Image Classification Using Deep Learning with CNN and Keras
Image classification is a fascinating deep learning project. Specifically, image classification comes under the computer vision project category.
In this project, I have build a convolution neural network in Keras with python on a CIFAR-10 dataset. First, I had explore about dataset, and then I had train our neural network using python and Keras.
What is Image Classification
- The classification problem is to categorize all the pixels of a digital image into one of the defined classes.
- Image classification is the most critical use case in digital image analysis.
- Image classification is an application of both supervised classification and unsupervised classification.
- In supervised classification, we select samples for each target class. We train our neural network on these target class samples and then classify new samples.
- In unsupervised classification, we group the sample images into clusters of images having similar properties. Then, we classify each cluster into our intended classes.
Supervised Learning:
Basically supervised learning is when we teach or train the machine using data that is well labeled. Which means some data is already tagged with the correct answer. After that, the machine is provided with a new set of examples(data) so that the supervised learning algorithm analyses the training data(set of training examples) and produces a correct outcome from labeled data.
Unsupervised Learning:
Unsupervised learning is the training of a machine using information that is neither classified nor labeled and allowing the algorithm to act on that information without guidance. Here the task of the machine is to group unsorted information according to similarities, patterns, and differences without any prior training of data.
Convolutional Neural Networks (CNN)
Deep Learning has proved to be a very powerful tool because of its ability to handle large amounts of data. The interest to use hidden layers has surpassed traditional techniques, especially in pattern recognition. One of the most popular deep neural networks is Convolutional Neural Networks.
About Image Classification Dataset
CIFAR-10 is a very popular computer vision dataset. This dataset is well studied in many types of deep learning research for object recognition.
This dataset consists of 60,000 images divided into 10 target classes, with each category containing 6000 images of shape 32*32. This dataset contains images of low resolution (32*32), which allows researchers to try new algorithms. The 10 different classes of this dataset are:
- Airplane
- Car
- Bird
- Cat
- Deer
- Dog
- Frog
- Horse
- Ship
- Truck
Project Prerequisites:
The prerequisite to develop and execute image classification project is Keras, matplotlib and Tensorflow installation.
Steps for image classification
Summary:
The objective of the image classification project was to start working with Keras to solve real-time deep learning problems.
In this keras deep learning Project, I talked about the image classification paradigm for digital image analysis. I have Learn supervised and unsupervised image classifications. Then it explains the CIFAR-10 dataset and its classes.
