Performance Benchmarking of CNN Architectures for Fine-Grained Image Classification on CIFAR-100

Abstract

Fine-grained image classification on small-resolution datasets, such as CIFAR-100, remains challenging given the high intra-class similarities and limited visual detail. To understand the different deep learning models dealing with such constraints, this work benchmarks three popular CNN architectures: ResNet-18, GoogLeNet, and EfficientNet, strictly under the same conditions. All three were trained from scratch on the CIFAR-100 dataset using uniform data pre-processing and augmentation, optimization settings, and evaluation metrics for fairness in comparison. Performance evaluations are made in terms of test accuracy, precision, recall, and F1-score. Observations of training stability and generalization behaviors were also considered. Experimental results indicate that among the three models, ResNet-18 achieved the highest test accuracy, EfficientNet provided a good trade-off between accuracy and computational efficiency, while GoogLeNet showed the lowest performance due to optimization instability on small-sized images. With this, there is a clear insight into how architectural differences underpin performance diversity among models for handling fine-grained classification tasks and provide, for the first time, a clear baseline on how to select between CNN models based on accuracy requirements and resource constraints