from timm.data.mixup importMixupfrom timm.loss import SoftTargetCrossEntropy from models.vmamba_efficient import EfficientMamba_T from torch.autograd import Variable from torchvision import datasets torch.backends.cudnn.benchmark = False import warnings warnings.filterwarnings("ignore") os.environ['CUDA...
作者研究了轻量级模型设计的新方法,通过引入视觉状态空间模型(SSM)以提高效率和性能。提出了一种名为EcientVMamba的高效模型变体,结合选择性扫描和有效跳跃采样,同时利用全局和局部表示特征。EcientVMamba在多种视觉任务中取得了具有竞争力的结果,并降低了计算复杂度。文章还探讨了SSMs在视觉任务中的应用,并指出现有轻...
接下来在EfficientVMamba实战:使用EfficientVMamba实现图像分类任务(二)中完成训练和测试。
使用EfficientVMamba实现图像分类任务 transformer做图像分类,号外号外:awesome-vit上新啦,欢迎大家StarStarStar~https://github.com/open-mmlab/awesome-vitgithub.com/open-mmlab/awesome-vitVisionTransformer必读系列之图像分类综述(一):概述VisionTransformer必读
git clone https://github.com/TerryPei/EfficientVMamba.git cd EfficientVMamba step2:Environment Setup: The install VMamba recommends setting up a conda environment and installing dependencies via pip. Use the following commands to set up your environment:...
To get started, first clone the VMamba repository and navigate to the project directory:git clone https://github.com/TerryPei/EfficientVMamba.git cd EfficientVMamba step2:Environment Setup: The install VMamba recommends setting up a conda environment and installing dependencies via pip. Use the ...
Recognizing the significance of prioritizing model efficiency and resource usage in knowledge tracing, we introduce Mamba4KT. This novel model is the first to explore enhanced efficiency and resource utilization in knowledge tracing. We also examine the interpretability of the Mamba structure both ...
To address these challenges, we propose NetMamba, an efficient linear-time state space model equipped with a comprehensive traffic representation scheme. We adopt a specially selected and improved unidirectional Mamba architecture for the networking field, instead of the Transformer, to address efficiency...
提出了一种名为EcientVMamba的高效模型变体,结合选择性扫描和有效跳跃采样,同时利用全局和局部表示特征。EcientVMamba在多种视觉任务中取得了具有竞争力的结果,并降低了计算复杂度。文章还探讨了SSMs在视觉任务中的应用,并指出现有轻量级模型在保持全局表示能力方面的挑战。
本文使用EcientVMamba模型实现图像分类任务,模型选择最小的EcientVMamba_T,在植物幼苗分类任务ACC达到了93%+,达到了ViM的水平。。 通过这篇文章能让你学到: 如何使用数据增强,包括transforms的增强、CutOut、MixUp、CutMix等增强手段? 如何实现EfficientVMamba模型实现训练?