Sarmadfismael

Sarmadfismael's Stars

• danfenghong/RSE_Cross-city

  Danfeng Hong, Bing Zhang, Hao Li, Yuxuan Li, Jing Yao, Chenyu Li, Martin Werner, Jocelyn Chanussot, Alexander Zipf, Xiao Xiang Zhu. Cross-City Matters: A Multimodal Remote Sensing Benchmark Dataset for Cross-City Semantic Segmentation using High-Resolution Domain Adapation Networks, Remote Sensing of Enviroment, 2023.

  Language:Python535

• qubvel-org/segmentation_models.pytorch

  Semantic segmentation models with 500+ pretrained convolutional and transformer-based backbones.

  Language:Python9.8k1.7k

• Sarmadfismael/RSOS_I2I

  Language:Python8

• google-research-datasets/scin

  The SCIN dataset contains 10,000+ images of dermatology conditions, crowdsourced with informed consent from US internet users. Contributions include self-reported demographic and symptom information and dermatologist labels. The dataset also contains estimated Fitzpatrick skin type and Monk Skin Tone.

  Language:Jupyter Notebook849

• RS-CSU/MemoryAdaptNet-master

  Language:Python311

• IGNF/FLAIR-1

  Semantic segmentation from aerial imagery (baseline of the FLAIR #1 challenge)

  Language:Python4613

• cv516Buaa/ST-DASegNet

  Language:Python254

• daifeng2016/FDANet

  This is a reporitory for releasing a novel DA method for high-resolution remote sensing image!

  Language:Python121

• clovaai/WCT2

  Software that can perform photorealistic style transfer without the need of any post-processing steps.

  Language:Python877145

• YanchaoYang/FDA

  Fourier Domain Adaptation for Semantic Segmentation

  Language:Python50581

• gjy3035/GCC-SFCN

  This is the official code of spatial FCN in the paper Learning from Synthetic Data for Crowd Counting in the Wild [CVPR2019].

  Language:Python16039

• csjliang/LPTN

  Official implementation of the paper 'High-Resolution Photorealistic Image Translation in Real-Time: A Laplacian Pyramid Translation Network' in CVPR 2021

  Language:Python41347

• rucv/DMDA

  Language:Python31

• taohan10200/NLT

  PyTorch implementations of the paper: "Neuron Linear Transformation: Modeling the Domain Shift for Crowd Counting. (T-NNLS, 2021)..."

  164

• DL-ML/pytorch-ssim

  pytorch structural similarity (SSIM) loss

  Language:Python1

• svishwa/crowdcount-mcnn

  Single Image Crowd Counting via MCNN (Unofficial Implementation)

  Language:Python509180

• gcding/CDFA-pytorch

  Code for Crowd counting via unsupervised cross-domain feature adaptation.

  Language:Python8

• Po-Hsun-Su/pytorch-ssim

  pytorch structural similarity (SSIM) loss

  Language:Python1.9k368

• jnoat92/Adversarial-Domain-Adaptation-for-Change-Detection

  Language:Python3

• AI-Chen/ColorMapGAN

  A pytorch implement of ColorMapGAN (TGRS2020)

  Language:Python226

• zhaoxin94/awesome-domain-adaptation

  A collection of AWESOME things about domian adaptation

  5.1k876

• tjvandal/unsupervised-spectral-synthesis

  Implementation of Spectral Synthesis for Satellite-to-Satellite Translation on GOES-16/17 and Himawari-8

  Language:Python71

• summitgao/RSIT_SRM_ISD

  Code for "Remote sensing image translation via style-based recalibration module and improved style discriminator", IEEE GRSL 2022

  Language:Python10

• mhaut/UIMG2DSM

  U-IMG2DSM: Unpaired Simulation of Digital Surface Models with Generative Adversarial Networks

  Language:Python152

• Adam-Liang/Aerial-Map-Translation

  Language:Python41

• xauter/BGAAE_CD

  Bipartite Graph Attention Autoencoders for Unsupervised Change Detection Using VHR Remote Sensing Images.

  Language:Python7

• eriklindernoren/PyTorch-GAN

  PyTorch implementations of Generative Adversarial Networks.

  Language:Python16.6k4.1k

• IPL-UV/pvl8dagans

  Official code for Cross-Sensor Adversarial Domain Adaptation of Landsat-8 and Proba-V images for Cloud Detection

  Language:Python173

• TongfeiLiu/LGPNet-for-building-CD

  Language:Python306

• risojevicv/RSSC-transfer

  Repository with the source code and models for the paper "Do we still need ImageNet pre-training in remote sensing scene classification?"

  Language:Python154