Classification of Tents in Street Bazaars Using CNN
Azamat Ibragimov, Ruslan Isaev, Remudin Reshid Mekuria, Gulnaz Gimaletdinova, Dim Shaiakhmetov

TL;DR
This paper compares a custom CNN and EfficientNetB0 for classifying tents in street bazaars, demonstrating that transfer learning with EfficientNetB0 achieves higher accuracy and better generalization on an augmented dataset.
Contribution
It introduces an extended, publicly available dataset for bazaar tent classification and evaluates the effectiveness of transfer learning with EfficientNetB0 in this context.
Findings
EfficientNetB0 achieved 98.4% accuracy
Custom CNN achieved 92.8% accuracy
Transfer learning significantly improves classification performance
Abstract
This research paper proposes an improved deep learning model for classifying tents in street bazaars, comparing a custom Convolutional Neural Network (CNN) with EfficientNetB0. This is a critical task for market organization with a tent classification, but manual methods in the past have been inefficient. Street bazaars represent a vital economic hub in many regions, yet their unstructured nature poses significant challenges for the automated classification of market infrastructure, such as tents. In Kyrgyzstan, more than a quarter of the country's GDP is derived from bazaars. While CNNs have been widely applied to object recognition, their application to bazaar-specific tasks remains underexplored. Here, we build upon our original approach by training on an extended set of 126 original photographs that were augmented to generate additional images. This dataset is publicly available for…
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