Comparative Analysis of Deep Learning Models for Brand Logo
  Classification in Real-World Scenarios
Qimao Yang Huili Chen Qiwei Dong
This report presents a comprehensive study on deep learning models for brand
logo classification in real-world scenarios. The dataset contains 3,717 labeled
images of logos from ten prominent brands. Two types of models, Convolutional
Neural Networks (CNN) and Vision Transformer (ViT), were evaluated for their
performance. The ViT model, DaViT small, achieved the highest accuracy of
99.60%, while the DenseNet29 achieved the fastest inference speed of 366.62
FPS. The findings suggest that the DaViT model is a suitable choice for offline
applications due to its superior accuracy. This study demonstrates the
practical application of deep learning in brand logo classification tasks.