Satellite Image Classification 🚀 This project aims to classify satellite images into four categories: cloudy areas, deserts, green areas, and bodies of water. Using Convolutional Neural Networks (CNN), the project addresses the problem of land cover analysis, providing valuable insights into ecosyste...
Deep Neural Network for Image Classification. Contribute to sinarazi/CNN-Image-Classification development by creating an account on GitHub.
读《ImageNet Classification with Deep Convolutional Neural Networks》 bell arXiv综述论文“Image Segmentation Using Deep Learning” 以前在CSDN写的。 arXiv于2020年1月15日上传图像分割综述论文“Image Segmentation Using Deep Learning: A Survey“。 CSDN-专业IT技术社区-登录本文探讨的 网络模型包括:1)全卷积...
In the case of ensemble learning, soft voting ensembles of task-specific CNNs achieved an accuracy of 90.4%. The feature fusion approach substantially improved the classification accuracy, with the SVM trained on fused features from the task specific-data achieving an accuracy of 97.3%. This ...
ResNet Github参考:https://github.com/tornadomeet/ResNet (转载请注明出处:http://www.jianshu.com/p/f71ba99157c7,谢谢!) Abstract 摘要:更深的神经网络往往更难以训练,我们在此提出一个残差学习的框架,以减轻网络的训练负担,这是个比以往的网络要深的多的网络。我们明确地将层作为输入学习残差函数,而不是...
Although CNNs excel in spatial feature extraction and can achieve relatively high classification accuracy, there are some limitations. First, the receptive field of CNNs is limited by the size of the convolution kernel, which introduces difficulties in capturing global information. Second, CNNs are ...
github地址:https://github.com/iduta/iresnet 论文地址:https://arxiv.org/abs/2004.04989 该论文主要关注点: 网络层之间的信息流动-the flow of information through the network layers 残差构造模块-the residual building block 投影捷径-the projection shortcut 该论文主要贡献: 提出了一种新的残差网络。该...
XNOR-Nets offer the possibility of running state-of-the-art networks on CPUs (rather than GPUs) in real-time. Our binary networks are simple, accurate, efficient, and work on challenging visual tasks. We evaluate our approach on the ImageNet classification task. The classification accuracy with...
We have implemented Monte Carlo dropout for semantic segmentation, pixel-wise regression and classification in the pipeline. During the inference phase, 20 different models are created using Monte Carlo dropout and model uncertainty is calculated on the test set. For pixel-wise regression and semantic...
We test our approach on image classification tasks using several networks on three different datasets, namely CIFAR10, SVHN, and CINIC10.Similar content being viewed by others The Research about Recurrent Model-Agnostic Meta Learning Article 01 January 2020 Few-shot and meta-learning methods for...