  Our review explores the comparative analysis between Convolutional Neural
Networks (CNNs) and Vision Transformers (ViTs) in the domain of image
classification, with a particular focus on clothing classification within the
e-commerce sector. Utilizing the Fashion MNIST dataset, we delve into the
unique attributes of CNNs and ViTs. While CNNs have long been the cornerstone
of image classification, ViTs introduce an innovative self-attention mechanism
enabling nuanced weighting of different input data components. Historically,
transformers have primarily been associated with Natural Language Processing
(NLP) tasks. Through a comprehensive examination of existing literature, our
aim is to unveil the distinctions between ViTs and CNNs in the context of image
classification. Our analysis meticulously scrutinizes state-of-the-art
methodologies employing both architectures, striving to identify the factors
influencing their performance. These factors encompass dataset characteristics,
image dimensions, the number of target classes, hardware infrastructure, and
the specific architectures along with their respective top results. Our key
goal is to determine the most appropriate architecture between ViT and CNN for
classifying images in the Fashion MNIST dataset within the e-commerce industry,
while taking into account specific conditions and needs. We highlight the
importance of combining these two architectures with different forms to enhance
overall performance. By uniting these architectures, we can take advantage of
their unique strengths, which may lead to more precise and reliable models for
e-commerce applications. CNNs are skilled at recognizing local patterns, while
ViTs are effective at grasping overall context, making their combination a
promising strategy for boosting image classification performance.
