xiaohehuanshu

xiaohehuanshu's Stars

• Stability-AI/generative-models

  Generative Models by Stability AI

  Language:Python23.2k2.6k

• lllyasviel/ControlNet

  Let us control diffusion models!

  Language:Python29k2.6k

• zuoxiaolei/swj_room_match

  特征工程和自编码神经网络匹配相似户型

  Language:Python41

• ysig/GraKeL

  A scikit-learn compatible library for graph kernels

  Language:Python58796

• boyu-ai/Hands-on-RL

  https://hrl.boyuai.com/

  Language:Jupyter Notebook2.1k491

• FighterLYL/GraphNeuralNetwork

  《深入浅出图神经网络：GNN原理解析》配套代码

  Language:Jupyter Notebook1.7k450

• lamelizard/GraphWaveFunctionCollapse

  WaveFunctionCollapse on graphs

  Language:Python577

• mxgmn/WaveFunctionCollapse

  Bitmap & tilemap generation from a single example with the help of ideas from quantum mechanics

  Language:C#23k1.2k

• YassineHimeur/QUD-dataset

  Qatar university dataset (QUD) is an open access repository, which includes micro-moments power consumption footprints of different appliances. It is collected at Qatar university energy lab. In the initial version of QUD, power usage footprints have been gathered for a period of more than 3 months until now. The collection campaign is still ongoing in order to cover a period of one year and other appliances. The testbeds used to glean the data are described more thoroughly in the in the paper: Y. Himeur, A. Alsalemi, F. Bensaali, A. Amira, Building power consumption datasets: Survey, taxonomy and future directions, Energy & Buildings, 2020. (Submitted) Those wishing to use the dataset in academic work should cite this paper as the reference. QUD_app-1.csv: this file includes the different kinds of data collected during the measurement campaign: Column 1: Date Column 2: Time Column 3: appID Column 4: occupancy pattern Column 5: Power consumption Column 6: Normalized power Column 7: Quantified power Column 8: Micro-moment class

  Language:Jupyter Notebook105

• rsjain1978/energy_performance_building

  Accurate quantitative estimation of energy performance of residential buildings using statistical machine learning tools

  Language:HTML2

• WZMIAOMIAO/deep-learning-for-image-processing

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

  Language:Python21.6k7.8k

• bubbliiiing/unet-pytorch

  这是一个unet-pytorch的源码，可以训练自己的模型

  Language:Python1.2k245

• bat67/pytorch-FCN-easiest-demo

  PyTorch Implementation of Fully Convolutional Networks (a very simple and easy demo).

  Language:Python19373

• woodfrog/floor-sp

  Floor-SP: Inverse CAD for Floorplans by Sequential Room-wise Shortest Path, ICCV 2019

  Language:Python12926

• zlzeng/DeepFloorplan

  Language:Python260100

• Deeachain/Segmentation-Pytorch

  Semantic Segmentation in Pytorch. Network include: FCN、FCN_ResNet、SegNet、UNet、BiSeNet、BiSeNetV2、PSPNet、DeepLabv3_plus、 HRNet、DDRNet

  Language:Python44768

• nikhilbarhate99/PPO-PyTorch

  Minimal implementation of clipped objective Proximal Policy Optimization (PPO) in PyTorch

  Language:Python1.6k331

• Megvii-BaseDetection/YOLOX

  YOLOX is a high-performance anchor-free YOLO, exceeding yolov3~v5 with MegEngine, ONNX, TensorRT, ncnn, and OpenVINO supported. Documentation: https://yolox.readthedocs.io/

  Language:Python9.2k2.2k

• wkentaro/pytorch-fcn

  PyTorch Implementation of Fully Convolutional Networks. (Training code to reproduce the original result is available.)

  Language:Python1.7k479

• hszhao/semseg

  Semantic Segmentation in Pytorch

  Language:Python1.3k244

• DLR-RM/stable-baselines3

  PyTorch version of Stable Baselines, reliable implementations of reinforcement learning algorithms.

  Language:Python8.3k1.6k

• gouxiangchen/ac-ppo

  Actor-Critic and openAI clipped PPO in gym cartpole-v0 and pendulum-v0 environment

  Language:Python216

• sweetice/Deep-reinforcement-learning-with-pytorch

  PyTorch implementation of DQN, AC, ACER, A2C, A3C, PG, DDPG, TRPO, PPO, SAC, TD3 and ....

  Language:Python3.7k837

• datawhalechina/easy-rl

  强化学习中文教程（蘑菇书🍄），在线阅读地址：https://datawhalechina.github.io/easy-rl/

  Language:Jupyter Notebook8.6k1.8k

• PacktPublishing/Deep-Reinforcement-Learning-Hands-On

  Hands-on Deep Reinforcement Learning, published by Packt

  Language:Python2.8k1.3k

• uvipen/Super-mario-bros-PPO-pytorch

  Proximal Policy Optimization (PPO) algorithm for Super Mario Bros

  Language:Python1.1k196

• louisnino/RLcode

  Language:Python832283

• guofei9987/scikit-opt

  Genetic Algorithm, Particle Swarm Optimization, Simulated Annealing, Ant Colony Optimization Algorithm,Immune Algorithm, Artificial Fish Swarm Algorithm, Differential Evolution and TSP(Traveling salesman)

  Language:Python5.1k971

• art-programmer/FloorplanTransformation

  Raster-to-Vector: Revisiting Floorplan Transformation

  Language:Lua472166

• mcneel/rhino-developer-samples

  Rhino and Grasshopper developer sample code

  Language:C#595329