Comparative Analysis of Deep Learning Models for Brand Logo Classification in Real-World Scenarios
This is an incremental improvement for applications in brand recognition and marketing.
This study tackled brand logo classification in real-world scenarios by evaluating deep learning models, with the DaViT small model achieving 99.60% accuracy and DenseNet29 reaching 366.62 FPS inference speed.
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.