EfficientNet PyTorch 快速开始 使用pip install efficientnet_pytorch的net_pytorch并使用以下命令加载经过预训练的EfficientNet： from efficientnet_pytorch import EfficientNet model = EfficientNet . from_pretrained ( 'efficientnet-b0' ) 更新 更新（2021年4月2日） 已发布！ 当您阅读本文时，我正在努力实现它：) 关于EfficientNetV2： EfficientNetV2是卷积网络的新家族，与以前的模型相比，它具有更快的训练速度和更好的参数效率。 为了开发该系列模型，我们将训练感知的神经体系结构搜索和缩放结合使用，以共同优化训练速度和参数效率。 从富含新操作（例如Fused-MBConv）的搜索空

Many research groups in academia and industry are focusing on the performance improvement of electronic nose (E-nose) systems mainly involving three.optimizations, which are sensitive material selection and sensor array optimization,enhanced feature extraction methods and pattern recognition method selection. For a specific application, the feature extraction method is a basic part of these three optimizations and a key point in E-nose system performance improvement. The aim of a feature extract

Automatic human body feature extraction and personal size measurement

A study for texture feature extraction of high-resolution satellite images based on a direction measure and gray level co-occurrence matrix fusion algorithm

We present a new edge-preserving image smoothing approach by incorporating local features into a holistic optimization framework. Our method embodies a gradient constraint to enforce detail eliminating and an intensity constraint to achieve shape maintaining. The gradients of high-contrast details are suppressed to a lower magnitude, subsequent to which structural edges can be located. The intensities of a small region are regulated to resemble the initial fabric, which facilitates further detai

特征提取的各种基础和先进方法,具有重要的参考价值,可用在图像处理/模式识别等各个领域.