Update README.md

README.md

@@ -26,7 +26,7 @@ DenseNet的核心组件为“Dense Block”，如图所示，由Dense connectivi
 git clone --recursive http://developer.hpccube.com/codes/modelzoo/densenet121_mmcv.git
 docker pull image.sourcefind.cn:5000/dcu/admin/base/pytorch:1.10.0-centos7.6-dtk-22.10.1-py37-latest
 # <your IMAGE ID>用以上拉取的docker的镜像ID替换
-docker run --shm-size 10g --network=host --name=nit-pytorch --privileged --device=/dev/kfd --device=/dev/dri --group-add video --cap-add=SYS_PTRACE --security-opt seccomp=unconfined -v $PWD/densenet121_mmcv:/home/densenet121_mmcv -it <your IMAGE ID> bash
+docker run --shm-size 10g --network=host --name=densenet121 --privileged --device=/dev/kfd --device=/dev/dri --group-add video --cap-add=SYS_PTRACE --security-opt seccomp=unconfined -v $PWD/densenet121_mmcv:/home/densenet121_mmcv -it <your IMAGE ID> bash
 
 cd densenet121_mmcv/mmclassification-mmcv
 pip install -r requirements.txt
@@ -37,7 +37,7 @@ pip install -r requirements.txt
 ```plaintext
 cd densenet121_mmcv/docker
 docker build --no-cache -t densenet121_mmcv:latest .
-docker run --rm --shm-size 10g --network=host --name=megatron --privileged --device=/dev/kfd --device=/dev/dri --group-add video --cap-add=SYS_PTRACE --security-opt seccomp=unconfined -v $PWD/../../densenet121_mmcv:/home/densenet121_mmcv -it megatron bash
+docker run --rm --shm-size 10g --network=host --name=densenet121 --privileged --device=/dev/kfd --device=/dev/dri --group-add video --cap-add=SYS_PTRACE --security-opt seccomp=unconfined -v $PWD/../../densenet121_mmcv:/home/densenet121_mmcv -it <your IMAGE ID> bash
 # 若遇到Dockerfile启动的方式安装环境需要长时间等待，可注释掉里面的pip安装，启动容器后再安装python库：pip install -r requirements.txt
 ```
 
@@ -71,9 +71,15 @@ pip install -r requirements.txt
 替换ImageNet数据集中的val目录，处理后的数据结构如下：
 
 ```
-├── meta
-├── train
-├── val
+data
+    ├──imagenet
+        ├── meta
+            ├──val.txt
+            ├──train.txt
+            ...
+        ├── train
+        ├── val
+  
 ```
 
 ## 训练
@@ -84,7 +90,11 @@ pip install -r requirements.txt
 
     ./densenet121.sh
 
-## 精度
+## result
+
+![img](https://developer.hpccube.com/codes/modelzoo/vit_pytorch/-/raw/master/image/README/1695381570003.png)
+
+### 精度
 
 测试数据使用的是ImageNet数据集，使用的加速卡是DCU Z100L。
 
@@ -92,9 +102,6 @@ pip install -r requirements.txt
 | :--: | :-----------------------: |
 |  8   | top1:0.74044;top5:0.91672 |
 
-## result
-
-![img](https://developer.hpccube.com/codes/modelzoo/vit_pytorch/-/raw/master/image/README/1695381570003.png)
 
 ## 应用场景
 
@@ -106,10 +113,10 @@ pip install -r requirements.txt
 
 制造，能源，交通，网安
 
-### 源码仓库及问题反馈
+## 源码仓库及问题反馈
 
 http://developer.hpccube.com/codes/modelzoo/densenet121_mmcv.git
 
-### 参考
+## 参考资料
 
-https://github.com/open-mmlab/mmpretrain
\ No newline at end of file
+https://github.com/open-mmlab/mmpretrain