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README.md

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+---
+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.