Intel Image Classification

Keywords: Intel Image

Background

Intel Image Classification involves the categorization of images into different classes based on their content, using Convolutional Neural Networks (CNN). In this study, the author explores the application of CNN for classifying images from the Intel Image Classification dataset, which consists of images representing various natural scenes, such as forests, mountains, buildings, and glaciers. First the dataset is loaded using Image Data Generator library, so that all the images in each sub folder may be loaded batch-wise. The CNN model is designed with multiple layers, including convolutional layers, pooling layers, and fully connected layers. Convolutional layers employ filters to extract meaningful features from the input images, capturing local patterns and structures. Pooling layers reduce the spatial dimensions of the features, aiding in reducing computation and extracting important information. Fully connected layers combine the extracted features and perform classification based on them. During training, the model optimizes its parameters using an optimization algorithm, called Adam optimizer, and minimizes the categorical cross-entropy loss function. The categorical cross-entropy loss measures the difference between predicted and actual class labels for each image. To enhance the model's generalization and prevent overfitting, techniques such as dropout and batch normalization are employed. The performance of the trained CNN model is evaluated using metrics called accuracy and the final accuracy of the model is 74 percent.

Aim & Objectives

Aim
To Classify the Intel Image Using DL Techniques
Objective

Create an ANN model to classify Intel images, achieving high accuracy in diverse environmental scenarios.
Explore ANN architectures to improve Intel image classification, considering efficiency and real-time applicability.
Evaluate the ANN's generalization across various Intel image categories, aiming for reliable real-world deployment.

Research Methodology

ANN Technique used

Software & Tools

Jupyter, Google Collab

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