MBInception: A new Multi-Block Inception Model for Enhancing Image Processing Efficiency

TL;DR

This paper presents MBInception, a novel multi-block inception model that improves image classification efficiency by outperforming existing architectures across multiple benchmark datasets.

Contribution

Introduction of MBInception, a new multi-block inception architecture that enhances image classification performance and efficiency compared to established models.

Findings

MBInception outperforms VG, ResNet, and MobileNet on benchmark datasets.

The model demonstrates higher accuracy and efficiency in image classification tasks.

Evaluation confirms the model's superiority across diverse datasets.

Abstract

Deep learning models, specifically convolutional neural networks, have transformed the landscape of image classification by autonomously extracting features directly from raw pixel data. This article introduces an innovative image classification model that employs three consecutive inception blocks within a convolutional neural networks framework, providing a comprehensive comparative analysis with well-established architectures such as Visual Geometry Group, Residual Network, and MobileNet. Through the utilization of benchmark datasets, including Canadian Institute for Advanced Researc, Modified National Institute of Standards and Technology database, and Fashion Modified National Institute of Standards and Technology database, we assess the performance of our proposed model in comparison to these benchmarks. The outcomes reveal that our novel model consistently outperforms its counterparts across diverse datasets, underscoring its effectiveness and potential for advancing the current state-of-the-art in image classification. Evaluation metrics further emphasize that the proposed model surpasses the other compared architectures, thereby enhancing the efficiency of image classification on standard datasets.