Benchmarking Pre-Trained Feature Extractors: A Comparative Study Across Deep Learning Tasks

Abstract

The rapid growth of artificial intelligence and deep learning has revolutionized diverse domains, including computer vision where transfer learning through pre-trained models has become a fundamental technique for enhancing performance of models and reducing computational costs. Although numerous pre-trained deep learning models have been widely adopted, their effectiveness varies significantly across application domains and datasets. The lack of comprehensive comparative studies makes it challenging for researchers to identify the most suitable models for achieving optimal performance in specific tasks. This study systematically compares seven pre-trained feature extractors across three architectural families, convolutional neural networks (CNNs), Vision Transformers (ViTs), and self-supervised models to provide practical guidance on model selection for downstream deep learning tasks. These models were evaluated on five benchmark datasets. Features were extracted with frozen weights and evaluated using linear probing, k-nearest neighbor retrieval, and 5-shot classification. ConvNeXt-B achieved the highest mean linear probe accuracy (90.40%), while DINOv2-S produced the best feature geometry for retrieval tasks (87.64%). CLIP-ViT-B/32 demonstrated the strongest cross-domain transfer, leading on texture recognition and satellite imagery few-shot classification. Older CNN architectures lagged significantly behind modern models by approximately 7%. Overall, this study simultaneously evaluates multiple feature extractors across multiple visual domains and evaluation protocols.