Which Backbone to Use: A Resource-efficient Domain Specific Comparison for Computer Vision
In contemporary computer vision applications, particularly image classification, architectural backbones pre-trained on large datasets like ImageNet are commonly employed as feature extractors. Despite the widespread use of these pre-trained convolutional neural networks (CNNs), there remains a gap...
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Main Authors | , |
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Format | Journal Article |
Language | English |
Published |
08.06.2024
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Subjects | |
Online Access | Get full text |
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Summary: | In contemporary computer vision applications, particularly image
classification, architectural backbones pre-trained on large datasets like
ImageNet are commonly employed as feature extractors. Despite the widespread
use of these pre-trained convolutional neural networks (CNNs), there remains a
gap in understanding the performance of various resource-efficient backbones
across diverse domains and dataset sizes. Our study systematically evaluates
multiple lightweight, pre-trained CNN backbones under consistent training
settings across a variety of datasets, including natural images, medical
images, galaxy images, and remote sensing images. This comprehensive analysis
aims to aid machine learning practitioners in selecting the most suitable
backbone for their specific problem, especially in scenarios involving small
datasets where fine-tuning a pre-trained network is crucial. Even though
attention-based architectures are gaining popularity, we observed that they
tend to perform poorly under low data finetuning tasks compared to CNNs. We
also observed that some CNN architectures such as ConvNeXt, RegNet and
EfficientNet performs well compared to others on a diverse set of domains
consistently. Our findings provide actionable insights into the performance
trade-offs and effectiveness of different backbones, facilitating informed
decision-making in model selection for a broad spectrum of computer vision
domains. Our code is available here: https://github.com/pranavphoenix/Backbones |
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DOI: | 10.48550/arxiv.2406.05612 |