MBInception: A new Multi-Block Inception Model for Enhancing Image Processing Efficiency
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866917872228368384 |
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| author | Froughirad, Fatemeh Eshtivani, Reza Bakhoda Khajavi, Hamed Rastgoo, Amir |
| author_facet | Froughirad, Fatemeh Eshtivani, Reza Bakhoda Khajavi, Hamed Rastgoo, Amir |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_13703 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | MBInception: A new Multi-Block Inception Model for Enhancing Image Processing Efficiency Froughirad, Fatemeh Eshtivani, Reza Bakhoda Khajavi, Hamed Rastgoo, Amir Image and Video Processing Computer Vision and Pattern Recognition Numerical Analysis 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. |
| title | MBInception: A new Multi-Block Inception Model for Enhancing Image Processing Efficiency |
| topic | Image and Video Processing Computer Vision and Pattern Recognition Numerical Analysis |
| url | https://arxiv.org/abs/2412.13703 |