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Folder Structure

Prepare the following folders to organize this repo:

├── MMCTLN (code)
├── pretrain_weights (save the pretrained weights like vit, swin, etc)
├── model_weights (save the model weights)
├── fig_results (save the masks predicted by models)
├── lightning_logs (CSV format training logs)
├── data
│   ├── LoveDA
│   ├── uavid
│   ├── vaihingen
│   ├── potsdam

Install

Open the folder airs using Linux Terminal and create python environment:

conda create -n airs python=3.8
conda activate airs

conda install pytorch==1.10.0 torchvision==0.11.0 torchaudio==0.10.0 cudatoolkit=11.3 -c pytorch -c conda-forge
pip install -r GeoSeg/requirements.txt

Pretrained Weights

Quark Netdisk : rEsN

Data Preprocessing

Quark Netdisk : YxVA

Training

python MMCTLN/train_supervision.py -c MMCTLN/config/uavid/***.py

Use different config to train different models.

Validation

For example:

python MMCTLN/loveda_test.py -c MMCTLN/config/loveda/***.py -o fig_results/loveda/*** --rgb --val -t 'd4'

Testing

LoveDA

python MMCTLN/loveda_test.py -c MMCTLN/config/loveda/***.py -o fig_results/loveda/*** -t 'd4'

UAVid

python MMCTLN/inference_uavid.py \
-i 'data/uavid/uavid_test' \
-c MMCTLN/config/uavid/***.py \
-o fig_results/uavid/*** \
-t 'lr' -ph 1152 -pw 1024 -b 2 -d "uavid"

Inference on huge remote sensing image

python MMCTLN/inference_huge_image.py \
-i data/vaihingen/test_images \
-c GeoSeg/config/vaihingen/***.py \
-o fig_results/vaihingen/*** \
-t 'lr' -ph 512 -pw 512 -b 2 -d "pv"

Reproduction Results

Method Dataset F1 OA mIoU
MMCTLN Vaihingen 91.18 91.63 84.02
MMCTLN Potsdam 93.37 91.95 87.77
MMCTLN LoveDA - - 53.11
MMCTLN UAVid - - 70.51

Due to some random operations in the training stage, reproduced results (run once) are slightly different from the reported in paper.

Acknowledgement

Thanks to the GeoSeg framework for the help of the above code implementation of this work, and to the hardware support provided by the Advanced Computing Center of China Three Gorges University.