🤖 AI Summary
This study systematically compares the performance of three mainstream visual recognition strategies—custom-designed CNNs, fixed pretrained models used as feature extractors, and fine-tuned transfer learning—under real-world conditions. Through controlled experiments across five image classification datasets, the methods are comprehensively evaluated in terms of accuracy, macro F1-score, training time, and parameter count. The work provides the first empirical evidence across diverse real-world domains that transfer learning consistently achieves the best predictive performance, while custom CNNs offer a more favorable trade-off between efficiency and accuracy under computational and memory constraints. These findings offer practical guidance for model selection in applied settings where resource limitations must be balanced against performance requirements.
📝 Abstract
Convolutional Neural Networks (CNNs) are a standard approach for visual recognition due to their capacity to learn hierarchical representations from raw pixels. In practice, practitioners often choose among (i) training a compact custom CNN from scratch, (ii) using a large pre-trained CNN as a fixed feature extractor, and (iii) performing transfer learning via partial or full fine-tuning of a pre-trained backbone. This report presents a controlled comparison of these three paradigms across five real-world image classification datasets spanning road-surface defect recognition, agricultural variety identification, fruit/leaf disease recognition, pedestrian walkway encroachment recognition, and unauthorized vehicle recognition. Models are evaluated using accuracy and macro F1-score, complemented by efficiency metrics including training time per epoch and parameter counts. The results show that transfer learning consistently yields the strongest predictive performance, while the custom CNN provides an attractive efficiency--accuracy trade-off, especially when compute and memory budgets are constrained.