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一种轻量化故障诊断框架——LiConvFormer
The intelligent fault diagnosis of HNU IDG
A few shot learning repository for bearing fault diagnosis.
Auto-Embedding Transformer for Interpretable Few-Shot Fault Diagnosis of Rolling Bearings
mall项目是一套电商系统，包括前台商城系统及后台管理系统，基于Spring Boot+MyBatis实现，采用Docker容器化部署。 前台商城系统包含首页门户、商品推荐、商品搜索、商品展示、购物车、订单流程、会员中心、客户服务、帮助中心等模块。 后台管理系统包含商品管理、订单管理、会员管理、促销管理、运营管理、内容管理、统计报表、财务管理、权限管理、设置等模块。
Implementation of paper - YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information
This is the official code for paper "Speech Emotion Recognition with Global-Aware Fusion on Multi-scale Feature Representation" published in ICASSP 2022
🍀 Pytorch implementation of various Attention Mechanisms, MLP, Re-parameter, Convolution, which is helpful to further understand papers.⭐⭐⭐
The official pytorch implemention of our ICML-2021 paper "SimAM: A Simple, Parameter-Free Attention Module for Convolutional Neural Networks".
The model for the paper “Spatio-Temporal-Spectral Hierarchical Graph Convolutional Network With Semisupervised Active Learning for Patient-Specific Seizure Prediction”
This is the model code of the paper "Epileptic Seizure Detection in EEG Signals Using a Unified Temporal-Spectral Squeeze-and-Excitation Network"
Pytorch Implementations of large number classical backbone CNNs, data enhancement, torch loss, attention, visualization and some common algorithms.
📚 Freely available programming books
UrbanSound classification using Convolutional Recurrent Networks in PyTorch
Classification of 11 types of audio clips using MFCCs features and LSTM. Pretrained on Speech Command Dataset with intensive data augmentation.
The aim of this work is based on 1D-CNN layers and a Long Short-Term Memory (LSTM) network for the detection of arrhythmia.
1D-CNN Vibration Signal Bearing Fault Diagnosis
The Pytorch implementation of sound classification supports EcapaTdnn, PANNS, TDNN, Res2Net, ResNetSE and other models, as well as a variety of preprocessing methods.
基于深度学习的日用品图像分类与识别的研究与应用_本科论文代码
该工作由电子科技大学陈昂、刘俊凯、夏子寒同学完成。我们提出可以使用深度学习的方案对中国车牌进行检测和识别。我们提出使用YOLOv3网络进行车牌检测，然后创新性地对检测后的车牌使用空间变换网络STN加以校正，最后使用LPRNet网络进行车牌的字符与数字识别。目前实测结果，在老师提供的45张数据集上，我们的YOLOv3网络检测准确率（IOU）达到98.2%，深度学习级联网络识别准确率为95.6%…