Add information about jcenter aar package

JDAI-CV · May 7, 2019 · e42ac94 · e42ac94
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diff --git a/README.md b/README.md
@@ -2,6 +2,7 @@
 
 [![Build Status](https://dev.azure.com/daquexian/dabnn/_apis/build/status/Android%20Build%20%26%20Test?branchName=master)](https://dev.azure.com/daquexian/dabnn/_build/latest?definitionId=2&branchName=master)
 [![License](https://img.shields.io/badge/license-BSD--3--Clause-blue.svg)](LICENSE) 
+[![jcenter](https://img.shields.io/badge/dynamic/json.svg?label=jcenter&query=name&url=https%3A%2F%2Fapi.bintray.com%2Fpackages%2Fdaquexian566%2Fmaven%2Fdabnn%2Fversions%2F_latest)](https://bintray.com/daquexian566/maven/dabnn/_latestVersion)
 [![Gitter Chat](https://img.shields.io/gitter/room/dabnn/dabnn.svg)](https://gitter.im/dabnn/dabnn)
 [![PRs Welcome](https://img.shields.io/badge/PRs-welcome-brightgreen.svg)](https://github.com/JDAI-CV/dabnn/pulls)
 
@@ -43,16 +44,14 @@ The following is the comparison between our dabnn and [Caffe](http://caffe.berke
 
 ![Comparison](images/comparison_en.png)
 
-## Example project
-
-Android app demo: https://github.com/JDAI-CV/dabnn-example
-
 ## Convert ONNX Model
 
 We provide a conversion tool, named onnx2bnn, to convert an ONNX model to a dabnn model. To get the conversion tool, just build the project using the native toolchain (instead of arm cross-compiling toolchain). For Linux users, we provide pre-built onnx2bnn AppImage. Linux users can download it from [GitHub Releases](https://github.com/JDAI-CV/dabnn/releases). For the usage and other information about AppImage, please check out https://appimage.org .
 
 Note: Binary convolution is a custom operator, so whether the ONNX model is dabnn-comptabile heavily depends on the implementation of the binary convolution in the training code. We will soon provide an dabnn-comptabile PyTorch implementation of binary convolution.
 
+After conversion, the generated dabnn model can be deployed on armv8 devices. For Android developer, we have provided Android AAR package and published it on [jcenter](https://bintray.com/daquexian566/maven/dabnn/_latestVersion), for the usage please check out [example project](https://github.com/JDAI-CV/dabnn-example).
+
 ## Pretrained Models
 
 We publish two pretrained binary neural network models based on [Bi-Real Net](https://arxiv.org/abs/1808.00278) on ImageNet. More pretrained models will be published in the future.
@@ -65,6 +64,10 @@ We publish two pretrained binary neural network models based on [Bi-Real Net](ht
 
 We plan to participate the [ACM Multimedia 2019 Open Source Software Competition](https://www.acmmm.org/2019/call-for-open-source-software-competition/). Our implementation details will be presented in a 4-page short paper soon.
 
+## Example project
+
+Android app demo: https://github.com/JDAI-CV/dabnn-example
+
 ## License
 
 [BSD 3 Clause](LICENSE)
diff --git a/README_CN.md b/README_CN.md
@@ -2,6 +2,7 @@
 
 [![Build Status](https://dev.azure.com/daquexian/dabnn/_apis/build/status/Android%20Build%20%26%20Test?branchName=master)](https://dev.azure.com/daquexian/dabnn/_build/latest?definitionId=2&branchName=master)
 [![License](https://img.shields.io/badge/license-BSD--3--Clause-blue.svg)](LICENSE) 
+[![jcenter](https://img.shields.io/badge/dynamic/json.svg?label=jcenter&query=name&url=https%3A%2F%2Fapi.bintray.com%2Fpackages%2Fdaquexian566%2Fmaven%2Fdabnn%2Fversions%2F_latest)](https://bintray.com/daquexian566/maven/dabnn/_latestVersion)
 [![Gitter Chat](https://img.shields.io/gitter/room/dabnn/dabnn.svg)](https://gitter.im/dabnn/dabnn)
 [![PRs Welcome](https://img.shields.io/badge/PRs-welcome-brightgreen.svg)](https://github.com/JDAI-CV/dabnn/pulls)
 
@@ -45,16 +46,14 @@ dabnn_bireal18_imagenet_stem   43279353 ns   41533009 ns         14       <---
 
 ![Comparison](images/comparison_cn.png)
 
-## 示例工程
-
-Android app demo: https://github.com/JDAI-CV/dabnn-example
-
 ## 如何转换 ONNX 模型
 
 我们提供模型转换工具 onnx2bnn 将 ONNX 模型转换为 dabnn 格式的模型。用本地编译工具链（而不是 arm 交叉编译工具链）编译这个项目就可以编译出 onnx2dnn。对 Linux 用户我们提供可以在 Linux 下无需编译直接运行的 AppImage，从 [GitHub Releases](https://github.com/JDAI-CV/dabnn/releases) 下载即可。AppImage 的使用方法和其它相关信息请参考 https://appimage.org/。
 
 注意：因为二值卷积是一种自定义操作，所以 ONNX 模型是否与 dabnn 兼容极大程度上依赖于训练代码中二值卷积的实现。我们很快会提供一个与 dabnn 兼容的二值卷积 PyTorch 实现。
 
+转换完成后得到的 dabnn 模型就可以在 armv8 设备上使用。对 Android 开发者我们已经把 Android AAR 包上传到了 [jcenter](https://bintray.com/daquexian566/maven/dabnn/_latestVersion)，使用方法请看[示例工程](https://github.com/JDAI-CV/dabnn-example)。
+
 ## 预训练模型
 
 我们提供两个在 ImageNet 上训练的、基于 [Bi-Real Net](https://arxiv.org/abs/1808.00278) 的二值网络模型，将来还会有其它的模型发布。
@@ -67,6 +66,10 @@ Android app demo: https://github.com/JDAI-CV/dabnn-example
 
 我们计划参加 [ACM Multimedia 2019 Open Source Software Competition](https://www.acmmm.org/2019/call-for-open-source-software-competition/). dabnn 的技术细节很快会在一篇四页的短论文中描述。
 
+## 示例工程
+
+Android app demo: https://github.com/JDAI-CV/dabnn-example
+
 ## 开源许可
 
 [BSD 3 Clause](LICENSE)