diff --git a/docker/Dockerfile b/docker/Dockerfile
index dbd926000e011d01062f89c0cabbf8d1203013c9..b24faf74922a27ec2ecea7a64d2f6333cf4a2cc0 100644
--- a/docker/Dockerfile
+++ b/docker/Dockerfile
@@ -20,7 +20,7 @@ RUN rm /etc/apt/sources.list.d/cuda.list \
 #    pip config set global.index-url https://pypi.tuna.tsinghua.edu.cn/simple
 
 RUN apt-get update \
-    && apt-get install -y ffmpeg libsm6 libxext6 git ninja-build libglib2.0-0 libsm6 libxrender-dev libxext6 \
+    && apt-get install -y ffmpeg libsm6 libxext6 git ninja-build libglib2.0-0 libxrender-dev \
     && apt-get clean \
     && rm -rf /var/lib/apt/lists/*
 
diff --git a/docker/Dockerfile_deployment b/docker/Dockerfile_deployment
new file mode 100644
index 0000000000000000000000000000000000000000..644c590e3c82799207debb8c9e6a058b9b5abf9d
--- /dev/null
+++ b/docker/Dockerfile_deployment
@@ -0,0 +1,60 @@
+FROM nvcr.io/nvidia/pytorch:22.04-py3
+
+WORKDIR /openmmlab
+ARG ONNXRUNTIME_VERSION=1.8.1
+ENV DEBIAN_FRONTEND=noninteractive \
+    APT_KEY_DONT_WARN_ON_DANGEROUS_USAGE=DontWarn \
+    FORCE_CUDA="1"
+
+RUN apt-key del 7fa2af80 \
+    && apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/3bf863cc.pub \
+    && apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64/7fa2af80.pub
+
+# (Optional)
+# RUN sed -i 's/http:\/\/archive.ubuntu.com\/ubuntu\//http:\/\/mirrors.aliyun.com\/ubuntu\//g' /etc/apt/sources.list \
+# && pip config set global.index-url https://pypi.tuna.tsinghua.edu.cn/simple
+
+RUN apt-get update \
+    && apt-get install -y ffmpeg git libgl1-mesa-glx libopencv-dev \
+    libsm6 libspdlog-dev libssl-dev ninja-build libxext6 libxrender-dev \
+    libglib2.0-0 vim wget --no-install-recommends \
+    && apt-get clean \
+    && rm -rf /var/lib/apt/lists/*
+
+# get onnxruntime
+RUN wget -q https://github.com/microsoft/onnxruntime/releases/download/v${ONNXRUNTIME_VERSION}/onnxruntime-linux-x64-${ONNXRUNTIME_VERSION}.tgz \
+    && tar -zxvf onnxruntime-linux-x64-${ONNXRUNTIME_VERSION}.tgz \
+    && pip install --no-cache-dir onnxruntime-gpu==${ONNXRUNTIME_VERSION} \
+    && pip install pycuda
+
+
+# Install OPENMIM MMENGINE MMCV MMDET
+RUN pip install --no-cache-dir openmim \
+    && mim install --no-cache-dir "mmengine>=0.3.0" "mmcv>=2.0.0rc1,<2.1.0" "mmdet>=3.0.0rc2,<3.1.0"
+
+# Install MMYOLO
+RUN git clone https://github.com/open-mmlab/mmyolo.git -b dev mmyolo \
+    && cd mmyolo \
+    && mim install --no-cache-dir -e . \
+    && cd ..
+
+# Install MMDEPLOY
+ENV ONNXRUNTIME_DIR=/openmmlab/onnxruntime-linux-x64-${ONNXRUNTIME_VERSION} \
+    TENSORRT_DIR=/usr/lib/x86_64-linux-gnu \
+    CUDNN_DIR=/usr/lib/x86_64-linux-gnu \
+    BACKUP_LD_LIBRARY_PATH=$LD_LIBRARY_PATH \
+    LD_LIBRARY_PATH=$ONNXRUNTIME_DIR/lib:$TENSORRT_DIR/lib:$CUDNN_DIR/lib64:$BACKUP_LD_LIBRARY_PATH
+
+RUN git clone https://github.com/open-mmlab/mmdeploy -b dev-1.x mmdeploy \
+    && cd mmdeploy \
+    && git submodule update --init --recursive \
+    && mkdir -p build \
+    && cd build \
+    && cmake -DMMDEPLOY_TARGET_BACKENDS="ort;trt" -DONNXRUNTIME_DIR=${ONNXRUNTIME_DIR} -DTENSORRT_DIR=${TENSORRT_DIR} -DCUDNN_DIR=${CUDNN_DIR} .. \
+    && make -j$(nproc) \
+    && make install \
+    && cd .. \
+    && mim install --no-cache-dir -e .
+
+# Fix undefined symbol bug
+RUN echo -e "\nldconfig" > /root/.bashrc
diff --git a/docs/zh_cn/advanced_guides/basic_deployment_guide.md b/docs/zh_cn/advanced_guides/basic_deployment_guide.md
index 7ddf4f8399f32531823ad4a34128ee0e9fab8ab7..25d77cf5eccbffc2db68343ec757982cc05d9590 100644
--- a/docs/zh_cn/advanced_guides/basic_deployment_guide.md
+++ b/docs/zh_cn/advanced_guides/basic_deployment_guide.md
@@ -209,7 +209,7 @@ use_efficientnms = False
 设置 `MMDeploy` 根目录为环境变量 `MMDEPLOY_DIR` ，例如 `export MMDEPLOY_DIR=/the/root/path/of/MMDeploy`
 
 ```shell
-python3 &(MMDEPLOY_DIR)/tools/deploy.py \
+python3 ${MMDEPLOY_DIR}/tools/deploy.py \
     ${DEPLOY_CFG_PATH} \
     ${MODEL_CFG_PATH} \
     ${MODEL_CHECKPOINT_PATH} \
@@ -239,10 +239,10 @@ python3 &(MMDEPLOY_DIR)/tools/deploy.py \
 
 # 模型评测
 
-当您将 PyTorch 模型转换为后端支持的模型后，您可能需要验证模型的精度，使用 `&(MMDEPLOY_DIR)/tools/test.py`
+当您将 PyTorch 模型转换为后端支持的模型后，您可能需要验证模型的精度，使用 `${MMDEPLOY_DIR}/tools/test.py`
 
 ```shell
-python3 &(MMDEPLOY_DIR)/tools/test.py \
+python3 ${MMDEPLOY_DIR}/tools/test.py \
     ${DEPLOY_CFG} \
     ${MODEL_CFG} \
     --model ${BACKEND_MODEL_FILES} \
@@ -282,31 +282,4 @@ python3 &(MMDEPLOY_DIR)/tools/test.py \
 - `--warmup`: 在计算推理时间之前进行预热，需要先开启 `speed-test`。
 - `--log-interval`: 每个日志之间的间隔，需要先设置 `speed-test`。
 
-注意：`&(MMDEPLOY_DIR)/tools/test.py` 中的其他参数用于速度测试。他们不影响评估。
-
-# 模型推理
-
-当您评测部署模型精度后，您可以使用 `mmyolo/demo/image_demo_deploy.py` 来测试本地图片并选择展示或保存可视化结果。
-
-```shell
-python3 demo/image_demo_deploy.py \
-    ${IMG} \
-    ${MODEL_CFG} \
-    ${DEPLOY_CFG} \
-    ${CHECKPOINT} \
-    [--out-dir ${OUTPUT_DIR}] \
-    [--device] ${DEVICE}\
-    [--show] \
-    [--score-thr ${SHOW_SCORE_THR}
-```
-
-## 参数描述
-
-- `img` ：待检测的图片路径，文件夹路径，或图片网址。
-- `model_cfg`: MMYOLO 模型配置文件。
-- `deploy_cfg`: 部署配置文件。
-- `CHECKPOINT`: 导出的后端模型。 例如, 如果我们导出了 TensorRT 模型，我们需要传入后缀为 ".engine" 文件路径。
-- `--out-dir`:  保存检测的可视化结果文件夹。
-- `--device`: 运行模型的设备。请注意，某些后端会限制设备。例如，TensorRT 必须在 cuda 上运行。
-- `--show`: 是否在屏幕上显示检测的可视化结果，当您开启此选项后，将会关闭保存可视化结果。
-- `--score-thr`: 确定是否显示检测边界框的阈值。
+注意：`${MMDEPLOY_DIR}/tools/test.py` 中的其他参数用于速度测试。他们不影响评估。
diff --git a/docs/zh_cn/advanced_guides/yolov5_deploy.md b/docs/zh_cn/advanced_guides/yolov5_deploy.md
index 3283f81aabfade0d351907aea5704e50a70a2a58..a0800bfef252b2af94b50a398edb17b3a0b28b4b 100644
--- a/docs/zh_cn/advanced_guides/yolov5_deploy.md
+++ b/docs/zh_cn/advanced_guides/yolov5_deploy.md
@@ -215,7 +215,7 @@ export PATH_TO_CHECKPOINTS=/home/openmmlab/dev/mmdeploy/yolov5s.pth
 #### ONNXRuntime
 
 ```shell
-python3 &(MMDEPLOY_DIR)/tools/deploy.py \
+python3 ${MMDEPLOY_DIR}/tools/deploy.py \
     configs/deploy/detection_onnxruntime_static.py \
     configs/deploy/model/yolov5_s-static.py \
     ${PATH_TO_CHECKPOINTS} \
@@ -228,7 +228,7 @@ python3 &(MMDEPLOY_DIR)/tools/deploy.py \
 #### TensorRT
 
 ```bash
-python3 &(MMDEPLOY_DIR)/tools/deploy.py \
+python3 ${MMDEPLOY_DIR}/tools/deploy.py \
     configs/deploy/detection_tensorrt_static-640x640.py \
     configs/deploy/model/yolov5_s-static.py \
     ${PATH_TO_CHECKPOINTS} \
@@ -243,7 +243,7 @@ python3 &(MMDEPLOY_DIR)/tools/deploy.py \
 #### ONNXRuntime
 
 ```shell
-python3 &(MMDEPLOY_DIR)/tools/deploy.py \
+python3 ${MMDEPLOY_DIR}/tools/deploy.py \
     configs/deploy/detection_onnxruntime_dynamic.py \
     configs/yolov5/yolov5_s-v61_syncbn_8xb16-300e_coco.py \
     ${PATH_TO_CHECKPOINTS} \
@@ -256,7 +256,7 @@ python3 &(MMDEPLOY_DIR)/tools/deploy.py \
 #### TensorRT
 
 ```shell
-python3 &(MMDEPLOY_DIR)/tools/deploy.py \
+python3 ${MMDEPLOY_DIR}/tools/deploy.py \
     configs/deploy/detection_tensorrt_dynamic-192x192-960x960.py \
     configs/yolov5/yolov5_s-v61_syncbn_8xb16-300e_coco.py \
     ${PATH_TO_CHECKPOINTS} \
@@ -280,12 +280,12 @@ python3 &(MMDEPLOY_DIR)/tools/deploy.py \
 
 ## 模型评测
 
-当您转换模型成功后，可以使用 `&(MMDEPLOY_DIR)/tools/test.py` 工具对转换后的模型进行评测。下面是对 `ONNXRuntime` 和 `TensorRT` 静态模型的评测，动态模型评测修改传入模型配置即可。
+当您转换模型成功后，可以使用 `${MMDEPLOY_DIR}/tools/test.py` 工具对转换后的模型进行评测。下面是对 `ONNXRuntime` 和 `TensorRT` 静态模型的评测，动态模型评测修改传入模型配置即可。
 
 ### ONNXRuntime
 
 ```shell
-python3 &(MMDEPLOY_DIR)/tools/test.py \
+python3 ${MMDEPLOY_DIR}/tools/test.py \
         configs/deploy/detection_onnxruntime_static.py \
         configs/deploy/model/yolov5_s-static.py \
         --model work_dir/end2end.onnx  \
@@ -302,7 +302,7 @@ python3 &(MMDEPLOY_DIR)/tools/test.py \
 **注意**： TensorRT 需要执行设备是 `cuda`
 
 ```shell
-python3 &(MMDEPLOY_DIR)/tools/test.py \
+python3 ${MMDEPLOY_DIR}/tools/test.py \
         configs/deploy/detection_tensorrt_static-640x640.py \
         configs/deploy/model/yolov5_s-static.py \
         --model work_dir/end2end.engine  \
@@ -316,6 +316,116 @@ python3 &(MMDEPLOY_DIR)/tools/test.py \
 
 **未来我们将会支持模型测速等更加实用的脚本**
 
+# 使用 Docker 部署测试
+
+`MMYOLO` 提供了一个 [`Dockerfile`](docker/Dockerfile_deployment) 用于构建镜像。请确保您的 `docker` 版本大于等于 `19.03`。
+
+温馨提示；国内用户建议取消掉 [`Dockerfile`](docker/Dockerfile_deployment) 里面 `Optional` 后两行的注释，可以获得火箭一般的下载提速：
+
+```dockerfile
+# (Optional)
+RUN sed -i 's/http:\/\/archive.ubuntu.com\/ubuntu\//http:\/\/mirrors.aliyun.com\/ubuntu\//g' /etc/apt/sources.list && \
+    pip config set global.index-url https://pypi.tuna.tsinghua.edu.cn/simple
+```
+
+构建命令：
+
+```shell
+# build an image with PyTorch 1.12, CUDA 11.6, TensorRT 8.2.4 ONNXRuntime 1.8.1
+docker build -f docker/Dockerfile_deployment -t mmyolo:v1 .
+```
+
+用以下命令运行 Docker 镜像：
+
+```shell
+export DATA_DIR=/path/to/your/dataset
+docker run --gpus all --shm-size=8g -it --name mmyolo -v ${DATA_DIR}:/openmmlab/mmyolo/data/coco mmyolo:v1
+```
+
+`DATA_DIR` 是 COCO 数据的路径。
+
+复制以下脚本到 `docker` 容器 `/openmmlab/mmyolo/script.sh`:
+
+```bash
+#!/bin/bash
+wget -q https://download.openmmlab.com/mmyolo/v0/yolov5/yolov5_s-v61_syncbn_fast_8xb16-300e_coco/yolov5_s-v61_syncbn_fast_8xb16-300e_coco_20220918_084700-86e02187.pth \
+  -O yolov5s.pth
+export MMDEPLOY_DIR=/openmmlab/mmdeploy
+export PATH_TO_CHECKPOINTS=/openmmlab/mmyolo/yolov5s.pth
+
+python3 ${MMDEPLOY_DIR}/tools/deploy.py \
+  configs/deploy/detection_tensorrt_static-640x640.py \
+  configs/deploy/model/yolov5_s-static.py \
+  ${PATH_TO_CHECKPOINTS} \
+  demo/demo.jpg \
+  --work-dir work_dir_trt \
+  --device cuda:0
+
+python3 ${MMDEPLOY_DIR}/tools/test.py \
+  configs/deploy/detection_tensorrt_static-640x640.py \
+  configs/deploy/model/yolov5_s-static.py \
+  --model work_dir_trt/end2end.engine \
+  --device cuda:0 \
+  --work-dir work_dir_trt
+
+python3 ${MMDEPLOY_DIR}/tools/deploy.py \
+  configs/deploy/detection_onnxruntime_static.py \
+  configs/deploy/model/yolov5_s-static.py \
+  ${PATH_TO_CHECKPOINTS} \
+  demo/demo.jpg \
+  --work-dir work_dir_ort \
+  --device cpu
+
+python3 ${MMDEPLOY_DIR}/tools/test.py \
+  configs/deploy/detection_onnxruntime_static.py \
+  configs/deploy/model/yolov5_s-static.py \
+  --model work_dir_ort/end2end.onnx \
+  --device cpu \
+  --work-dir work_dir_ort
+```
+
+在 `/openmmlab/mmyolo` 下运行：
+
+```shell
+sh script.sh
+```
+
+脚本会自动下载 `MMYOLO` 的 `YOLOv5` 预训练权重并使用 `MMDeploy` 进行模型转换和测试。您将会看到以下输出：
+
+- TensorRT：
+
+  ![image](https://user-images.githubusercontent.com/92794867/199657349-1bad9196-c00b-4a65-84f5-80f51e65a2bd.png)
+
+- ONNXRuntime：
+
+  ![image](https://user-images.githubusercontent.com/92794867/199657283-95412e84-3ba4-463f-b4b2-4bf52ec4acbd.png)
+
+可以看到，经过 `MMDeploy` 部署的模型与 [MMYOLO-YOLOv5](`https://github.com/open-mmlab/mmyolo/tree/main/configs/yolov5`) 的 mAP-37.7 差距在 1% 以内。
+
+如果您需要测试您的模型推理速度，可以使用以下命令：
+
+- TensorRT
+
+```shell
+python3 ${MMDEPLOY_DIR}/tools/profiler.py \
+  configs/deploy/detection_tensorrt_static-640x640.py \
+  configs/deploy/model/yolov5_s-static.py \
+  data/coco/val2017 \
+  --model work_dir_trt/end2end.engine \
+  --device cuda:0
+```
+
+- ONNXRuntime
+
+```shell
+python3 ${MMDEPLOY_DIR}/tools/profiler.py \
+  configs/deploy/detection_tensorrt_static-640x640.py \
+  configs/deploy/model/yolov5_s-static.py \
+  data/coco/val2017 \
+  --model work_dir_trt/end2end.engine \
+  --device cpu
+```
+
 ## 模型推理
 
 TODO