LoveDA: A Remote Sensing Land-Cover Dataset for Domain Adaptive Semantic Segmentation

Last update: Nov 21, 2022

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Deep Learning swin-upernet

Overview

LoveDA: A Remote Sensing Land-Cover Dataset for Domain Adaptive Semantic Segmentation

by Junjue Wang, Zhuo Zheng, Ailong Ma, Xiaoyan Lu, and Yanfei Zhong

This is an initial benchmark for Unsupervised Domain Adaptation.

Getting Started

Requirements:

pytorch >= 1.7.0
python >=3.6
pandas >= 1.1.5

Prepare LoveDA Dataset

ln -s </path/to/LoveDA> ./LoveDA

Evaluate CBST Model on the predict set

1. Download the pre-trained weights

2. Move weight file to log directory

mkdir -vp ./log/
mv ./CBST_2Urban.pth ./log/CBST_2Urban.pth

3. Evaluate on Urban test set

bash ./scripts/predict_cbst.sh

Submit your test results on LoveDA Unsupervised Domain Adaptation Challenge and you will get your Test score.

Train CBST Model

From Rural to Urban

bash ./scripts/train_cbst.sh

Eval CBST Model on Urban val set

bash ./scripts/eval_cbst.sh

Owner

Payphone

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LoveDA: A Remote Sensing Land-Cover Dataset for Domain Adaptive Semantic Segmentation

Related tags

Overview

LoveDA: A Remote Sensing Land-Cover Dataset for Domain Adaptive Semantic Segmentation

by Junjue Wang, Zhuo Zheng, Ailong Ma, Xiaoyan Lu, and Yanfei Zhong

This is an initial benchmark for Unsupervised Domain Adaptation.

Getting Started

Requirements:

Prepare LoveDA Dataset

Evaluate CBST Model on the predict set

1. Download the pre-trained weights

2. Move weight file to log directory

3. Evaluate on Urban test set

Train CBST Model

Owner

Payphone

Effect of Deep Transfer and Multi task Learning on Sperm Abnormality Detection

Pytorch implementation of MLP-Mixer with loading pre-trained models.

Implementation of CVAE. Trained CVAE on faces from UTKFace Dataset to produce synthetic faces with a given degree of happiness/smileyness.

DiffStride: Learning strides in convolutional neural networks

Python calculations for the position of the sun and moon.

This package proposes simplified exporting pytorch models to ONNX and TensorRT, and also gives some base interface for model inference.

A tight inclusion function for continuous collision detection

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ROS-UGV-Control-Interface - Control interface which can be used in any UGV

Block Sparse movement pruning

Code repository for "Reducing Underflow in Mixed Precision Training by Gradient Scaling" presented at IJCAI '20

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