acm-sigspatial-cup-2023

https://github.com/mahan3340/acm-sigspatial-cup-2023

Science Score: 44.0%

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Repository

Basic Info

Host: GitHub
Owner: Mahan3340
License: apache-2.0
Language: Python
Default Branch: main
Size: 18.6 MB

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Created over 2 years ago · Last pushed over 2 years ago

Metadata Files

Readme License Citation

Install the mmsegmentation framework

shell conda create -n segmentation python=3.9 conda activate segmentation pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118 pip install -U openmim mim install mmengine mim install "mmcv>=2.0.0" pip install -v -e . pip install opencv-python pillow matplotlib seaborn tqdm pytorch-lightning 'mmdet>=3.1.0'

train the network

For the U-Net shell python tools/train.py lakeSegConfig/LakeSegDataset_UNetR_20231001.py For the KNet shell python tools/train.py lakeSegConfig/LakeSegDataset_KNnetR_20231001.py

Inference the network

Fill out the configfile and checkpointfile. The checkpointfile could be found at the workdirs Then run python python inference.py

Processed dataset

The processed dataset could be located at the data/geo_datasetsR

data_processing.ipynb file contains modules as follows:

clip_regions: clip and padding targeted 6 lake regions from raw Geotiffs;

label_regions: label each region tiff pixels

clip_tiles: split region tiff and labels into small labels

The preparedata2pngReduced.py will generate the final dataset. The dataset should be organized as data/geo_datasetsR More details are in the python files.

Owner

Name: Mahan
Login: Mahan3340
Kind: user
Location: USA

Repositories: 1
Profile: https://github.com/Mahan3340

Citation (CITATION.cff)

cff-version: 1.2.0
message: "If you use this software, please cite it as below."
authors:
  - name: "MMSegmentation Contributors"
title: "OpenMMLab Semantic Segmentation Toolbox and Benchmark"
date-released: 2020-07-10
url: "https://github.com/open-mmlab/mmsegmentation"
license: Apache-2.0

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