Add icon and scnet.

README.md

-# ViT 
-
+# ViT
 ## 论文
 `An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale`
 - https://arxiv.org/abs/2010.11929
+
 ## 模型结构
 Vision Transformer先将图像用卷积进行分块以降低计算量，再对每一块进行展平处理变成序列，然后将序列添加位置编码和cls token，再输入多层Transformer结构提取特征，最后将cls tooken取出来通过一个MLP（多层感知机）用于分类。
 
 ![img](./docs/vit.png)
+
 ## 算法原理
 图像领域借鉴《Transformer is all you need！》算法论文中的Encoder结构提取特征，Transformer的核心思想是利用注意力模块attention提取特征：
 
 ![img](./docs/attention.png)
+
 ## 环境配置
 ### Docker（方法一）
 ```
@@ -18,19 +20,19 @@ docker pull image.sourcefind.cn:5000/dcu/admin/base/custom:decode-ffmpeg-dtk23.0
 # <your IMAGE ID>用以上拉取的docker的镜像ID替换
 docker run --shm-size 10g --network=host --name=vit_migraphx --privileged --device=/dev/kfd --device=/dev/dri --group-add video --cap-add=SYS_PTRACE --security-opt seccomp=unconfined -v path_to_vit_migraphx:/home/vit_migraphx -it <your IMAGE ID> bash
 ```
+
 ### Dockerfile（方法二）
 ```
 cd vit_migraphx/docker
 docker build --no-cache -t vit_migraphx:test .
 docker run --rm --shm-size 10g --network=host --name=vit_migraphx --privileged --device=/dev/kfd --device=/dev/dri --group-add video --cap-add=SYS_PTRACE --security-opt seccomp=unconfined -v path_to_vit_migraphx:/home/vit_migraphx -it vit_migraphx:test bash
 ```
+
 ## 数据集
+下载推理数据[flower_photos.tgz](http://113.200.138.88:18080/aidatasets/project-dependency/flower_photos/-/raw/master/flower_photos.tgz)
 
-下载推理数据
-```
-wget https://storage.googleapis.com/download.tensorflow.org/example_images/flower_photos.tgz
-tar -zxvf flower_photos.tgz
 数据结构如下：
+```
 flower_photos
 ├── daisy
 │   ├── 100080576_f52e8ee070_n.jpg
@@ -56,7 +58,6 @@ flower_photos
 ```
 
 ## 推理
-
 ### 编译
 ```
 git clone https://developer.hpccube.com/codes/modelzoo/vit_migraphx.git
@@ -77,7 +78,6 @@ make
 ![img](./docs/result.jpg)
 
 ## 精度
-
 测试数据使用的是[flower_photos](https://storage.googleapis.com/download.tensorflow.org/example_images/flower_photos.tgz)，使用的加速卡是DCU Z100
 
 | Engine | Model Path| Data | Accuracy(%) |
@@ -88,13 +88,15 @@ make
 | MIGraphX | models/model.onnx | sunflowers | 97.4 |
 | MIGraphX | models/model.onnx | tulips | 94.1 |
 
-
 ## 应用场景
 ### 算法类别
-`图像分类`
+图像分类
+
 ### 热点应用行业
-`制造,环境,医疗,气象`
+制造,环境,医疗,气象
+
 ## 源码仓库及问题反馈
 - https://developer.hpccube.com/codes/modelzoo/vit_migraphx.git
+
 ## 参考资料
 - https://github.com/WZMIAOMIAO/deep-learning-for-image-processing