The recent advances in deep learning (DL) have been accelerated by access to large-scale data and compute. These large-scale resources have been used to train progressively larger models which are resource intensive in terms of compute, data, energy, and carbon emissions. These costs are becoming a new type of entry barrier to researchers and practitioners with limited access to resources at such scale, particularly in the Global South. In this work, we take a comprehensive look at the landscape of existing DL models for vision tasks and demonstrate their usefulness in settings where resources are limited. To account for the resource consumption of DL models, we introduce a novel measure to estimate the performance per resource unit, which we call the PePR score. Using a diverse family of 131 unique DL architectures (spanning 1M to 130M trainable parameters) and three medical image datasets, we capture trends about the performance-resource trade-offs. In applications like medical image analysis, we argue that small-scale, specialized models are better than striving for large-scale models. Furthermore, we show that using pretrained models can significantly reduce the computational resources and data required. We hope this work will encourage the community to focus on improving AI equity by developing methods and models with smaller resource footprints.

翻译：深度学习（DL）的最新进展得益于大规模数据和计算资源的获取。这些大规模资源被用于训练日益庞大的模型，这类模型在计算、数据、能源和碳排放方面消耗巨大。这些成本正成为获取有限规模资源的研究者和从业者（尤其是在全球南方地区）面临的新型准入门槛。本文系统审视了现有面向视觉任务的深度学习模型格局，并论证了它们在资源受限环境中的实用价值。为衡量深度学习模型的资源消耗，我们提出了一种新型度量指标——每资源单元性能评分（PePR分数），用以估算单位资源对应的性能产出。通过131种独特深度学习架构（参数量跨度从1M到130M）与三个医学影像数据集的实验，我们捕捉到性能与资源之间的权衡趋势。在医学图像分析等应用中，我们认为小规模专业化模型优于追求大规模模型。此外，研究表明使用预训练模型可显著降低所需计算资源与数据量。希望这项工作能激励学界通过开发具有更小资源足迹的方法与模型，聚焦提升人工智能公平性。