Survey of resource-efficient backbones for computer vision for each domain
Survey of resource-efficient backbones for computer vision for each domain
Which Backbone to Use: A Resource-efficient Domain Specific Comparison for Computer Vision
arXiv paper abstract https://arxiv.org/abs/2406.05612
arXiv PDF paper https://arxiv.org/pdf/2406.05612
In ... computer vision ... particularly image classification ... remains a gap in understanding the performance of ... resource-efficient backbones across ... domains and dataset sizes.
... study ... evaluates ... lightweight, pre-trained CNN backbones under ... variety of datasets, including natural images, medical images, galaxy images, and remote sensing images.
... aims to aid ... practitioners in selecting the most suitable backbone for their specific problem, especially in ... small datasets where fine-tuning a pre-trained network is crucial.
Even though attention-based architectures are gaining popularity, ... observed that they tend to perform poorly under low data finetuning tasks compared to CNNs.
... also observed that some CNN architectures such as ConvNeXt, RegNet and EfficientNet performs well compared to others on a diverse set of domains consistently.
... provide ... insights into the performance trade-offs and effectiveness of ... backbones, facilitating informed decision-making in model ... for ... spectrum of ... domains ...
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