Performance comparison of deep learning-based image classification algorithms on ImageNet

The rapid evolution of Artificial Intelligence (AI) technology has propelled image recognition to the forefront of computational advancements. Since the inception of Convolutional Neural Networks (CNNs), the field has expanded into a multitude of sophisticated models and their derivatives, each tailored to address specific challenges and applications. Image recognition's landscape encompasses foundational tasks such as object and face detection, extending to more specialized applications like emotion analysis, optical character recognition, and complex interpretation of biological imagery. This domain's historical perspectives trace back to models like AlexNet, which set benchmarks with accuracy rates of around 70%. Fast forward to contemporary times, and advanced algorithms consistently achieve accuracy figures beyond the 90% threshold on benchmark datasets like ImageNet. Moreover, the diversification of AI applications has led to the development of models like MobileNet, which are intricately designed for streamlined efficiency on mobile devices, balancing performance with resource constraints. This discourse will navigate the intricate maze of image recognition, primarily leveraging insights from the ImageNet dataset as a canonical reference. By the end of this exploration, this work will discuss several cost-efficient models. Finally, this work will also cover some complex algorithms with high accuracy. All these algorithms use different approaches and obtain good performance in either cost-efficiency or accuracy. This discourse will provide an overview of these algorithms, detailing their novelty, implementation, and experimental results for accuracy and cost-efficiency.