add en doc for vehicle attr (#6638)

* add en doc for vehicle attr

* fix some grammer problems

* fix grammer

* fix command line error in doc

deploy/pipeline/docs/tutorials/pphuman_action.md

@@ -52,21 +52,23 @@ SKELETON_ACTION: # 基于骨骼点的行为识别模型配置
 ### 使用方法
 1. 从`模型库`中下载`行人检测/跟踪`、`关键点识别`、`摔倒行为识别`三个预测部署模型并解压到```./output_inference```路径下;默认自动下载模型，如果手动下载，需要修改模型文件夹为模型存放路径。
 2. 目前行为识别模块仅支持视频输入，根据期望开启的行为识别方案类型，设置infer_cfg_pphuman.yml中`SKELETON_ACTION`的enable: True, 然后启动命令如下：
-```python
-python deploy/pipeline/pipeline.py --config deploy/pipeline/config/infer_cfg_pphuman.yml \
+    ```bash
+    python deploy/pipeline/pipeline.py --config deploy/pipeline/config/infer_cfg_pphuman.yml \
                                                    --video_file=test_video.mp4 \
                                                    --device=gpu \
-```
+    ```
+
 3. 若修改模型路径，有以下两种方式：
 
     - ```./deploy/pipeline/config/infer_cfg_pphuman.yml```下可以配置不同模型路径，关键点模型和摔倒行为识别模型分别对应`KPT`和`SKELETON_ACTION`字段，修改对应字段下的路径为实际期望的路径即可。
     - 命令行中--config后面紧跟着增加`-o KPT.model_dir=xxx SKELETON_ACTION.model_dir=xxx `修改模型路径：
-```python
-python deploy/pipeline/pipeline.py --config deploy/pipeline/config/infer_cfg_pphuman.yml \
-                                                   -o KPT.model_dir=./dark_hrnet_w32_256x192 SKELETON_ACTION.model_dir=./STGCN\
+    ```bash
+    python deploy/pipeline/pipeline.py --config deploy/pipeline/config/infer_cfg_pphuman.yml \
+                                                   -o KPT.model_dir=./dark_hrnet_w32_256x192 SKELETON_ACTION.model_dir=./STGCN \
                                                    --video_file=test_video.mp4 \
                                                    --device=gpu
-```
+    ```
+
 4. 启动命令中的完整参数说明，请参考[参数说明](./PPHuman_QUICK_STARTED.md)。
 
 

deploy/pipeline/docs/tutorials/pphuman_action_en.md

@@ -63,20 +63,21 @@ SKELETON_ACTION: # Config for skeleton-based action recognition model
 
 2. Now the only available input is the video input in the action recognition module. set the "enable: True" of `SKELETON_ACTION` in infer_cfg_pphuman.yml. And then run the command:
 
-  ```python
-  python deploy/pipeline/pipeline.py --config deploy/pipeline/config/infer_cfg_pphuman.yml \
+    ```bash
+    python deploy/pipeline/pipeline.py --config deploy/pipeline/config/infer_cfg_pphuman.yml \
                                                      --video_file=test_video.mp4 \
                                                      --device=gpu
-  ```
+    ```
 
 3. There are two ways to modify the model path:
 
   - In ```./deploy/pipeline/config/infer_cfg_pphuman.yml```, you can configurate different model paths，which is proper only if you match keypoint models and action recognition models with the fields of `KPT` and `SKELETON_ACTION` respectively, and modify the corresponding path of each field into the expected path.
   - Add `-o KPT.model_dir=xxx SKELETON_ACTION.model_dir=xxx ` in the command line following the --config to change the model path：
 
-    ```python
-python deploy/pipeline/pipeline.py --config deploy/pipeline/config/infer_cfg_pphuman.yml \
-                                                   -o KPT.model_dir=./dark_hrnet_w32_256x192 SKELETON_ACTION.model_dir=./STGCN\
+
+    ```bash
+    python deploy/pipeline/pipeline.py --config deploy/pipeline/config/infer_cfg_pphuman.yml \
+                                                   -o KPT.model_dir=./dark_hrnet_w32_256x192 SKELETON_ACTION.model_dir=./STGCN \
                                                    --video_file=test_video.mp4 \
                                                    --device=gpu
     ```
@@ -172,7 +173,7 @@ ID_BASED_DETACTION: # Config for detection-based action recognition model
 2. Now the only available input is the video input in the action recognition module. set the "enable: True" of `ID_BASED_DETACTION` in infer_cfg_pphuman.yml.
 
 3. Run this command:
-  ```python
+  ```bash
   python deploy/pipeline/pipeline.py --config deploy/pipeline/config/infer_cfg_pphuman.yml \
                                                      --video_file=test_video.mp4 \
                                                      --device=gpu
@@ -229,7 +230,7 @@ VIDEO_ACTION:  # Config for detection-based action recognition model
 3. Now the only available input is the video input in the action recognition module. set the "enable: True" of `VIDEO_ACTION` in infer_cfg_pphuman.yml.
 
 4. Run this command:
-  ```python
+  ```bash
   python deploy/pipeline/pipeline.py --config deploy/pipeline/config/infer_cfg_pphuman.yml \
                                                      --video_file=test_video.mp4 \
                                                      --device=gpu

deploy/pipeline/docs/tutorials/ppvehicle_attribute.md

+[English](ppvehicle_attribute_en.md) | 简体中文
 
 # PP-Vehicle属性识别模块
 
@@ -46,7 +47,7 @@
 
 ### 配置项说明
 
-配置文件中与属性相关的参数如下：
+[配置文件](../../config/infer_cfg_ppvehicle.yml)中与属性相关的参数如下：
 ```
 VEHICLE_ATTR:
   model_dir: output_inference/vehicle_attribute_infer/ # 车辆属性模型调用路径
@@ -91,13 +92,13 @@ python deploy/pipeline/pipeline.py --config deploy/pipeline/config/infer_cfg_ppv
 5. 若修改模型路径，有以下两种方式：
 
     - 方法一：`./deploy/pipeline/config/infer_cfg_ppvehicle.yml`下可以配置不同模型路径，属性识别模型修改`VEHICLE_ATTR`字段下配置
-    - 方法二：命令行中增加--model_dir修改模型路径：
+    - 方法二：直接在命令行中增加`-o`，以覆盖配置文件中的默认模型路径：
 
 ```bash
 python deploy/pipeline/pipeline.py --config deploy/pipeline/config/infer_cfg_ppvehicle.yml \
                                                    --video_file=test_video.mp4 \
                                                    --device=gpu \
-                                                   --model_dir vehicle_attr=output_inference/vehicle_attribute_infer
+                                                   -o VEHICLE_ATTR.model_dir=output_inference/vehicle_attribute_infer
 ```
 
 测试效果如下：
@@ -107,7 +108,7 @@ python deploy/pipeline/pipeline.py --config deploy/pipeline/config/infer_cfg_ppv
 </div>
 
 ## 方案说明
-车辆属性模型使用了[PaddleClas](https://github.com/PaddlePaddle/PaddleClas) 的超轻量图像分类方案(PULC，Practical Ultra Lightweight image Classification)。关于该模型的数据准备、训练、测试等详细内容，请见[PULC 车辆属性识别模型](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.4/docs/zh_CN/PULC/PULC_vehicle_attribute.md).
+车辆属性识别模型使用了[PaddleClas](https://github.com/PaddlePaddle/PaddleClas) 的超轻量图像分类方案(PULC，Practical Ultra Lightweight image Classification)。关于该模型的数据准备、训练、测试等详细内容，请见[PULC 车辆属性识别模型](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.4/docs/zh_CN/PULC/PULC_vehicle_attribute.md).
 
 车辆属性识别模型选用了轻量级、高精度的PPLCNet。并在该模型的基础上，进一步使用了以下优化方案：
 

deploy/pipeline/docs/tutorials/ppvehicle_attribute_en.md

+English | [简体中文](ppvehicle_attribute.md)
+
+# Attribute Recognition Module of PP-Vehicle
+
+Vehicle attribute recognition is widely used in smart cities, smart transportation and other scenarios. In PP-Vehicle, a vehicle attribute recognition module is integrated, which can identify vehicle color and model.
+
+| Task | Algorithm | Precision | Inference Speed | Download |
+|-----------|------|-----------|----------|---------------------|
+| Vehicle Detection/Tracking | PP-YOLOE | - | - | [Inference and Deployment Model](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_l_36e_ppvehicle.zip) |
+| Vehicle Attribute Recognition | PPLCNet | 90.81 | 2.36 ms | [Inference and Deployment Model](https://bj.bcebos.com/v1/paddledet/models/pipeline/vehicle_attribute_model.zip) |
+
+
+Note:
+1. The inference speed of the attribute model is obtained from the test on Intel(R) Xeon(R) Gold 6148 CPU @ 2.40GHz, with the MKLDNN acceleration strategy enabled, and 10 threads.
+2. For introductions, please refer to [PP-LCNet Series](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.4/docs/en/models/PP-LCNet_en.md). Related paper is available on PP-LCNet paper
+3. The training and test phase of vehicle attribute recognition model are both obtained from [VeRi dataset](https://www.v7labs.com/open-datasets/veri-dataset).
+
+
+- The provided pre-trained model supports 10 colors and 9 models, which is the same with VeRi dataset. The details are as follows:
+
+```yaml
+# Vehicle Colors
+- "yellow"
+- "orange"
+- "green"
+- "gray"
+- "red"
+- "blue"
+- "white"
+- "golden"
+- "brown"
+- "black"
+
+# Vehicle Models
+- "sedan"
+- "suv"
+- "van"
+- "hatchback"
+- "mpv"
+- "pickup"
+- "bus"
+- "truck"
+- "estate"
+```
+
+## Instructions
+
+### Description of Configuration
+
+Parameters related to vehicle attribute recognition in the [config file](../../config/infer_cfg_ppvehicle.yml) are as follows:
+
+```yaml
+VEHICLE_ATTR:
+  model_dir: output_inference/vehicle_attribute_infer/ # Path of the model
+  batch_size: 8     # The size of the inference batch
+  color_threshold: 0.5  # Threshold of color. Confidence is required to reach this threshold to determine the specific attribute, otherwise it will be 'Unknown‘.
+  type_threshold: 0.5   # Threshold of vehicle model. Confidence is required to reach this threshold to determine the specific attribute, otherwise it will be 'Unknown‘.
+  enable: False         # Whether to enable this function
+```
+
+### How to Use
+1. Download models `Vehicle Detection/Tracking` and `Vehicle Attribute Recognition` from the links in `Model Zoo` and unzip them to ```./output_inference```. The models are automatically downloaded by default. If you download them manually, you need to modify the `model_dir` as the model storage path to use this function.
+
+2. Set the "enable: True" of `VEHICLE_ATTR` in infer_cfg_ppvehicle.yml.
+
+3. For image input, please run these commands. (Description of more parameters, please refer to [QUICK_STARTED - Parameter_Description](./PPVehicle_QUICK_STARTED.md).
+
+```bash
+# For single image
+python deploy/pipeline/pipeline.py --config deploy/pipeline/config/infer_cfg_ppvehicle.yml \
+                                                   --image_file=test_image.jpg \
+                                                   --device=gpu
+
+# For folder contains one or multiple images
+python deploy/pipeline/pipeline.py --config deploy/pipeline/config/infer_cfg_ppvehicle.yml \
+                                                   --image_dir=images/ \
+                                                   --device=gpu
+```
+
+4. For video input, please run these commands.
+
+```bash
+# For single video
+python deploy/pipeline/pipeline.py --config deploy/pipeline/config/infer_cfg_ppvehicle.yml \
+                                                   --video_file=test_video.mp4 \
+                                                   --device=gpu
+
+# For folder contains one or multiple videos
+python deploy/pipeline/pipeline.py --config deploy/pipeline/config/infer_cfg_ppvehicle.yml \
+                                                   --video_dir=test_videos/ \
+                                                   --device=gpu
+```
+
+5. There are two ways to modify the model path:
+
+    - Method 1：Set paths of each model in `./deploy/pipeline/config/infer_cfg_ppvehicle.yml`. For vehicle attribute recognition, the path should be modified under the `VEHICLE_ATTR` field.
+    - Method 2: Directly add `-o` in command line to override the default model path in the configuration file:
+
+```bash
+python deploy/pipeline/pipeline.py --config deploy/pipeline/config/infer_cfg_ppvehicle.yml \
+                                                   --video_file=test_video.mp4 \
+                                                   --device=gpu \
+                                                   -o VEHICLE_ATTR.model_dir=output_inference/vehicle_attribute_infer
+```
+
+The result is shown as follow:
+
+<div width="1000" align="center">
+  <img src="../images/vehicle_attribute.gif"/>
+</div>
+
+
+### Features to the Solution
+
+The vehicle attribute recognition model adopts PULC, Practical Ultra Lightweight image Classification from [PaddleClas](https://github.com/PaddlePaddle/PaddleClas). For details on data preparation, training, and testing of the model, please refer to [PULC Recognition Model of Vehicle Attribute](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.4/docs/en/PULC/PULC_vehicle_attribute_en.md).
+
+The vehicle attribute recognition model adopts the lightweight and high-precision PPLCNet. And on top of PPLCNet, our model optimized via::
+
+- Improved about 0.5 percentage points accuracy by using the SSLD pre-trained model without changing the inference speed.
+- Improved 0.52 percentage points accuracy further by integrating EDA data augmentation strategy.
+- Improved 0.23 percentage points accuracy by using SKL-UGI knowledge distillation.