add README.md

README.md

-<img src=".github/Detectron2-Logo-Horz.svg" width="300" >
-
-Detectron2 is Facebook AI Research's next generation software system
-that implements state-of-the-art object detection algorithms.
-It is a ground-up rewrite of the previous version,
-[Detectron](https://github.com/facebookresearch/Detectron/),
-and it originates from [maskrcnn-benchmark](https://github.com/facebookresearch/maskrcnn-benchmark/).
-
-<div align="center">
-  <img src="https://user-images.githubusercontent.com/1381301/66535560-d3422200-eace-11e9-9123-5535d469db19.png"/>
-</div>
-
-### What's New
-* It is powered by the [PyTorch](https://pytorch.org) deep learning framework.
-* Includes more features such as panoptic segmentation, densepose, Cascade R-CNN, rotated bounding boxes, etc.
-* Can be used as a library to support [different projects](projects/) on top of it.
-  We'll open source more research projects in this way.
-* It [trains much faster](https://detectron2.readthedocs.io/notes/benchmarks.html).
-
-See our [blog post](https://ai.facebook.com/blog/-detectron2-a-pytorch-based-modular-object-detection-library-/)
-to see more demos and learn about detectron2.
-
-## Installation
-
-See [INSTALL.md](INSTALL.md).
-
-## Quick Start
-
-See [GETTING_STARTED.md](GETTING_STARTED.md),
-or the [Colab Notebook](https://colab.research.google.com/drive/16jcaJoc6bCFAQ96jDe2HwtXj7BMD_-m5).
-
-Learn more at our [documentation](https://detectron2.readthedocs.org).
-And see [projects/](projects/) for some projects that are built on top of detectron2.
-
-## Model Zoo and Baselines
-
-We provide a large set of baseline results and trained models available for download in the [Detectron2 Model Zoo](MODEL_ZOO.md).
-
-
-## License
+# <div aligh="center"><strong>Detectron2</strong></div>
+## 简介
+Detectron2 是 Facebook AI Research 的下一代软件系统实现最先进的对象检测算法
+
+### 使用pip方式安装
+detectron2 whl包下载目录：[http://10.6.10.68:8000/customized/detectron2/dtk2310/](http://10.6.10.68:8000/customized/detectron2/dtk2310/). 目前只提供有python3.8版本的安装包
+```shell
+pip install detectron2* (下载的detectron2的whl包)
+```
+### 使用源码编译方式安装
+
+#### 编译环境准备
+- 安装相关依赖
+```shell
+pip install numpy
+pip install 'urllib3==1.26.14'
+pip insta;; wheel
+```
+- 在首页 | 光合开发者社区下载 det23.10 解压在 /opt/ 路径下，并建立软连接，例如:
+```shell
+cd /opt
+wget http://10.6.10.68:8000/dtk-release/dtk23.10/CentOS7.6/DTK-23.10-CentOS7.6-x86_64.tar.gz
+tar -zxvf DTK-23.10-CentOS7.6-x86_64.tar.gz
+ln -s dtk-23.10 dtk
+source /opt/dtk/env.sh
+```
 
-Detectron2 is released under the [Apache 2.0 license](LICENSE).
+- 安装pytorch. pytorch whl包下载目录: [http://10.6.10.68:8000/debug/pytorch/dtk23.10/hipify](http://10.6.10.68:8000/debug/pytorch/dtk23.10/hipify). 根据需求下载对应的版本，安装如下：
+```shell
+pip install torch* (下载的torch的whl包)
+```
+#### 源码下载编译安装
+```shell
+git clone -b v0.1.3-release http://developer.hpccube.com/codes/aicomponent/detectron2.git
+python pymap_script.py /path/to/detectron2
+python -m pip install -e detectron2
+```
 
-## Citing Detectron2
+## 单侧
+```shell
+cd detectron2
+python -m unittest discover -v -s ./tests
+```
 
-If you use Detectron2 in your research or wish to refer to the baseline results published in the [Model Zoo](MODEL_ZOO.md), please use the following BibTeX entry.
+## Known Issue
+```
+无
+```
 
-```BibTeX
-@misc{wu2019detectron2,
-  author =       {Yuxin Wu and Alexander Kirillov and Francisco Massa and
-                  Wan-Yen Lo and Ross Girshick},
-  title =        {Detectron2},
-  howpublished = {\url{https://github.com/facebookresearch/detectron2}},
-  year =         {2019}
-}
+## 参考资料
+```shell
+https://github.com/facebookresearch/detectron2.git
 ```

README_BAK.md

+<img src=".github/Detectron2-Logo-Horz.svg" width="300" >
+
+Detectron2 is Facebook AI Research's next generation software system
+that implements state-of-the-art object detection algorithms.
+It is a ground-up rewrite of the previous version,
+[Detectron](https://github.com/facebookresearch/Detectron/),
+and it originates from [maskrcnn-benchmark](https://github.com/facebookresearch/maskrcnn-benchmark/).
+
+<div align="center">
+  <img src="https://user-images.githubusercontent.com/1381301/66535560-d3422200-eace-11e9-9123-5535d469db19.png"/>
+</div>
+
+### What's New
+* It is powered by the [PyTorch](https://pytorch.org) deep learning framework.
+* Includes more features such as panoptic segmentation, densepose, Cascade R-CNN, rotated bounding boxes, etc.
+* Can be used as a library to support [different projects](projects/) on top of it.
+  We'll open source more research projects in this way.
+* It [trains much faster](https://detectron2.readthedocs.io/notes/benchmarks.html).
+
+See our [blog post](https://ai.facebook.com/blog/-detectron2-a-pytorch-based-modular-object-detection-library-/)
+to see more demos and learn about detectron2.
+
+## Installation
+
+See [INSTALL.md](INSTALL.md).
+
+## Quick Start
+
+See [GETTING_STARTED.md](GETTING_STARTED.md),
+or the [Colab Notebook](https://colab.research.google.com/drive/16jcaJoc6bCFAQ96jDe2HwtXj7BMD_-m5).
+
+Learn more at our [documentation](https://detectron2.readthedocs.org).
+And see [projects/](projects/) for some projects that are built on top of detectron2.
+
+## Model Zoo and Baselines
+
+We provide a large set of baseline results and trained models available for download in the [Detectron2 Model Zoo](MODEL_ZOO.md).
+
+
+## License
+
+Detectron2 is released under the [Apache 2.0 license](LICENSE).
+
+## Citing Detectron2
+
+If you use Detectron2 in your research or wish to refer to the baseline results published in the [Model Zoo](MODEL_ZOO.md), please use the following BibTeX entry.
+
+```BibTeX
+@misc{wu2019detectron2,
+  author =       {Yuxin Wu and Alexander Kirillov and Francisco Massa and
+                  Wan-Yen Lo and Ross Girshick},
+  title =        {Detectron2},
+  howpublished = {\url{https://github.com/facebookresearch/detectron2}},
+  year =         {2019}
+}
+```