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README更新
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README.md
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# Cloud Segmentation
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[](https://huggingface.co/spaces/caixiaoshun/cloudseg)
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[](https://github.com/pre-commit/pre-commit)
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[](https://pytorch.org/get-started/locally/)
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[](https://pytorchlightning.ai/)
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[](https://hydra.cc/)
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[](https://github.com/XavierJiezou/cloudseg#license)
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[](https://github.com/XavierJiezou/cloudseg/graphs/contributors)
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[](https://github.com/ashleve/lightning-hydra-template)
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[](https://www.nature.com/articles/nature14539)
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[](https://papers.nips.cc/paper/2020)
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## Datasets
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```bash
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cloudseg
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├── src
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├── configs
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├── data
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│ ├── hrcwhu
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│ │ ├── train.txt
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│ │ ├── test.txt
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│ │ ├── img_dir
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│ │ │ ├── train
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│ │ │ ├── test
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│ │ ├── ann_dir
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│ │ │ ├── train
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│ │ │ ├── test
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```
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## Supported Methods
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- [UNet (MICCAI 2016)](configs/model/unet)
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- [CDNetv1 (TGRS 2019)](configs/model/cdnetv1)
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- [CDNetv2 (TGRS 2021)](configs/model/cdnetv2)
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- [DBNet (TGRS 2022)](configs/model/dbnet)
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- [HrCloudNet (JEI 2024)](configs/model/hrcloudnet)
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- [McdNet (International Journal of Applied Earth Observation and Geoinformation 2024)](configs/model/mcdnet)
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- [Scnn (ISPRS 2024)](configs/model/scnn)
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## Installation
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```bash
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git clone https://github.com/XavierJiezou/cloudseg.git
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cd cloudseg
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conda env create -f environment.yaml
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conda activate cloudseg
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```
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## Usage
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**Train model with default configuration**
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```bash
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# train on CPU
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python src/train.py trainer=cpu
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# train on GPU
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python src/train.py trainer=gpu
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```
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**Train model with chosen experiment configuration from [configs/experiment/](configs/experiment/)**
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```bash
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python src/train.py experiment=experiment_name.yaml
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```
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**Tranin Example**
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```bash
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python src/train.py experiment=hrcwhu_cdnetv1.yaml
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```
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**You can override any parameter from command line like this**
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```bash
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python src/train.py trainer.max_epochs=20 data.batch_size=64
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```
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**Visualization in wandb**
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```bash
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python wand_vis.py --model-name model_name
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```
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**Example**
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```bash
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python wand_vis.py --model-name cdnetv1
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```
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