jajajajaja121

jajajajaja121's Stars

• Ha0Tang/SelectionGAN

  [CVPR 2019 Oral] Multi-Channel Attention Selection GAN with Cascaded Semantic Guidance for Cross-View Image Translation

  Language:Python46261

• menyifang/ADGAN

  The Implementation of paper "Controllable Person Image Synthesis with Attribute-Decomposed GAN" CVPR 2020 (Oral); Pose and Appearance Attributes Transfer;

  Language:Python47790

• microsoft/CoCosNet

  Cross-domain Correspondence Learning for Exemplar-based Image Translation. (CVPR 2020 Oral)

  Language:Python40145

• wtjiang98/BeautyGAN_pytorch

  Official PyTorch implementation of BeautyGAN (ACM MM 2018)

  Language:Python22141

• yunjey/stargan

  StarGAN - Official PyTorch Implementation (CVPR 2018)

  Language:Python5.2k972

• lucidrains/stylegan2-pytorch

  Simplest working implementation of Stylegan2, state of the art generative adversarial network, in Pytorch. Enabling everyone to experience disentanglement

  Language:Python3.7k591

• tomguluson92/StyleGAN_PyTorch

  The implementation of StyleGAN on PyTorch 1.0.1

  Language:Python54696

• junyanz/pytorch-CycleGAN-and-pix2pix

  Image-to-Image Translation in PyTorch

  Language:Python23.3k6.3k

• AliaksandrSiarohin/first-order-model

  This repository contains the source code for the paper First Order Motion Model for Image Animation

  Language:Jupyter Notebook14.6k3.2k

• wtjiang98/PSGAN

  PyTorch code for "PSGAN: Pose and Expression Robust Spatial-Aware GAN for Customizable Makeup Transfer" (CVPR 2020 Oral)

  Language:Python736147

• facebookresearch/detr

  End-to-End Object Detection with Transformers

  Language:Python13.8k2.5k

• DLLXW/data-science-competition

  该仓库用于记录作者本人参加的各大数据科学竞赛的获奖方案源码以及一些新比赛的原创baseline. 主要涵盖：kaggle, 阿里天池，华为云大赛校园赛，百度aistudio，和鲸社区，datafountain等

  Language:Python1.3k473

• ZFTurbo/Weighted-Boxes-Fusion

  Set of methods to ensemble boxes from different object detection models, including implementation of "Weighted boxes fusion (WBF)" method.

  Language:Python1.7k238

• chuliuT/Tianchi_Fabric_defects_detection

  广东工业智造大赛--布匹瑕疵检测复赛代码

  Language:Python18149

• aleju/imgaug

  Image augmentation for machine learning experiments.

  Language:Python14.5k2.4k

• huawei-noah/Efficient-AI-Backbones

  Efficient AI Backbones including GhostNet, TNT and MLP, developed by Huawei Noah's Ark Lab.

  Language:Python4.1k709

• cardwing/Codes-for-Lane-Detection

  Learning Lightweight Lane Detection CNNs by Self Attention Distillation (ICCV 2019)

  Language:Lua1k333

• Tramac/Fast-SCNN-pytorch

  A PyTorch Implementation of Fast-SCNN: Fast Semantic Segmentation Network

  Language:Python38093

• bermanmaxim/LovaszSoftmax

  Code for the Lovász-Softmax loss (CVPR 2018)

  Language:Jupyter Notebook1.4k269

• 4uiiurz1/pytorch-nested-unet

  PyTorch implementation of UNet++ (Nested U-Net).

  Language:Python872204

• MrGiovanni/UNetPlusPlus

  [IEEE TMI] Official Implementation for UNet++

  Language:Python2.3k543

• ZJUGiveLab/UNet-Version

  Language:Python676143

• MIC-DKFZ/nnUNet

  Language:Python6.1k1.8k

• lucastabelini/cnn_from_scratch

  A simple implementation of a convolutional neural network from scratch in C++

  Language:C++1310

• lucastabelini/PolyLaneNet

  Code for the paper entitled "PolyLaneNet: Lane Estimation via Deep Polynomial Regression" (ICPR 2020)

  Language:Python30375

• huggingface/transformers

  🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.

  Language:Python137k27.4k

• erikwijmans/Pointnet2_PyTorch

  PyTorch implementation of Pointnet2/Pointnet++

  Language:Python1.5k350

• charlesq34/pointnet

  PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation

  Language:Python4.9k1.5k

• zhoushengisnoob/DeepClustering

  Methods and Implements of Deep Clustering

  2.9k416

• houshanren/hangzhou_house_knowledge

  2017年买房经历总结出来的买房购房知识分享给大家，希望对大家有所帮助。买房不易，且买且珍惜。Sharing the knowledge of buy an own house that according to the experience at hangzhou in 2017 to all the people. It's not easy to buy a own house, so I hope that it would be useful to everyone.

  Language:CSS26.7k5.3k