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PyQt Examples（PyQt各种测试和例子） PyQt4 PyQt5

基于resnet的动物图像分类系统（python期末大作业）PyQt+Flask+HTML5+PyTorch

PyQt6中文教程

YoloSide - YOLOv8 GUI By PySide6

yolov3 with mobilenet v2 and ASFF

deep learning for image processing including classification and object-detection etc.

为GPT/GLM等LLM大语言模型提供实用化交互接口，特别优化论文阅读/润色/写作体验，模块化设计，支持自定义快捷按钮&函数插件，支持Python和C++等项目剖析&自译解功能，PDF/LaTex论文翻译&总结功能，支持并行问询多种LLM模型，支持chatglm3等本地模型。接入通义千问, deepseekcoder, 讯飞星火, 文心一言, llama2, rwkv, claude2, m…

PyTorch入门教程，在线阅读地址：https://datawhalechina.github.io/thorough-pytorch/

Inspired by the convolutional recurrent neural network(CRNN) and inception, we propose a multiscale time-frequency convolutional recurrent neural network (MTF-CRNN) for audio event detection. Our g…

The implementation of various lightweight networks by using PyTorch. such as：MobileNetV2，MobileNeXt，GhostNet，ParNet，MobileViT、AdderNet，ShuffleNetV1-V2，LCNet，ConvNeXt，etc. ⭐⭐⭐⭐⭐

Implementing Searching for MobileNetV3 paper using Pytorch

MobileNetV3-SSD for object detection and implementation in PyTorch

Caffe Implementation of MobileNets V3

MobileNetV3 in pytorch and ImageNet pretrained models

MobileNetV2-YoloV3-Nano: 0.5BFlops 3MB HUAWEI P40: 6ms/img, YoloFace-500k:0.1Bflops 420KB:fire::fire::fire:

74.3% MobileNetV3-Large and 67.2% MobileNetV3-Small model on ImageNet

mobilenetv3 with pytorch，provide pre-train model

一种轻量化故障诊断框架——LiConvFormer

《李宏毅深度学习教程》（李宏毅老师推荐👍，苹果书🍎），PDF下载地址：https://github.com/datawhalechina/leedl-tutorial/releases

Source codes for the paper "Deep Learning Algorithms for Rotating Machinery Intelligent Diagnosis: An Open Source Benchmark Study"

this code library is mainly about applying graph neural networks to intelligent diagnostic and prognostic.

This code is about the implementation of Domain Adversarial Graph Convolutional Network for Fault Diagnosis Under Variable Working Conditions.

pytorch handbook是一本开源的书籍，目标是帮助那些希望和使用PyTorch进行深度学习开发和研究的朋友快速入门，其中包含的Pytorch教程全部通过测试保证可以成功运行

Open rotating mechanical fault datasets (开源旋转机械故障数据集整理)

一种新的用于噪声干扰下刀具的无监督异常检测方法

Fault Diagnosis Employing Transfer Learning Techniques: Domain Adaptation and Domain Generalization