diff --git a/modelcenter/DeepCFD/info.yaml b/modelcenter/DeepCFD/info.yaml
index 58d860b183cbc84ed7668764bdba86bcc5bc9f3b..5caf7c863b6627161ca00dbba18589338985f80b 100644
--- a/modelcenter/DeepCFD/info.yaml
+++ b/modelcenter/DeepCFD/info.yaml
@@ -1,31 +1,17 @@
 ---
 Model_Info:
-  name: "DeepCFD"
+  name: "DeepCFD 模型"
   description: "基于DeepCFD模型实现流体绕过任意障碍物的定常流场仿真"
   description_en: "Simulation of steady flow of fluid bypassing any obstacle based on DeepCFD model"
-  icon: "@后续UE统一设计之后，会存到bos上某个位置"
-  from_repo: ""
-
+  icon: "https://ai-studio-static-online.cdn.bcebos.com/f221a878e6c04cf4a71432040d85cc734e5960b6d9e946baad166b6a54ad62fe"
+  from_repo: "https://github.com/zbyandmoon/DeepCFD_with_PaddlePaddle/tree/main/paddle"
 Task:
--
-    tag: "科学计算"
-    tag_en: "Scientific Computing"
-    sub_tag: "计算流体力学"
-    sub_tag_en: "Computational fluid dynamics"
-
-Example:
--
-   tag: "工业/能源"
-   tag_en: "Industrial/Energy"
-   sub_tag: "计算流体力学"
-   sub_tag_en: "Computational fluid dynamics"
-   title: "基于PaddlePaddle的DeepCFD复现"
-   
-   url: https://aistudio.baidu.com/aistudio/projectdetail/4400677?channelType=0&channel=0
-   url_en: https://aistudio.baidu.com/aistudio/projectdetail/4400677?channelType=0&channel=0
-
-Datasets: "Data_DeepCFD"
-Publisher: "Baidu"
+- tag: "科学计算"
+  tag_en: "Scientific Computing"
+  sub_tag: "计算流体力学"
+  sub_tag_en: "Computational Fluid Dynamics"
+Datasets: "deepcfd-data"
+Pulisher: "Baidu"
 License: "apache.2.0"
 IfTraining: 1
 IfOnlineDemo: 1
diff --git a/modelcenter/DeepCFD/introduction.ipynb b/modelcenter/DeepCFD/introduction.ipynb
index 49863ded74959638b5032963aa3f3f258d8366c0..00d3394b5734e93d76ed072b09e6ba72364911e9 100644
--- a/modelcenter/DeepCFD/introduction.ipynb
+++ b/modelcenter/DeepCFD/introduction.ipynb
@@ -40,19 +40,103 @@
     "\n",
     "或https://www.dropbox.com/s/kg0uxjnbhv390jv/Data_DeepCFD.7z?dl=0\n",
     "\n",
+    "本示例数据集已经保存在百度云环境中，可直接使用，在项目clone之后，数据集存储在项目路径下。示意代码为\n",
+    "```\n",
+    "dataset = 'https://deepcfd-data.bj.bcebos.com/dataset.zip'\n",
+    "!wget dataset\n",
+    "```\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "30023dd9-12e2-4006-9f24-26350ba900c0",
+   "metadata": {},
+   "source": [
     "## 3.3 快速开始\n",
     "\n",
     "**step1：克隆本项目**\n",
-    "\n",
-    "搜索DeepCFD_with_PaddlePaddle，选择对应的版本，Fork。\n",
+    "以下方式可以二选一：\n",
+    "* 搜索DeepCFD_with_PaddlePaddle，选择对应的版本，Fork.\n",
     "\n",
     "![fork.png](https://github.com/zbyandmoon/Picture/blob/main/picture_DeepCFD/fork.png?raw=true)\n",
     "\n",
-    "**step2：开始训练**\n",
+    "  选择目标路径，代码如下：\n",
+    "```\n",
+    "% cd ./DeepCFD_with_PaddlePaddle/\n",
+    "```\n",
     "\n",
-    "选择进入终端。\n",
+    "* git clone 项目\n",
+    "  \n",
+    " notebook上命令如下：\n",
+    "```\n",
+    "!git clone https://github.com/zbyandmoon/DeepCFD_with_PaddlePaddle/\n",
+    "%cd ./DeepCFD_with_PaddlePaddle/\n",
+    "```\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b72eba25-7fbb-4b8c-82d9-7940d18c95f2",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "# 默认使用git clone方式，如基于已有的aistudio项目fork，可注释之后的git clone部分\n",
+    "# %cd ~\n",
+    "# %cd ./DeepCFD_with_PaddlePaddle/"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "16c652da-a201-4b9c-9d90-b220c093715d",
+   "metadata": {
+    "scrolled": true,
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# 若选择git clone的方式，则忽略fork部分的操作和脚本，使用如下脚本（默认使用git方式,若repo链接较慢，可多试几次）\n",
+    "%cd ~\n",
+    "!git clone https://github.com/zbyandmoon/DeepCFD_with_PaddlePaddle/\n",
+    "%cd ./DeepCFD_with_PaddlePaddle/"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ca9a2051-78f1-4fe2-b847-73d68a67903d",
+   "metadata": {},
+   "source": [
+    "**step2：数据集加载**\n",
+    "执行以下脚本，将服务器数据下载到工作路径下"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c94c5aed-5da3-45cb-a8b2-2df7cafa925d",
+   "metadata": {
+    "scrolled": true,
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "%cd ./data\n",
+    "!wget https://deepcfd-data.bj.bcebos.com/dataset.zip\n",
+    "!unzip dataset.zip"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2fe5b62e-a3fe-4e51-918d-2f7a13587135",
+   "metadata": {},
+   "source": [
+    "**step3：开始训练**\n",
     "\n",
-    "![click_terminal.png](https://github.com/zbyandmoon/Picture/blob/main/picture_DeepCFD/click_terminal.png?raw=true)\n",
+    "确认在正确路径后，执行如下命令\n",
     "\n",
     "**单卡训练**\n",
     "\n",
@@ -105,9 +189,43 @@
     "        Validation Ux MSE = 169.64230501853814\n",
     "        Validation Uy MSE = 140.46789757680085\n",
     "        Validation p MSE = 2.6092084981627384\n",
-    "```\n",
-    "\n",
-    "**step3：评估模型**\n",
+    "```\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "53bd402d-919a-4dd1-89b6-0e37291e2c9e",
+   "metadata": {
+    "scrolled": true,
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "#单卡\n",
+    "%cd ..\n",
+    "!python train.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fa9f2223-7105-4bb0-a583-180294e5eae8",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "#四卡\n",
+    "# !python -m paddle.distributed.launch --gpus=0,1,2,3 train.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ba4172f6-97c8-4f69-8d99-f24b08bffe60",
+   "metadata": {},
+   "source": [
+    "**step4：评估模型**\n",
     "\n",
     "```python\n",
     "python eval.py\n",
@@ -117,15 +235,201 @@
     "\n",
     "```python\n",
     "Total MSE is 1.895322561264038, Ux MSE is 0.6951090097427368, Uy MSE is 0.21001490950584412, p MSE is 0.9901986718177795\n",
-    "```\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9e2c443f-1f6e-4739-9fbd-c689e74fa5be",
+   "metadata": {
+    "scrolled": true,
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "!python eval.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dfe49b5d-9ab8-4e03-a44d-35389f0fe3f5",
+   "metadata": {},
+   "source": [
+    "**step5：使用预训练模型预测**\n",
+    "\n",
+    "考虑到需要展示流场图像对比结果，在Jupyter notebook环境中运行如下代码进行结果输出。\n",
     "\n",
-    "**step4：使用预训练模型预测**\n",
+    "某个障碍物的流场预测结果展示如下：\n",
     "\n",
-    "考虑到需要展示流场图像对比结果，单独写了一个predict.ipynb来进行模型的验证，需要在Jupyter notebook环境中运行。\n",
+    "![paddle_contour.png](https://github.com/zbyandmoon/Picture/blob/main/picture_DeepCFD/paddle_contour.png?raw=true)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "3e2878e0-12e9-4b2f-a671-7678ff2c17de",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2022-11-21T08:09:49.525111Z",
+     "iopub.status.busy": "2022-11-21T08:09:49.524456Z",
+     "iopub.status.idle": "2022-11-21T08:09:51.006361Z",
+     "shell.execute_reply": "2022-11-21T08:09:51.005357Z",
+     "shell.execute_reply.started": "2022-11-21T08:09:49.525083Z"
+    },
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/__init__.py:107: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working\r\n",
+      "  from collections import MutableMapping\r\n",
+      "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/rcsetup.py:20: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working\r\n",
+      "  from collections import Iterable, Mapping\r\n",
+      "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/colors.py:53: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working\r\n",
+      "  from collections import Sized\r\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "['./config/config.ini']"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# 本文件用于使用训练好的模型预测流场\n",
+    "import pickle\n",
+    "from utils.train_functions import *\n",
+    "from utils.functions import *\n",
+    "from model.UNetEx import UNetEx\n",
+    "import configparser\n",
+    "\n",
+    "config = configparser.ConfigParser()\n",
+    "config.read(\"./config/config.ini\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "1399dc25-3dc0-4660-957d-123b85deb9a5",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2022-11-21T08:09:58.842591Z",
+     "iopub.status.busy": "2022-11-21T08:09:58.841557Z",
+     "iopub.status.idle": "2022-11-21T08:10:00.514878Z",
+     "shell.execute_reply": "2022-11-21T08:10:00.513944Z",
+     "shell.execute_reply.started": "2022-11-21T08:09:58.842557Z"
+    },
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "W1121 16:09:58.850512   192 gpu_resources.cc:61] Please NOTE: device: 0, GPU Compute Capability: 7.0, Driver API Version: 11.2, Runtime API Version: 11.2\r\n",
+      "W1121 16:09:58.854226   192 gpu_resources.cc:91] device: 0, cuDNN Version: 8.2.\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 设置卷积核大小\n",
+    "kernel_size = int(config[\"net_parameter\"][\"kernel_size\"])\n",
+    "# 设置卷积层channel数目\n",
+    "filters = [int(i) for i in config[\"net_parameter\"][\"filters\"].split(\",\")]\n",
+    "# 设置batch_norm和weight_norm\n",
+    "bn = bool(int(config[\"net_parameter\"][\"batch_norm\"]))\n",
+    "wn = bool(int(config[\"net_parameter\"][\"weight_norm\"]))\n",
+    "# 构建模型\n",
+    "model = UNetEx(3, 3, filters=filters, kernel_size=kernel_size, batch_norm=bn, weight_norm=wn)\n",
+    "# 加载模型参数\n",
+    "model.set_state_dict(\n",
+    "        paddle.load(os.path.join(config[\"path\"][\"save_path\"], config[\"path\"][\"model_name\"])))\n",
+    "\n",
+    "# 加载数据集并处理\n",
+    "x = pickle.load(open(os.path.join(config[\"path\"][\"data_path\"], \"dataX.pkl\"), \"rb\"))\n",
+    "y = pickle.load(open(os.path.join(config[\"path\"][\"data_path\"], \"dataY.pkl\"), \"rb\"))\n",
+    "x = paddle.to_tensor(x, dtype=\"float32\")\n",
+    "y = paddle.to_tensor(y, dtype=\"float32\")\n",
+    "y_trans = paddle.transpose(y, perm=[0, 2, 3, 1])\n",
+    "channels_weights = paddle.reshape(\n",
+    "    paddle.sqrt(paddle.mean(paddle.transpose(y, perm=[0, 2, 3, 1]).reshape((981 * 172 * 79, 3)) ** 2, axis=0)),\n",
+    "    shape=[1, -1, 1, 1])\n",
+    "\n",
+    "# 按7：3的比例分割数据集，7为训练集，3为测试集\n",
+    "train_data, test_data = split_tensors(x, y, ratio=float(config[\"hyperparameter\"][\"train_test_ratio\"]))\n",
+    "\n",
+    "train_dataset, test_dataset = paddle.io.TensorDataset([train_data[0], train_data[1]]), paddle.io.TensorDataset(\n",
+    "    [test_data[0], test_data[1]])\n",
+    "test_x, test_y = test_dataset[:]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "b94bd14f-620e-4116-9ce2-335c1d81855f",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2022-11-21T08:10:02.981402Z",
+     "iopub.status.busy": "2022-11-21T08:10:02.980567Z",
+     "iopub.status.idle": "2022-11-21T08:10:05.887726Z",
+     "shell.execute_reply": "2022-11-21T08:10:05.886982Z",
+     "shell.execute_reply.started": "2022-11-21T08:10:02.981371Z"
+    },
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/cbook/__init__.py:2349: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working\r\n",
+      "  if isinstance(obj, collections.Iterator):\r\n",
+      "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/cbook/__init__.py:2366: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working\r\n",
+      "  return list(data) if isinstance(data, collections.MappingView) else data\r\n",
+      "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/colors.py:101: DeprecationWarning: np.asscalar(a) is deprecated since NumPy v1.16, use a.item() instead\r\n",
+      "  ret = np.asscalar(ex)\r\n",
+      "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/image.py:425: DeprecationWarning: np.asscalar(a) is deprecated since NumPy v1.16, use a.item() instead\r\n",
+      "  a_min = np.asscalar(a_min.astype(scaled_dtype))\r\n",
+      "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/image.py:426: DeprecationWarning: np.asscalar(a) is deprecated since NumPy v1.16, use a.item() instead\r\n",
+      "  a_max = np.asscalar(a_max.astype(scaled_dtype))\r\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1500x1000 with 18 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# 测试训练模型\n",
+    "out = model(test_x)\n",
+    "# 计算残差\n",
+    "error = paddle.abs(out.cpu() - test_y.cpu())\n",
+    "# 作出CFD和CNN的计算结果对比图以及对应的残差图(s可修改)\n",
+    "s = 0\n",
+    "visualize(test_y.detach().numpy(), out.detach().numpy(), error.detach().numpy(), s)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f70e1450-71c9-4f01-892f-c03cdf876747",
+   "metadata": {},
+   "source": [
     "\n",
-    "运行predict.ipynb,某个障碍物的流场预测结果展示如下：\n",
     "\n",
-    "![paddle_contour.png](https://github.com/zbyandmoon/Picture/blob/main/picture_DeepCFD/paddle_contour.png?raw=true)\n",
     "\n",
     "## 3.4 代码结构与参数说明\n",
     "\n",
@@ -218,10 +522,18 @@
     "\n",
     "AI Studio: https://aistudio.baidu.com/aistudio/projectdetail/4400677?contributionType=1\n",
     "\n",
-    "github: https://github.com/zbyandmoon/DeepCFD_with_PaddlePaddle/tree/main/paddle\n",
-    "\n",
-    "\n"
+    "github: https://github.com/zbyandmoon/DeepCFD_with_PaddlePaddle/tree/main/paddle"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c2f32b16-1b51-4e76-a1a9-a80c8c3b899e",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
diff --git a/modelcenter/ERNIE-3.0 Zeus/info.yaml b/modelcenter/ERNIE-3.0 Zeus/info.yaml
index 022b08fec49f83c2c0e8d6420f5c97267ce96e32..9253bfe47b8f323ae05fd6fb2643bc600c3cca87 100644
--- a/modelcenter/ERNIE-3.0 Zeus/info.yaml	
+++ b/modelcenter/ERNIE-3.0 Zeus/info.yaml	
@@ -3,15 +3,15 @@ Model_Info:
   name: "ERNIE 3.0 Zeus"
   description: "文心任务知识增强大模型"
   description_en: "Large model of task knowledge enhancement"
-  icon: "@后续UE统一设计之后，会存到bos上某个位置"
+  icon: "https://user-images.githubusercontent.com/23690325/203383033-946fdbd7-3756-49cd-aedb-66d000020964.jpg"
   from_repo: ""
 Task:
 - tag_en: "Wenxin Big Model"
   tag: "文心大模型"
-  sub_tag_en: "ERNIE-3.0 Zeus"
+  sub_tag_en: "Knowledge Enhancement"
   sub_tag: "任务知识增强"
 Datasets: ""
 Publisher: "Baidu"
 License: "apache.2.0"
-IfTraining: 1
+IfTraining: 0
 IfOnlineDemo: 1
diff --git a/modelcenter/ERNIE-Layout/info.yaml b/modelcenter/ERNIE-Layout/info.yaml
index ba7474feb19259323474aba1187efaeab7410c6c..ea730fc53f5999119f4b6cf6af849f54e0653396 100644
--- a/modelcenter/ERNIE-Layout/info.yaml
+++ b/modelcenter/ERNIE-Layout/info.yaml
@@ -10,7 +10,7 @@ Task:
 - tag: 文心大模型
   tag_en: Wenxin Big Models
   sub_tag: 文档分析
-  sub_tag_en: Ernie-Layout
+  sub_tag_en: Document Analysis
 Example:
 - tag: 互联网
   tag_en: Internet
@@ -19,7 +19,7 @@ Example:
   title: PaddleNLP文档智能技术重磅升级，动手搭建端到端文档抽取问答模型
   title_en: PaddleNLP document intelligent technology has been heavily upgraded, and the end-to-end document extraction question and answer model has been set up
   url: https://aistudio.baidu.com/aistudio/projectdetail/4881278?channelType=0&channel=0
-  url_en: https://aistudio.baidu.com/aistudio/projectdetail/4881278?channelType=0&channel=0
+  url_en: 
 Datasets: FUNSD, XFUND-ZH, DocVQA-ZH, RVL-CDIP (sampled)
 Publisher: Baidu
 License: Apache 2.0
diff --git a/modelcenter/ERNIE-ViLG/info.yaml b/modelcenter/ERNIE-ViLG/info.yaml
index e8c1bfb16efc241b86b1eeff68caae73aed286e7..5e706a61d5d326e4eda1c180fd397d018a7f6bab 100644
--- a/modelcenter/ERNIE-ViLG/info.yaml
+++ b/modelcenter/ERNIE-ViLG/info.yaml
@@ -2,17 +2,16 @@
 Model_Info:
   name: "ERNIE-ViLG"
   description: "文心知识增强跨模态图文生成大模型"
-  description_en: "Large model of text centered knowledge enhanced cross modal text\
-    \ generation"
-  icon: "@后续UE统一设计之后，会存到bos上某个位置"
+  description_en: "Large model of text centered knowledge enhanced cross modal text generation"
+  icon: "https://user-images.githubusercontent.com/23690325/203378609-e7ab3c5e-fb70-48b8-a669-47d2d866abee.jpg"
   from_repo: ""
 Task:
 - tag_en: "Wenxin Big Model"
   tag: "文心大模型"
-  sub_tag_en: "ERNIE-ViLG"
+  sub_tag_en: "Text2Image"
   sub_tag: "图文生成"
 Datasets: ""
 Publisher: "Baidu"
 License: "apache.2.0"
-IfTraining: 1
+IfTraining: 0
 IfOnlineDemo: 1
diff --git a/modelcenter/PINN-CFD/info.yaml b/modelcenter/PINN-CFD/info.yaml
index 6b703d8541b8878639190539a3bb9765fcdf6ce2..7dd5258cb0067b3c954f5f866e09ff7facd2b220 100644
--- a/modelcenter/PINN-CFD/info.yaml
+++ b/modelcenter/PINN-CFD/info.yaml
@@ -1,29 +1,23 @@
 ---
 Model_Info:
   name: "PINN_CFD"
-  description: "基于PINN的2D圆柱绕流模型设计及仿真验证"
+  description: "2D圆柱绕流模型设计及仿真验证"
   description_en: "Design and simulation of 2D cylinder flow model"
-  icon: "@后续UE统一设计之后，会存到bos上某个位置"
+  icon: "https://ai-studio-static-online.cdn.bcebos.com/18932a98076948e7bf26c79c60015250af10b7aa3d114283ae53536189697a56"
   from_repo: "PaddleScience"
 Task:
--
-    tag: "科学计算"
-    tag_en: "Scientific Computing"
-    sub_tag: "2D圆柱绕流"
-    sub_tag_en: "2D cylinder flow"
-
+- tag: "科学计算"
+  tag_en: "Scientific Computing"
+  sub_tag: "圆柱绕流"
+  sub_tag_en: "Cylinder Flow"
 Example:
--
-   tag: "工业/能源"
-   tag_en: "Industrial/Energy"
-   sub_tag: "圆柱绕流"
-   sub_tag_en: "Cylinder Flow"
-   title: "基于AI求解2D非定常圆柱绕流，真的很流体"
-   
-   url: https://aistudio.baidu.com/aistudio/projectdetail/4178470?contributionType=1
-   url_en: https://aistudio.baidu.com/aistudio/projectdetail/4178470?contributionType=1
-
-   
+- tag: "工业/能源"
+  tag_en: "Industrial/Energy"
+  sub_tag: "圆柱绕流"
+  sub_tag_en: "Cylinder Flow"
+  title: "基于AI求解2D非定常圆柱绕流，真的很流体"
+  url: https://aistudio.baidu.com/aistudio/projectdetail/4178470?contributionType=1
+  url_en: 
 Datasets: "cylinder2D_continuous"
 Publisher: "Baidu"
 License: "apache.2.0"
diff --git a/modelcenter/PP-ASR/info.yaml b/modelcenter/PP-ASR/info.yaml
index abf761fbc6dda61a2dc1386e5cfc801847f27ebb..54c02736bbc3b6abf4b8cdb8441f0be999697eee 100755
--- a/modelcenter/PP-ASR/info.yaml
+++ b/modelcenter/PP-ASR/info.yaml
@@ -7,11 +7,10 @@ Model_Info:
    icon: "https://paddlespeech.bj.bcebos.com/demos/speech_web/e1cbf586e8da856faf4b4b4a6d236894.jpg"
    from_repo: "PaddleSpeech"
 Task:
--
-    tag: "智能语音"
-    tag_en: "Speech&Audio"
-    sub_tag: "语音识别"
-    sub_tag_en: "Speech Recognition"
+- tag: "智能语音"
+  tag_en: "Speech&Audio"
+  sub_tag: "语音识别"
+  sub_tag_en: "Speech Recognition"
 Example:
 - tag: 互联网
   tag_en: Internet
diff --git a/modelcenter/PP-HelixFold/info.yaml b/modelcenter/PP-HelixFold/info.yaml
index 1bb77937f91af4db79aeb71dfe1068164d601b79..5c6a841a3408fe4d5633184f74772b69069aedfb 100644
--- a/modelcenter/PP-HelixFold/info.yaml
+++ b/modelcenter/PP-HelixFold/info.yaml
@@ -3,7 +3,7 @@ Model_Info:
   name: "PP-HelixFold"
   description: "适配国产软硬件的蛋白质结构预测模型"
   description_en: "A protein structure prediction model adapted to domestic software and hardware"
-  icon: ""
+  icon: "https://user-images.githubusercontent.com/23690325/203377707-5823cb12-9cfe-4d07-ba97-e1acd3f339b7.jpg"
   from_repo: "PaddleHelix"
 Task:
 - tag_en: "Scientific Computing"
diff --git a/modelcenter/PP-HumanV2/info.yaml b/modelcenter/PP-HumanV2/info.yaml
index 4014f390329fafc9f5fc6e5655ddf6844a50b5aa..b502bfb105d0e28346e4269f64fa5a4e3f0a9ff9 100644
--- a/modelcenter/PP-HumanV2/info.yaml
+++ b/modelcenter/PP-HumanV2/info.yaml
@@ -19,19 +19,19 @@ Task:
   sub_tag_en: "Behavior Recognition"
   sub_tag: "行为识别"
 Example:
-- tag_en: "Smart Security"
+- tag_en: "Intelligent Security"  
   tag: "智慧安防"
   sub_tag_en: "Fall detection"
   title: "基于PP-Human v2的摔倒检测"
   sub_tag: "摔倒检测"
   url: "https://aistudio.baidu.com/aistudio/projectdetail/4606001"
-- tag_en: "Smart Security"
+- tag_en: "Intelligent Security"
   tag: "智慧安防"
   sub_tag_en: "Fight identification"
   title: "基于PP-Human的打架识别"
   sub_tag: "打架识别"
   url: "https://aistudio.baidu.com/aistudio/projectdetail/4086987?contributionType=1"
-- tag_en: "Smart Security"
+- tag_en: "Intelligent Security"
   tag: "智慧安防"
   sub_tag_en: "Access management"
   title: "基于PP-Human的来客分析案例教程"
diff --git a/modelcenter/PP-LCNet/info.yaml b/modelcenter/PP-LCNet/info.yaml
index acd4a3b718d302cd85ab7ea1992a85fac41e9788..fabb10b1d2cb582c0509462818c6b4a0ccc1b3a4 100644
--- a/modelcenter/PP-LCNet/info.yaml
+++ b/modelcenter/PP-LCNet/info.yaml
@@ -11,9 +11,9 @@ Task:
   sub_tag_en: "Image Classification"
   sub_tag: "图像分类"
 Example:
-- tag_en: "Smart Security"
+- tag_en: "Intelligent Security"
   tag: "智慧安防"
-  sub_tag_en: "Personnel access management"
+  sub_tag_en: "Personnel Access Management"
   title: "人员进出视频管理方案详解"
   sub_tag: "人员进出管理"
   url: "https://aistudio.baidu.com/aistudio/projectdetail/4094475"
diff --git a/modelcenter/PP-LiteSeg/info.yml b/modelcenter/PP-LiteSeg/info.yaml
similarity index 100%
rename from modelcenter/PP-LiteSeg/info.yml
rename to modelcenter/PP-LiteSeg/info.yaml
diff --git a/modelcenter/PP-MSVSR/info.yaml b/modelcenter/PP-MSVSR/info.yaml
index d4a5b12a84b7bbae145821d35d16d0041cfe5f6b..c97e3dc7a1ef590a6a4e7a4cb487d01d7d130c18 100644
--- a/modelcenter/PP-MSVSR/info.yaml
+++ b/modelcenter/PP-MSVSR/info.yaml
@@ -3,7 +3,7 @@ Model_Info:
   name: "PP-MSVSR"
   description: "视频超分"
   description_en: "video super resolution"
-  icon: "@后续UE统一设计之后，会存到bos上某个位置"
+  icon: "https://user-images.githubusercontent.com/23690325/203386955-a86f6d3e-9ca1-4c56-9a7d-07be5acf9076.png"
   from_repo: "PaddleGAN"
 Task:
 - tag_en: "Computer Vision"
diff --git a/modelcenter/PP-Matting/info.yaml b/modelcenter/PP-Matting/info.yaml
index 7ffac6d494692107b740699b1d6779b9fd64055c..4c7846dcfcd762842662f4a00e917fc6a43db61d 100644
--- a/modelcenter/PP-Matting/info.yaml
+++ b/modelcenter/PP-Matting/info.yaml
@@ -10,15 +10,12 @@ Task:
   tag: "计算机视觉"
   sub_tag_en: "Image Matting"
   sub_tag: "图像抠图"
-
 - tag_en: "Computer Vision"
   tag: "计算机视觉"
   sub_tag_en: "Figure Cutout"
-  sub_tag: "人像抠图"
-  
+  sub_tag: "人像抠图" 
 Example:
-- 
-  tag: "互联网"
+- tag: "互联网"
   sub_tag: "人像抠图"
   tag_en: "Internet"
   sub_tag_en: "Figure Cutout"
diff --git a/modelcenter/PP-OCRv2/info.yaml b/modelcenter/PP-OCRv2/info.yaml
index e029615dd2f923d71de32369f1552d0bd5d4cc18..ec98e6635f045fc665cf424d240a6f05c4280155 100644
--- a/modelcenter/PP-OCRv2/info.yaml
+++ b/modelcenter/PP-OCRv2/info.yaml
@@ -6,21 +6,15 @@ Model_Info:
    icon: "@后续UE统一设计之后，会存到bos上某个位置"
    from_repo: "PaddleOCR"
 Task:
-   - tag_en: "Computer Vision"
-     tag: "计算机视觉"
-     sub_tag_en: "Text Detection"
-     sub_tag: "文字检测"
-   - tag_en: "Computer Vision"
-     tag: "计算机视觉"
-     sub_tag_en: "Character Recognition"
-     sub_tag: "文字识别"
-   - tag_en: "Computer Vision"
-     tag: "计算机视觉"
-     sub_tag_en: "Optical Character Recognition"
-     sub_tag: "OCR"
-
+- tag_en: "Computer Vision"
+  tag: "计算机视觉"
+  sub_tag_en: "Text Detection"
+  sub_tag: "文字检测"
+- tag_en: "Computer Vision"
+  tag: "计算机视觉"
+  sub_tag_en: "Character Recognition"
+  sub_tag: "文字识别"
 Example:
-
 Datasets: "ICDAR 2015, ICDAR2019-LSVT,ICDAR2017-RCTW-17,Total-Text,ICDAR2019-ArT"
 Publisher: "Baidu"
 License: "apache.2.0"
diff --git a/modelcenter/PP-StructureV2/info.yaml b/modelcenter/PP-StructureV2/info.yaml
index 465b22246624543b3b0b4d37fe53089c5f1c9698..aa091cf1d23261a44a764c568b174c445bf19df4 100644
--- a/modelcenter/PP-StructureV2/info.yaml
+++ b/modelcenter/PP-StructureV2/info.yaml
@@ -37,7 +37,7 @@ Example:
      url_en: "https://aistudio.baidu.com/aistudio/projectdetail/4823162?channelType=0&channel=0"
      tag_en: "Intelligent Finance"
      tag: "智慧金融"
-     sub_tag_en: "Invoice key information extraction"
+     sub_tag_en: "Invoice Key Information Extraction"
      sub_tag: "发票关键信息抽取"
 Datasets: "ICDAR 2015, ICDAR2019-LSVT,ICDAR2017-RCTW-17,Total-Text,ICDAR2019-ArT"
 Publisher: "Baidu"
diff --git a/modelcenter/PP-TinyPose/info.yaml b/modelcenter/PP-TinyPose/info.yaml
index 5436b5c870a67fa659688f283bc4b9ffc651bfe0..65e65c202a223cee272317278334855c1e66fb6b 100644
--- a/modelcenter/PP-TinyPose/info.yaml
+++ b/modelcenter/PP-TinyPose/info.yaml
@@ -17,7 +17,7 @@ Task:
 Example:
 - tag_en: "Intelligent Sport"
   tag: "智慧体育"
-  sub_tag_en: "motion classification"
+  sub_tag_en: "Motion Classification"
   title: "智能健身动作识别"
   sub_tag: "动作分类"
   url: "https://aistudio.baidu.com/aistudio/projectdetail/4385813"
diff --git a/modelcenter/PP-YOLOv2/info.yaml b/modelcenter/PP-YOLOv2/info.yaml
index 5918054fbc5ec1a4ddd6e1b4c05e1cb18b7fb459..5f3f15af5b28a677c5d793ab23052cec9715c707 100644
--- a/modelcenter/PP-YOLOv2/info.yaml
+++ b/modelcenter/PP-YOLOv2/info.yaml
@@ -13,7 +13,7 @@ Task:
   sub_tag_en: Object Detection 
 Example:
 - tag: 智慧安防
-  tag_en: Smart Security
+  tag_en: Intelligent Security
   sub_tag: 火灾/烟雾检测
   sub_tag_en: Smoke Detction
   title: 基于PP-YOLOv2的火灾/烟雾检测
diff --git a/modelcenter/VIMER-CAE/info.yaml b/modelcenter/VIMER-CAE/info.yaml
index 9a16662435757dfe5e16334613daa855f08780a8..3d41c19e3d5cad2110626471c0ea13f76dee0d86 100644
--- a/modelcenter/VIMER-CAE/info.yaml
+++ b/modelcenter/VIMER-CAE/info.yaml
@@ -3,16 +3,13 @@ Model_Info:
    name: "VIMER-CAE1.0"
    description: "自监督预训练方法"
    description_en: "masked image modeling approach for self-supervised representation pretraining"
-   icon: ""
+   icon: "https://user-images.githubusercontent.com/23690325/203380905-1bb0b00b-7c40-4044-a248-492436199b7d.jpg"
    from_repo: "VIMER"
-
 Task:
-   - tag_en: "Wenxin Big Models"
-     tag: "文心大模型"
-     sub_tag_en: "VIMER-CAE"
-     sub_tag: "自监督预训练"
-
-     
+-  tag_en: "Wenxin Big Models"
+   tag: "文心大模型"
+   sub_tag_en: "VIMER-CAE"
+   sub_tag: "自监督预训练"    
 Datasets: "ImageNet1K, MSCOCO, ADE20K"
 Publisher: "Baidu"
 License: "apache.2.0"
diff --git a/modelcenter/VIMER-StrucTexT/info.yaml b/modelcenter/VIMER-StrucTexT/info.yaml
index 9eb2783a8e23eb629b484be2fe4949a7a496e1f7..a0a24da2ff145da86c647b68889bf413d334c968 100644
--- a/modelcenter/VIMER-StrucTexT/info.yaml
+++ b/modelcenter/VIMER-StrucTexT/info.yaml
@@ -3,11 +3,11 @@ Model_Info:
   name: "VIMER-StrucTexT1.0"
   description: "结构化文本理解"
   description_en: "Structured Text Understanding"
-  icon: ""
+  icon: "https://user-images.githubusercontent.com/23690325/203381082-18717d2e-2080-4233-91c5-84f48e1fdef2.png"
   from_repo: "VIMER"
 Task:
 - tag_en: "Wenxin Big Models"
-  tag: "文心CV大模型"
+  tag: "文心大模型"
   sub_tag_en: "Multi-modal Information Extraction"
   sub_tag: "多模态信息提取"
 Datasets: "SROIE,FUNSD,EPHOIE,XFUND"
diff --git a/modelcenter/VIMER-UFO/info.yaml b/modelcenter/VIMER-UFO/info.yaml
index a22dcbe0181cbaaeb6bb0c6e8266628fc25cc6a6..da5ef61277e65fdb36181c2df8529d51f694c8c8 100644
--- a/modelcenter/VIMER-UFO/info.yaml
+++ b/modelcenter/VIMER-UFO/info.yaml
@@ -3,17 +3,14 @@ Model_Info:
    name: "VIMER-UFO2.0"
    description: "视觉多任务大模型"
    description_en: "UFO:Unified Feature Optimization"
-   icon: ""
+   icon: "https://user-images.githubusercontent.com/23690325/203381746-0fb8e96f-f13d-4106-8d8f-be4f6fe29834.jpg"
    from_repo: "VIMER"
-
 Task:
    - tag_en: "Wenxin Big Models"
-     tag: "文心CV大模型"
+     tag: "文心大模型"
      sub_tag_en: "VIMER-UFO"
-     sub_tag: "多任务大一统模型"
-
-     
-Datasets: "More than 20 datasets details refs to github repo"
+     sub_tag: "多任务大一统模型"     
+Datasets: ""
 Publisher: "Baidu"
 License: "apache.2.0"
 Paper:
diff --git a/modelcenter/guide_cn.md b/modelcenter/guide_cn.md
index 7e0f420bc91e20ad4d33916e3c7a032ce13a9447..9c57794684b6478f12bf771e18aac66ffeeac788 100644
--- a/modelcenter/guide_cn.md
+++ b/modelcenter/guide_cn.md
@@ -1,30 +1,30 @@
-# 1. 模型中心
+# 1. 飞桨模型库
 
 ## 1.1 模型介绍
-本部分介绍每个模型的基本信息，包括模型背景、应用场景、快速开始及模型原理等，帮助大家全方位了解模型。点击跳转【模型介绍】页面
+本部分介绍每个模型的基本信息，包括模型背景、应用场景、快速开始及模型原理等，帮助大家全方位了解模型。
 
 ## 1.2 模型空间
-本部分提供在线体验空间的所有代码，可实现基于模型的可视化demo APP。您可以对代码进行下载、预览和编辑（暂未上线），也可利用Streamlit和Gradio两种高效的方法，为模型打造炫酷的showcase效果。点击跳转【模型空间】页面
+本部分提供在线体验空间的所有代码，可实现基于模型的可视化demo APP。您可以对代码进行下载、预览，也可利用Streamlit和Gradio两种高效的方法，为模型打造炫酷的showcase效果。
 
 ## 1.3 模型下载
-本部分提供模型各任务场景下的推理模型文件和预训练模型文件，您可以直接获取、下载体验。点击跳转【模型下载】页面
+本部分提供模型各任务场景下的推理模型文件和预训练模型文件，您可以直接获取、下载体验。
 
 ## 1.4 模型Benchmark
-本部分提供模型的训练和推理Benchmark，包括软硬件环境、数据集、训练和推理指标效果等评估数据。点击跳转【模型Benchmark】页面
+本部分提供模型的训练和推理Benchmark，包括软硬件环境、数据集、训练和推理指标效果等评估数据。
 
 ## 1.5 模型范例
-本部分提供模型相关的产业范例项目，每个范例均来源于真实业务场景，通过完整的代码实现，提供从数据准备到模型部署的全流程方案。您可以点击【运行一下】，感受模型实际的落地效果。点击跳转【模型范例】页面
+本部分提供模型相关的产业范例项目，每个范例均来源于真实业务场景，通过完整的代码实现，提供从数据准备到模型部署的全流程方案。您可以点击【运行一下】，感受模型实际的落地效果。
 
 
-#2. 快速体验
+# 2. 快速体验
 
-## 2.1 在AI Studio Notebook中打开
+## 2.1 学习在 AI Studio 深度学习实训平台
 
 此方式可直接跳转到AI Studio对应的模型项目页面。登陆后，您可以直接选择机器资源并运行，文档和代码会全部复制到项目中，欢迎您在线体验。
 
 备注：AI Studio是基于飞桨的人工智能学习与实训社区，为开发者提供高效易用的学习和开发环境、丰富的体系化课程、海量开源实践项目和高价值的AI竞赛，并提供教育版支撑高校和机构老师轻松实现AI教学，助力深度学习人才培养。
 
-## 2.2 在BML Notebook中打开
+## 2.2 实践在 BML 产业 AI 中台
 
 此方式可直接跳转到BML AI中台对应的模型项目页面。登陆后，您可以直接选择机器资源并运行，文档和代码会全部复制到项目中，欢迎您在线体验。
 
@@ -35,3 +35,6 @@
 此方式可以直接将模型代码压缩包到本地，在您自己的环境中使用和体验。需要注意的是，您需要提前安装好飞桨框架基础环境，安装链接见：https://www.paddlepaddle.org.cn/install/quick?docurl=/documentation/docs/zh/develop/install/pip/windows-pip.html
 
 备注：压缩包内不包含模型训练文件和模型推理文件
+
+# 3. 欢迎开发者参与共建
+所有的源文件均开源在飞桨产业级模型库（[Models in GitHub](https://github.com/PaddlePaddle/models)）,（[Models in Gitee](https://gitee.com/PaddlePaddle/models)）,也欢迎广大开发者参与共建。
diff --git a/modelcenter/guide_en.md b/modelcenter/guide_en.md
index e8ec4f82e640ff5fc6e6a5f60fbbd884ba69cd26..35def47b4d77dfa77607143a5e98908c5c5c109a 100644
--- a/modelcenter/guide_en.md
+++ b/modelcenter/guide_en.md
@@ -1,3 +1,5 @@
-# 1. ModelCenter
+# 1. Models
 
 # 2. QuickStart
+
+# 3. Welcome to contribute