AdaptoVision通过精简设计实现高效图像识别，适合低资源环境部署。
AdaptoVision: A Multi-Resolution Image Recognition Model for Robust and Scalable Classification
- 采用增强残差单元与分组卷积，降低参数和计算量
- 在CIFAR-10达到95.3%准确率，CIFAR-100达85.77%，无需预训练
- 结构简洁，适合实时和嵌入式系统应用
本文提出AdaptoVision，一种新型卷积神经网络架构，旨在高效平衡计算复杂度与分类精度。通过引入增强残差单元、深度可分离卷积和分层跳跃连接，AdaptoVision显著减少参数量与计算需求，同时在多个基准与医学图像数据集上保持优异性能。实验表明，该模型在BreakHis数据集上达到当前最优表现，在无需预训练权重的情况下，CIFAR-10准确率达95.3%，CIFAR-100达85.77%。其轻量化设计促进有效特征提取与鲁棒泛化，特别适用于实时与资源受限场景的部署。
原文摘要 · Abstract (English)
This paper introduces AdaptoVision, a novel convolutional neural network (CNN) architecture designed to efficiently balance computational complexity and classification accuracy. By leveraging enhanced residual units, depth-wise separable convolutions, and hierarchical skip connections, AdaptoVision significantly reduces parameter count and computational requirements while preserving competitive performance across various benchmark and medical image datasets. Extensive experimentation demonstrates that AdaptoVision achieves state-of-the-art on BreakHis dataset and comparable accuracy levels, notably 95.3\% on CIFAR-10 and 85.77\% on CIFAR-100, without relying on any pretrained weights. The model's streamlined architecture and strategic simplifications promote effective feature extraction and robust generalization, making it particularly suitable for deployment in real-time and resource-constrained environments.
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