Pinned Repositories
DCNet-for-CV
EasyChatGPT-API
用python和flask简单实现调用chatGPT的API
EasyWeChatBot
1分钟用ChatGPT API实现微信聊天机器人
GxyNet
Geographical XY Coordinates Networks 用于在地理信息任务中检测关键点，可以一次检测多类多个。
Hyperspectral_unmixing_using_neural_network
1
Methanol_detection
ML-GIS
一些机器学习、深度学习算法在地理信息上的应用
ResNet50-MNIST-pytorch
这是一个resnet-50的pytorch实现的库，在MNIST数据集上进行训练和测试。
Swin-HU
The code of paper 'Efficient Blind Hyperspectral Unmixing with Non-local Spatial information based on Swin Transformer'
yolov3-pytorch
利用Ptorch搭建自己的yolov3目标检测平台
wangyunjeff's Repositories
wangyunjeff/EasyChatGPT-API
用python和flask简单实现调用chatGPT的API
wangyunjeff/ResNet50-MNIST-pytorch
这是一个resnet-50的pytorch实现的库，在MNIST数据集上进行训练和测试。
wangyunjeff/EasyWeChatBot
1分钟用ChatGPT API实现微信聊天机器人
wangyunjeff/Hyperspectral_unmixing_using_neural_network
1
wangyunjeff/Swin-HU
The code of paper 'Efficient Blind Hyperspectral Unmixing with Non-local Spatial information based on Swin Transformer'
wangyunjeff/yolov3-pytorch
利用Ptorch搭建自己的yolov3目标检测平台
wangyunjeff/DCNet-for-CV
wangyunjeff/GxyNet
Geographical XY Coordinates Networks 用于在地理信息任务中检测关键点，可以一次检测多类多个。
wangyunjeff/ML-GIS
一些机器学习、深度学习算法在地理信息上的应用
wangyunjeff/non-local-nets-for-CV
wangyunjeff/remote_sensing_to_DEM
wangyunjeff/swa
Stochastic Weight Averaging in PyTorch
wangyunjeff/Swin-Spectral
wangyunjeff/Unet-Segmentation-pytorch
wangyunjeff/Methanol_detection
wangyunjeff/copilot-note
wangyunjeff/CVNets
在常用的简单公开数据集尝试不同的计算机视觉网路
wangyunjeff/Figure-Bed
1
wangyunjeff/hu_autoencoders
A repository for methods by and implemented by the authors' that were used in the paper: Blind Hyperspectral Unmixing using Autoencoders: A Critical Comparison
wangyunjeff/llm_multimodel_WS_predictiondict
wangyunjeff/ML_in_Materals
wangyunjeff/pandas-cookbook
Recipes for using Python's pandas library
wangyunjeff/pandas-tutorial
适合初级到中级晋升者，有了体系之后就看熟练度了。
wangyunjeff/pytorch-vgg-cifar10
This is the PyTorch implementation of VGG network trained on CIFAR10 dataset
wangyunjeff/wangyunjeff.github.io
Github Pages template for academic personal websites, forked from mmistakes/minimal-mistakes