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.

翻译：深度学习模型，特别是卷积神经网络，通过直接从原始像素数据中自主提取特征，彻底改变了图像分类的领域。本文介绍了一种创新的图像分类模型，该模型在卷积神经网络框架内采用了三个连续的Inception模块，并与Visual Geometry Group、Residual Network和MobileNet等成熟架构进行了全面的对比分析。通过使用包括加拿大高级研究所数据集、改进型国家标准与技术研究院数据库以及时尚改进型国家标准与技术研究院数据库在内的基准数据集，我们评估了所提模型相对于这些基准模型的性能。结果表明，我们提出的新模型在不同数据集上均持续优于对比模型，突显了其有效性及其在推进当前图像分类技术前沿方面的潜力。评估指标进一步强调，所提出的模型超越了其他对比架构，从而提升了在标准数据集上进行图像分类的效率。