From ed34024a886d01d294a9c0c09bf6e70bb5b01464 Mon Sep 17 00:00:00 2001 From: Yulv-git Date: Sat, 23 Apr 2022 10:59:49 +0800 Subject: [PATCH] Update some links in README_CN.md and fix some typos. --- docs/NAS-Bench-201-PURE.md | 2 +- docs/NAS-Bench-201.md | 2 +- docs/README_CN.md | 8 ++++---- 3 files changed, 6 insertions(+), 6 deletions(-) diff --git a/docs/NAS-Bench-201-PURE.md b/docs/NAS-Bench-201-PURE.md index f46a12d..56a4afb 100644 --- a/docs/NAS-Bench-201-PURE.md +++ b/docs/NAS-Bench-201-PURE.md @@ -29,7 +29,7 @@ You can simply type `pip install nas-bench-201` to install our api. Please see s You can move it to anywhere you want and send its path to our API for initialization. - [2020.02.25] APIv1.0/FILEv1.0: [`NAS-Bench-201-v1_0-e61699.pth`](https://drive.google.com/open?id=1SKW0Cu0u8-gb18zDpaAGi0f74UdXeGKs) (2.2G), where `e61699` is the last six digits for this file. It contains all information except for the trained weights of each trial. - [2020.02.25] APIv1.0/FILEv1.0: The full data of each architecture can be download from [ -NAS-BENCH-201-4-v1.0-archive.tar](https://drive.google.com/open?id=1X2i-JXaElsnVLuGgM4tP-yNwtsspXgdQ) (about 226GB). This compressed folder has 15625 files containing the the trained weights. +NAS-BENCH-201-4-v1.0-archive.tar](https://drive.google.com/open?id=1X2i-JXaElsnVLuGgM4tP-yNwtsspXgdQ) (about 226GB). This compressed folder has 15625 files containing the trained weights. - [2020.02.25] APIv1.0/FILEv1.0: Checkpoints for 3 runs of each baseline NAS algorithm are provided in [Google Drive](https://drive.google.com/open?id=1eAgLZQAViP3r6dA0_ZOOGG9zPLXhGwXi). - [2020.03.09] APIv1.2/FILEv1.0: More robust API with more functions and descriptions - [2020.03.16] APIv1.3/FILEv1.1: [`NAS-Bench-201-v1_1-096897.pth`](https://drive.google.com/open?id=16Y0UwGisiouVRxW-W5hEtbxmcHw_0hF_) (4.7G), where `096897` is the last six digits for this file. It contains information of more trials compared to `NAS-Bench-201-v1_0-e61699.pth`, especially all models trained by 12 epochs on all datasets are avaliable. diff --git a/docs/NAS-Bench-201.md b/docs/NAS-Bench-201.md index 4b8957d..4fcb7f5 100644 --- a/docs/NAS-Bench-201.md +++ b/docs/NAS-Bench-201.md @@ -27,7 +27,7 @@ You can simply type `pip install nas-bench-201` to install our api. Please see s You can move it to anywhere you want and send its path to our API for initialization. - [2020.02.25] APIv1.0/FILEv1.0: [`NAS-Bench-201-v1_0-e61699.pth`](https://drive.google.com/open?id=1SKW0Cu0u8-gb18zDpaAGi0f74UdXeGKs) (2.2G), where `e61699` is the last six digits for this file. It contains all information except for the trained weights of each trial. - [2020.02.25] APIv1.0/FILEv1.0: The full data of each architecture can be download from [ -NAS-BENCH-201-4-v1.0-archive.tar](https://drive.google.com/open?id=1X2i-JXaElsnVLuGgM4tP-yNwtsspXgdQ) (about 226GB). This compressed folder has 15625 files containing the the trained weights. +NAS-BENCH-201-4-v1.0-archive.tar](https://drive.google.com/open?id=1X2i-JXaElsnVLuGgM4tP-yNwtsspXgdQ) (about 226GB). This compressed folder has 15625 files containing the trained weights. - [2020.02.25] APIv1.0/FILEv1.0: Checkpoints for 3 runs of each baseline NAS algorithm are provided in [Google Drive](https://drive.google.com/open?id=1eAgLZQAViP3r6dA0_ZOOGG9zPLXhGwXi). - [2020.03.09] APIv1.2/FILEv1.0: More robust API with more functions and descriptions - [2020.03.16] APIv1.3/FILEv1.1: [`NAS-Bench-201-v1_1-096897.pth`](https://drive.google.com/open?id=16Y0UwGisiouVRxW-W5hEtbxmcHw_0hF_) (4.7G), where `096897` is the last six digits for this file. It contains information of more trials compared to `NAS-Bench-201-v1_0-e61699.pth`, especially all models trained by 12 epochs on all datasets are avaliable. diff --git a/docs/README_CN.md b/docs/README_CN.md index fc34443..1f90656 100644 --- a/docs/README_CN.md +++ b/docs/README_CN.md @@ -3,7 +3,7 @@

--------- -[![MIT licensed](https://img.shields.io/badge/license-MIT-brightgreen.svg)](LICENSE.md) +[![MIT licensed](https://img.shields.io/badge/license-MIT-brightgreen.svg)](../LICENSE.md) 自动深度学习库 (AutoDL-Projects) 是一个开源的，轻量级的，功能强大的项目。 该项目实现了多种网络结构搜索(NAS)和超参数优化(HPO)算法。 @@ -142,8 +142,8 @@ # 其他 -如果你想要给这份代码库做贡献，请看[CONTRIBUTING.md](.github/CONTRIBUTING.md)。 -此外，使用规范请参考[CODE-OF-CONDUCT.md](.github/CODE-OF-CONDUCT.md)。 +如果你想要给这份代码库做贡献，请看[CONTRIBUTING.md](../.github/CONTRIBUTING.md)。 +此外，使用规范请参考[CODE-OF-CONDUCT.md](../.github/CODE-OF-CONDUCT.md)。 # 许可证 -The entire codebase is under [MIT license](LICENSE.md) +The entire codebase is under [MIT license](../LICENSE.md)