Google Speech API

Speech recognition means converting spoken words into text. It used in various artificial intelligence applications such as home automation, speech to text, etc. In this article, youâ€™ll learn how to do basic speech recognition in Python using the Google Speech Recognition API.Step 1: Install Require

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Speech recognition is the process of turning spoken words into text. It is a key part of any voice assistant. In Python the SpeechRecognition module helps us do this by capturing audio and converting it to text. In this guide weâ€™ll create a basic voice assistant using Python.Step 1: Install Required

Human Activity Recognition - Using Deep Learning Model

Human activity recognition using smartphone sensors like accelerometer is one of the hectic topics of research. HAR is one of the time series classification problem. In this project various machine learning and deep learning models have been worked out to get the best final result. In the same seque

Fine-tuning BERT model for Sentiment Analysis

Google created a transformer-based machine learning approach for natural language processing pre-training called Bidirectional Encoder Representations from Transformers. It has a huge number of parameters, hence training it on a small dataset would lead to overfitting. This is why we use a pre-train

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BERT stands for Bidirectional Representation for Transformers and was proposed by researchers at Google AI language in 2018. Although the main aim of that was to improve the understanding of the meaning of queries related to Google Search, BERT becomes one of the most important and complete architec

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Recurrent Neural Networks (RNNs) are used in sequence tasks such as sentiment analysis due to their ability to capture context from sequential data. In this article we will be apply RNNs to analyze the sentiment of customer reviews from Swiggy food delivery platform. The goal is to classify reviews

Building an Autocorrector Using NLP in Python

Autocorrect feature predicts and correct misspelled words, it helps to save time invested in the editing of articles, emails and reports. This feature is added many websites and social media platforms to ensure easy typing. In this tutorial we will build a Python-based autocorrection feature using N

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Natural language processing (NLP) is an area of computer science and artificial intelligence concerned with the interactions between computers and human (natural) languages, in particular how to program computers to process and analyze large amounts of natural language data. It is the branch of mach

Restaurant Review Analysis Using NLP and SQLite

Normally, a lot of businesses are remained as failures due to lack of profit, lack of proper improvement measures. Mostly, restaurant owners face a lot of difficulties to improve their productivity. This project really helps those who want to increase their productivity, which in turn increases thei

Twitter Sentiment Analysis using Python

This article covers the sentiment analysis of any topic by parsing the tweets fetched from Twitter using Python. What is sentiment analysis? Sentiment Analysis is the process of 'computationally' determining whether a piece of writing is positive, negative or neutral. Itâ€™s also known as opinion mini

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Customer Segmentation using Unsupervised Machine Learning in Python

Customer Segmentation involves grouping customers based on shared characteristics, behaviors and preferences. By segmenting customers, businesses can tailor their strategies and target specific groups more effectively and enhance overall market value. Today we will use Unsupervised Machine Learning

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When did we see a video on youtube let's say it was funny then the next time you open your youtube app you get recommendations of some funny videos in your feed ever thought about how? This is nothing but an application of Machine Learning using which recommender systems are built to provide persona

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