| --- |
| license: apache-2.0 |
| language: |
| - en |
| tags: |
| - OneScience |
| - Earth Science |
| - Extreme Weather |
| - Semantic Segmentation |
| - Tropical Cyclone |
| - Atmospheric River |
| frameworks: PyTorch |
| --- |
| |
| <p align="center"><strong><span style="font-size: 30px;">ClimateNet</span></strong></p> |
|
|
| # Model Introduction |
|
|
| ClimateNet is an expert-labeled extreme-weather dataset and pixel-level segmentation model for tropical cyclones and atmospheric rivers. |
|
|
| Paper: ClimateNet: an expert-labeled open dataset and deep learning architecture for enabling high-precision analyses of extreme weather |
| https://doi.org/10.5194/gmd-14-107-2021 |
|
|
| # Model Description |
|
|
| The model was proposed by teams from LBNL, UC Berkeley, ETH Zurich, NVIDIA, NCAR, and collaborators. It was trained with four-channel CAM5.1 fields and expert segmentation masks. DeepLabv3+ supports tropical-cyclone and atmospheric-river detection and conditional precipitation analysis. |
|
|
| # Use Cases |
|
|
| | Use Case | Description | |
| | :---: | :--- | |
| | Extreme segmentation | Identify background, TC, and AR pixels. | |
| | Climate scenarios | Transfer segmentation to warming experiments. | |
| | Conditional precipitation | Extract event-conditioned precipitation statistics. | |
| | ModelScope/OneCode execution | Validate data, training, inference, segmentation metrics, and visualization. | |
| | Multi-GPU training | Start multi-process training through `torchrun`. | |
|
|
| # Usage Instructions |
|
|
| ```bash |
| hf download OneScience-Group/ClimateNet --local-dir ./ClimateNet |
| cd ClimateNet |
| ``` |
|
|
| ### Environment Dependencies |
|
|
| **Hardware Requirements** |
|
|
| - A GPU or DCU is recommended. |
| - A CPU can be used for connectivity validation with the default small-sample configuration. |
| - DCU users should install DTK 25.04.2 or a compatible OneScience-recommended version first. |
|
|
| **DCU Environment** |
|
|
| ```bash |
| # Activate DTK and Conda first |
| conda create -n onescience311 python=3.11 -y |
| conda activate onescience311 |
| pip install onescience[earth-dcu] -i http://mirrors.onescience.ai:3141/pypi/simple/ --trusted-host mirrors.onescience.ai |
| ``` |
|
|
| **GPU Environment** |
|
|
| ```bash |
| # Activate Conda first |
| conda create -n onescience311 python=3.11 -y libstdcxx-ng=12 libgcc-ng=12 gcc_linux-64=12 gxx_linux-64=12 |
| conda activate onescience311 |
| pip install onescience[earth-gpu] -i http://mirrors.onescience.ai:3141/pypi/simple/ --trusted-host mirrors.onescience.ai |
| ``` |
|
|
| ```bash |
| python scripts/fake_data.py |
| python scripts/train.py |
| torchrun --standalone --nproc_per_node=2 scripts/train.py |
| python scripts/inference.py |
| python scripts/result.py |
| ``` |
| Training uses weighted cross-entropy. Inference returns finite class probabilities and evaluation reports per-class and mean IoU. |
| ## Trained Weights |
| No weights are bundled under `weight/`. The authors provide trained models and data at https://portal.nersc.gov/project/ClimateNet/. |
| # Citation and License |
| This repository is an independent engineering reproduction of the public ClimateNet specifications. |
|
|
| The original paper is licensed under CC BY 4.0; official models, code, and data retain their respective terms. |
|
|
