Due to the depth of the model and the convolution process of each layer will produce a great amount of calculation, the GPU and storage performance of the device are extremely demanding, and the GPU and storage devices equipped on the embedded and mobile terminals cannot su...
BigEarthNet Deep Learning Models with A New Class-Nomenclature for Remote Sensing Image Understanding at the depth of 16 and 19 layers [VGG16 and VGG19] and ResNet model at the depth of 50, 101 and 152 layers [ResNet50, ResNet101, ResNet152] as well as K-Branch CNN model) in the...
- 《IEEE Transactions on Image Processing》 被引量: 161发表: 2016年 A Light CNN for Deep Face Representation with Noisy Labels The volume of convolutional neural network (CNN) models proposed for face recognition has been continuously growing larger to better fit large amount of tr... X Wu,...
--dataset-d Dataset name defined in qtcls/datasets/__init__.py, such as cifar10 and imagenet1k. / --model_lib Model library where models come from. The toolbox's basic (default) model library is extended from torchvision and timm, and the toolbox also supports the original timm. defa...
^Factorized bilinear models for image recognition ^abNon-local neural networks ^abcdefSqueeze-and-excitation networks ^abcCBAM: Convolutional block attention module ^abcGather-excite: Exploiting feature context in convolutional neural networks ^abcdA2-Nets: Double attention networks ^abcGlobal second-order...
Netron: Visualizer for neural network models, On line URL:Netron Falshtorch: Visualization toolkit for neural networks in PyTorch ! Bag of Tricks for Image Classification with Convolutional Neural Networks ... Dataset-Setting This project has been tailored to suit theCityscapesandCamViddatasets. The ...
In recent years, convolutionalneural networks(CNNs) have shown their advantages on MR image super-resolution (SR) tasks. Many current SR models, however, have heavy demands on computation and memory, which are not friendly to magnetic resonance imaging (MRI) where computing resource is usually co...
This repo is aimed to provide the info for AutoML research (especially for the lightweight models). Welcome to PR the works (papers, repositories) that are missed by the repo. 1.) Neural Architecture Search [Papers] Gradient: Searching for A Robust Neural Architecture in Four GPU Hours| [...
Through rigorous experimental validation on different datasets, ELMANet performs better in terms of steganalysis detection accuracy, while requiring lower network parameters and computational resource overheads compared to existing image steganalysis models. 展开 ...
The LH-ViT network proposed in this work consists of a multi-layer pyramid and alternate stacked Radar-ViT and RES-SE models. The recognition performance and efficiency of the LH-ViT are closely related to the number of the pyramid layer, the alternate stacked Radar-ViT and RES-SE models. ...