Official MegEngine implementation of CREStereo(CVPR 2022 Oral).
关键参数与规格
关键参数与规格
参数
值
语言
Python
类别
Model Development
子类别
Domain-specific training
关键词
Benchmarks
星标数
639
分叉数
78
贡献者
13
创建日期
2023-02-24
更新日期
2026-10-14
1 天星标数
0
7 天星标数
1
排名
15,694
上次排名
15,500
排名变化
-194
7 天星标排名
11,075
上次 7 天星标排名
11,144
7 天星标排名变化
69
总星标排名
15,999
上次总星标排名
15,977
总星标排名变化
-22
国家
USA
README 摘录
WeatherBench 2 - A benchmark for the next generation of data-driven global weather models **🚨🚨🚨 WeatherBench-X has been released. It provides an updated version of our evaluation code base. We encourage users to switch to the new codebase. 🚨🚨🚨** arXiv paper Google AI Blog post Why WeatherBench? WeatherBench 2 is a framework for evaluating and comparing data-driven and traditional numerical weather forecasting models. WeatherBench consists of: Publicly available, cloud-optimized ground truth and baseline datasets. See this quick-start to
explore the basic functionality or the API docs for more detail. Since high-resolution forecast files can be large, the WeatherBench 2 code was written with scalability in mind. See the command-line scripts based on Xarray-Beam and this guide for running the scripts on GCP using DataFlow. A website displaying up-to-date scores of many of the state-of-the-art data-driven and physical approaches. A paper describing the rationale behind the evaluation setup. WeatherBench 2
has been built as an evolving tool for the entire community. For this reason, we welcome any feedback (ideally, submitted as GitHub issues) or contributions. If you would like you model to be part of WeatherBench, check out this guide. Citation License This is not an official Google product.
常见问题
记录了多少星标和分叉？
google-research/weatherbench2 有 639 个星标和 78 个分叉。
仓库排名是多少？
google-research/weatherbench2 排名第 15,694。
记录的语言和类别是什么？
google-research/weatherbench2 有 639 个星标，语言为 Python，类别为 Model Development。
WeatherBench 2 is a framework for evaluating and comparing data-driven and traditional numerical weather forecasting models. WeatherBench consists of:
Publicly available, cloud-optimized ground truth and baseline datasets. For a complete list, see this page.
Open-source evaluation code. See this quick-start to explore the basic functionality or the API docs for more detail. Since high-resolution forecast files can be large, the WeatherBench 2 code was written with scalability in mind. See the command-line scripts based on Xarray-Beam and this guide for running the scripts on GCP using DataFlow.
A website displaying up-to-date scores of many of the state-of-the-art data-driven and physical approaches.
A paper describing the rationale behind the evaluation setup.
WeatherBench 2 has been built as an evolving tool for the entire community. For this reason, we welcome any feedback (ideally, submitted as GitHub issues) or contributions. If you would like you model to be part of WeatherBench, check out this guide.
Citation
@misc{rasp2023weatherbench,
title={WeatherBench 2: A benchmark for the next generation of data-driven global weather models},
author={Stephan Rasp and Stephan Hoyer and Alexander Merose and Ian Langmore and Peter Battaglia and Tyler Russel and Alvaro Sanchez-Gonzalez and Vivian Yang and Rob Carver and Shreya Agrawal and Matthew Chantry and Zied Ben Bouallegue and Peter Dueben and Carla Bromberg and Jared Sisk and Luke Barrington and Aaron Bell and Fei Sha},
year={2023},
eprint={2308.15560},
archivePrefix={arXiv},
primaryClass={physics.ao-ph}
}
License
This is not an official Google product.
Copyright 2023 Google LLC
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
https://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
