[cherry-pick]update pphuman & ppvehicle train configs (#6741) (#6767)

* update pphuman model of keypoint\attribute\action recognition

* update jetson docs

* mv yolov3 pedestrian to pphuman

* update ppvehicle&human det

* update links

* add vehicle plate & attribute

* add pphuman json & ppvehicle json

configs/mot/deepsort/detector/README.md

@@ -16,7 +16,7 @@ English | [简体中文](README_cn.md)
 **Notes:**
   - The above models are trained with **MOT17-half train** set, it can be downloaded from this [link](https://dataset.bj.bcebos.com/mot/MOT17.zip).
   - **MOT17-half train** set is a dataset composed of pictures and labels of the first half frame of each video in MOT17 Train dataset (7 sequences in total). **MOT17-half val set** is used for evaluation, which is composed of the second half frame of each video. They can be downloaded from this [link](https://paddledet.bj.bcebos.com/data/mot/mot17half/annotations.zip). Download and unzip it in the `dataset/mot/MOT17/images/`folder.
-  - YOLOv3 is trained with the same pedestrian dataset as `configs/pedestrian/pedestrian_yolov3_darknet.yml`, which is not open yet.
+  - YOLOv3 is trained with the same pedestrian dataset as `configs/pphuman/pedestrian_yolov3/pedestrian_yolov3_darknet.yml`, which is not open yet.
   - For pedestrian tracking, please use pedestrian detector combined with pedestrian ReID model. For vehicle tracking, please use vehicle detector combined with vehicle ReID model.
   - High quality detected boxes are required for DeepSORT tracking, so the post-processing settings such as NMS threshold of these models are different from those in pure detection tasks.
 

configs/mot/deepsort/detector/README_cn.md

@@ -17,7 +17,7 @@
 **注意:**
   - 以上模型均可采用**MOT17-half train**数据集训练，数据集可以从[此链接](https://dataset.bj.bcebos.com/mot/MOT17.zip)下载。
   - **MOT17-half train**是MOT17的train序列(共7个)每个视频的前一半帧的图片和标注组成的数据集，而为了验证精度可以都用**MOT17-half val**数据集去评估，它是每个视频的后一半帧组成的，数据集可以从[此链接](https://paddledet.bj.bcebos.com/data/mot/mot17half/annotations.zip)下载，并解压放在`dataset/mot/MOT17/images/`文件夹下。
-  - YOLOv3和`configs/pedestrian/pedestrian_yolov3_darknet.yml`是相同的pedestrian数据集训练的，此数据集暂未开放。
+  - YOLOv3和`configs/pphuman/pedestrian_yolov3/pedestrian_yolov3_darknet.yml`是相同的pedestrian数据集训练的，此数据集暂未开放。
   - 行人跟踪请使用行人检测器结合行人ReID模型。车辆跟踪请使用车辆检测器结合车辆ReID模型。
   - 用于DeepSORT跟踪时需要高质量的检出框，因此这些模型的NMS阈值等后处理设置会与纯检测任务的设置不同。
 

configs/pphuman/README.md

@@ -2,19 +2,24 @@
 
 # PP-YOLOE Human 检测模型
 
-PaddleDetection团队提供了针对行人的基于PP-YOLOE的检测模型，用户可以下载模型进行使用。
+PaddleDetection团队提供了针对行人的基于PP-YOLOE的检测模型，用户可以下载模型进行使用。PP-Human中使用模型为业务数据集模型，我们同时提供CrowdHuman训练配置，可以使用开源数据进行训练。
 其中整理后的COCO格式的CrowdHuman数据集[下载链接](https://bj.bcebos.com/v1/paddledet/data/crowdhuman.zip)，检测类别仅一类 `pedestrian(1)`，原始数据集[下载链接](http://www.crowdhuman.org/download.html)。
 
 |    模型   |  数据集  | mAP<sup>val<br>0.5:0.95 | mAP<sup>val<br>0.5 |  下载  | 配置文件 |
 |:---------|:-------:|:------:|:------:| :----: | :------:|
 |PP-YOLOE-s|   CrowdHuman   |  42.5  |  77.9  | [下载链接](https://paddledet.bj.bcebos.com/models/ppyoloe_crn_s_36e_crowdhuman.pdparams) | [配置文件](./ppyoloe_crn_s_36e_crowdhuman.yml) |
 |PP-YOLOE-l|   CrowdHuman   |  48.0  |  81.9  | [下载链接](https://paddledet.bj.bcebos.com/models/ppyoloe_crn_l_36e_crowdhuman.pdparams) | [配置文件](./ppyoloe_crn_l_36e_crowdhuman.yml) |
+|PP-YOLOE-s|   业务数据集   |  53.2  |  -  | [下载链接](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_s_36e_pipeline.zip) | [配置文件](./ppyoloe_crn_s_36e_pphuman.yml) |
+|PP-YOLOE-l|   业务数据集   |  57.8  |  -  | [下载链接](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_l_36e_pipeline.zip) | [配置文件](./ppyoloe_crn_l_36e_pphuman.yml) |
 
 
 **注意:**
 - PP-YOLOE模型训练过程中使用8 GPUs进行混合精度训练，如果**GPU卡数**或者**batch size**发生了改变，你需要按照公式 **lr<sub>new</sub> = lr<sub>default</sub> * (batch_size<sub>new</sub> * GPU_number<sub>new</sub>) / (batch_size<sub>default</sub> * GPU_number<sub>default</sub>)** 调整学习率。
 - 具体使用教程请参考[ppyoloe](../ppyoloe#getting-start)。
 
+# YOLOv3 Human 检测模型
+
+请参考[Human_YOLOv3页面](./pedestrian_yolov3/README_cn.md)
 
 # PP-YOLOE 香烟检测模型
 基于PP-YOLOE模型的香烟检测模型，是实现PP-Human中的基于检测的行为识别方案的一环，如何在PP-Human中使用该模型进行吸烟行为识别，可参考[PP-Human行为识别模块](../../deploy/pipeline/docs/tutorials/pphuman_action.md)。该模型检测类别仅包含香烟一类。由于数据来源限制，目前暂无法直接公开训练数据。该模型使用了小目标数据集VisDrone上的权重(参照[visdrone](../visdrone))作为预训练模型，以提升检测效果。
@@ -23,6 +28,47 @@ PaddleDetection团队提供了针对行人的基于PP-YOLOE的检测模型，用
 |:---------|:-------:|:------:|:------:| :----: | :------:|
 | PP-YOLOE-s | 香烟业务数据集 |  39.7 | 79.5 |[下载链接](https://bj.bcebos.com/v1/paddledet/models/pipeline/ppyoloe_crn_s_80e_smoking_visdrone.pdparams) | [配置文件](./ppyoloe_crn_s_80e_smoking_visdrone.yml) |
 
+# PP-HGNet 打电话识别模型
+基于PP-HGNet模型实现了打电话行为识别，详细可参考[PP-Human行为识别模块](../../deploy/pipeline/docs/tutorials/pphuman_action.md)。该模型基于[PaddleClas](https://github.com/PaddlePaddle/PaddleClas/blob/develop/docs/zh_CN/models/PP-HGNet.md#3.3)套件进行训练。此处提供预测模型下载：
+
+|    模型   |  数据集  | Acc | 下载  | 配置文件 |
+|:---------|:-------:|:------:| :----: | :------:|
+| PP-HGNet | 业务数据集 |  86.85 |[下载链接](https://bj.bcebos.com/v1/paddledet/models/pipeline/PPHGNet_tiny_calling_halfbody.zip) | - |
+
+# HRNet 人体关键点模型
+人体关键点模型与ST-GCN模型一起完成[基于骨骼点的行为识别](../../deploy/pipeline/docs/tutorials/pphuman_action.md)方案。关键点模型采用HRNet模型，关于关键点模型相关详细资料可以查看关键点专栏页面[KeyPoint](../keypoint/README.md)。此处提供训练模型下载链接。
+
+|    模型   |  数据集  | AP<sup>val<br>0.5:0.95 | 下载  | 配置文件 |
+|:---------|:-------:|:------:| :----: | :------:|
+| HRNet | 业务数据集 |  87.1 |[下载链接](https://bj.bcebos.com/v1/paddledet/models/pipeline/dark_hrnet_w32_256x192.pdparams) | [配置文件](./hrnet_w32_256x192.yml) |
+
+
+# ST-GCN 骨骼点行为识别模型
+人体关键点模型与[ST-GCN](https://arxiv.org/abs/1801.07455)模型一起完成[基于骨骼点的行为识别](../../deploy/pipeline/docs/tutorials/pphuman_action.md)方案。
+ST-GCN模型基于[PaddleVideo](https://github.com/PaddlePaddle/PaddleVideo/blob/develop/applications/PPHuman)完成训练。
+此处提供预测模型下载链接。
+
+|    模型   |  数据集  | AP<sup>val<br>0.5:0.95 | 下载  | 配置文件 |
+|:---------|:-------:|:------:| :----: | :------:|
+| ST-GCN | 业务数据集 |  87.1 |[下载链接](https://bj.bcebos.com/v1/paddledet/models/pipeline/STGCN.zip) | [配置文件](https://github.com/PaddlePaddle/PaddleVideo/blob/develop/applications/PPHuman/configs/stgcn_pphuman.yaml) |
+
+# PP-TSM 视频分类模型
+基于`PP-TSM`模型完成了[基于视频分类的行为识别](../../deploy/pipeline/docs/tutorials/pphuman_action.md)方案。
+PP-TSM模型基于[PaddleVideo](https://github.com/PaddlePaddle/PaddleVideo/tree/develop/applications/FightRecognition)完成训练。
+此处提供预测模型下载链接。
+
+|    模型   |  数据集  | Acc | 下载  | 配置文件 |
+|:---------|:-------:|:------:| :----: | :------:|
+| PP-TSM | 组合开源数据集 |  89.06 |[下载链接](https://videotag.bj.bcebos.com/PaddleVideo-release2.3/ppTSM_fight.zip) | [配置文件](https://github.com/PaddlePaddle/PaddleVideo/tree/develop/applications/FightRecognition/pptsm_fight_frames_dense.yaml) |
+
+# PP-HGNet、PP-LCNet 属性识别模型
+基于PP-HGNet、PP-LCNet 模型实现了行人属性识别，详细可参考[PP-Human行为识别模块](../../deploy/pipeline/docs/tutorials/pphuman_attribute.md)。该模型基于[PaddleClas](https://github.com/PaddlePaddle/PaddleClas/blob/develop/docs/zh_CN/models/PP-LCNet.md)套件进行训练。此处提供预测模型下载链接.
+
+|    模型   |  数据集  | mA | 下载  | 配置文件 |
+|:---------|:-------:|:------:| :----: | :------:|
+| PP-HGNet_small | 业务数据集 |  95.4 |[下载链接](https://bj.bcebos.com/v1/paddledet/models/pipeline/PPHGNet_small_person_attribute_954_infer.zip) | - |
+| PP-LCNet | 业务数据集 |  94.5 |[下载链接](https://bj.bcebos.com/v1/paddledet/models/pipeline/PPLCNet_x1_0_person_attribute_945_infer.zip) | [配置文件](https://github.com/PaddlePaddle/PaddleClas/blob/develop/ppcls/configs/PULC/person_attribute/PPLCNet_x1_0.yaml) |
+
 
 ## 引用
 ```

configs/pphuman/hrnet_w32_256x192.yml

+use_gpu: true
+log_iter: 5
+save_dir: output
+snapshot_epoch: 10
+weights: output/hrnet_w32_256x192/model_final
+epoch: 210
+num_joints: &num_joints 17
+pixel_std: &pixel_std 200
+metric: KeyPointTopDownCOCOEval
+num_classes: 1
+train_height: &train_height 256
+train_width: &train_width 192
+trainsize: &trainsize [*train_width, *train_height]
+hmsize: &hmsize [48, 64]
+flip_perm: &flip_perm [[1, 2], [3, 4], [5, 6], [7, 8], [9, 10], [11, 12], [13, 14], [15, 16]]
+
+
+#####model
+architecture: TopDownHRNet
+pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/Trunc_HRNet_W32_C_pretrained.pdparams
+
+TopDownHRNet:
+  backbone: HRNet
+  post_process: HRNetPostProcess
+  flip_perm: *flip_perm
+  num_joints: *num_joints
+  width: &width 32
+  loss: KeyPointMSELoss
+
+HRNet:
+  width: *width
+  freeze_at: -1
+  freeze_norm: false
+  return_idx: [0]
+
+KeyPointMSELoss:
+  use_target_weight: true
+
+
+#####optimizer
+LearningRate:
+  base_lr: 0.0005
+  schedulers:
+  - !PiecewiseDecay
+    milestones: [170, 200]
+    gamma: 0.1
+  - !LinearWarmup
+    start_factor: 0.001
+    steps: 1000
+
+OptimizerBuilder:
+  optimizer:
+    type: Adam
+  regularizer:
+    factor: 0.0
+    type: L2
+
+
+#####data
+TrainDataset:
+  !KeypointTopDownCocoDataset
+    image_dir: train2017
+    anno_path: annotations/person_keypoints_train2017.json
+    dataset_dir: dataset/coco
+    num_joints: *num_joints
+    trainsize: *trainsize
+    pixel_std: *pixel_std
+    use_gt_bbox: True
+
+
+EvalDataset:
+  !KeypointTopDownCocoDataset
+    image_dir: val2017
+    anno_path: annotations/person_keypoints_val2017.json
+    dataset_dir: dataset/coco
+    bbox_file: bbox.json
+    num_joints: *num_joints
+    trainsize: *trainsize
+    pixel_std: *pixel_std
+    use_gt_bbox: True
+    image_thre: 0.0
+
+
+TestDataset:
+  !ImageFolder
+    anno_path: dataset/coco/keypoint_imagelist.txt
+
+worker_num: 2
+global_mean: &global_mean [0.485, 0.456, 0.406]
+global_std: &global_std [0.229, 0.224, 0.225]
+TrainReader:
+  sample_transforms:
+    - RandomFlipHalfBodyTransform:
+        scale: 0.5
+        rot: 40
+        num_joints_half_body: 8
+        prob_half_body: 0.3
+        pixel_std: *pixel_std
+        trainsize: *trainsize
+        upper_body_ids: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
+        flip_pairs: *flip_perm
+    - TopDownAffine:
+        trainsize: *trainsize
+    - ToHeatmapsTopDown:
+        hmsize: *hmsize
+        sigma: 2
+  batch_transforms:
+    - NormalizeImage:
+        mean: *global_mean
+        std: *global_std
+        is_scale: true
+    - Permute: {}
+  batch_size: 64
+  shuffle: true
+  drop_last: false
+
+EvalReader:
+  sample_transforms:
+    - TopDownAffine:
+        trainsize: *trainsize
+  batch_transforms:
+    - NormalizeImage:
+        mean: *global_mean
+        std: *global_std
+        is_scale: true
+    - Permute: {}
+  batch_size: 16
+
+TestReader:
+  inputs_def:
+    image_shape: [3, *train_height, *train_width]
+  sample_transforms:
+    - Decode: {}
+    - TopDownEvalAffine:
+        trainsize: *trainsize
+    - NormalizeImage:
+        mean: *global_mean
+        std: *global_std
+        is_scale: true
+    - Permute: {}
+  batch_size: 1
+  fuse_normalize: false #whether to fuse nomalize layer into model while export model

configs/pedestrian/README.md → configs/pphuman/pedestrian_yolov3/README.md

@@ -5,7 +5,7 @@ We provide some models implemented by PaddlePaddle to detect objects in specific
 
 | Task                 | Algorithm | Box AP | Download                                                                                | Configs |
 |:---------------------|:---------:|:------:| :-------------------------------------------------------------------------------------: |:------:|
-| Pedestrian Detection |  YOLOv3  |  51.8  | [model](https://paddledet.bj.bcebos.com/models/pedestrian_yolov3_darknet.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.5/configs/pedestrian/pedestrian_yolov3_darknet.yml) |
+| Pedestrian Detection |  YOLOv3  |  51.8  | [model](https://paddledet.bj.bcebos.com/models/pedestrian_yolov3_darknet.pdparams) | [config](./pedestrian_yolov3_darknet.yml) |
 
 ## Pedestrian Detection
 
@@ -36,15 +36,15 @@ Users can employ the model to conduct the inference:
 
 ```
 export CUDA_VISIBLE_DEVICES=0
-python -u tools/infer.py -c configs/pedestrian/pedestrian_yolov3_darknet.yml \
+python -u tools/infer.py -c configs/pphuman/pedestrian_yolov3/pedestrian_yolov3_darknet.yml \
                          -o weights=https://paddledet.bj.bcebos.com/models/pedestrian_yolov3_darknet.pdparams \
-                         --infer_dir configs/pedestrian/demo \
+                         --infer_dir configs/pphuman/pedestrian_yolov3/demo \
                          --draw_threshold 0.3 \
-                         --output_dir configs/pedestrian/demo/output
+                         --output_dir configs/pphuman/pedestrian_yolov3/demo/output
 ```
 
 Some inference results are visualized below:
 
-![](../../docs/images/PedestrianDetection_001.png)
+![](../../../docs/images/PedestrianDetection_001.png)
 
-![](../../docs/images/PedestrianDetection_004.png)
+![](../../../docs/images/PedestrianDetection_004.png)

configs/pedestrian/README_cn.md → configs/pphuman/pedestrian_yolov3/README_cn.md

@@ -5,7 +5,7 @@
 
 | 任务                 | 算法 | 精度(Box AP) | 下载                                                                                | 配置文件 |
 |:---------------------|:---------:|:------:| :---------------------------------------------------------------------------------: | :------:|
-| 行人检测 |  YOLOv3  |  51.8  | [下载链接](https://paddledet.bj.bcebos.com/models/pedestrian_yolov3_darknet.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.5/configs/pedestrian/pedestrian_yolov3_darknet.yml) |
+| 行人检测 |  YOLOv3  |  51.8  | [下载链接](https://paddledet.bj.bcebos.com/models/pedestrian_yolov3_darknet.pdparams) | [配置文件](./pedestrian_yolov3_darknet.yml) |
 
 ## 行人检测（Pedestrian Detection）
 
@@ -37,15 +37,15 @@ IOU=.5-.95时的AP为 0.518。
 
 ```
 export CUDA_VISIBLE_DEVICES=0
-python -u tools/infer.py -c configs/pedestrian/pedestrian_yolov3_darknet.yml \
+python -u tools/infer.py -c configs/pphuman/pedestrian_yolov3/pedestrian_yolov3_darknet.yml \
                          -o weights=https://paddledet.bj.bcebos.com/models/pedestrian_yolov3_darknet.pdparams \
-                         --infer_dir configs/pedestrian/demo \
+                         --infer_dir configs/pphuman/pedestrian_yolov3/demo \
                          --draw_threshold 0.3 \
-                         --output_dir configs/pedestrian/demo/output
+                         --output_dir configs/pphuman/pedestrian_yolov3/demo/output
 ```
 
 预测结果示例：
 
-![](../../docs/images/PedestrianDetection_001.png)
+![](../../../docs/images/PedestrianDetection_001.png)
 
-![](../../docs/images/PedestrianDetection_004.png)
+![](../../../docs/images/PedestrianDetection_004.png)

configs/pedestrian/pedestrian_yolov3_darknet.yml → configs/pphuman/pedestrian_yolov3/pedestrian_yolov3_darknet.yml

@@ -26,4 +26,4 @@ EvalDataset:
 
 TestDataset:
   !ImageFolder
-    anno_path: configs/pedestrian/pedestrian.json
+    anno_path: configs/pphuman/pedestrian_yolov3/pedestrian.json

configs/pphuman/ppyoloe_crn_l_36e_pphuman.yml

+_BASE_: [
+  '../datasets/coco_detection.yml',
+  '../runtime.yml',
+  '../ppyoloe/_base_/optimizer_300e.yml',
+  '../ppyoloe/_base_/ppyoloe_crn.yml',
+  '../ppyoloe/_base_/ppyoloe_reader.yml',
+]
+log_iter: 100
+snapshot_epoch: 4
+weights: output/ppyoloe_crn_l_36e_pphuman/model_final
+
+pretrain_weights: https://paddledet.bj.bcebos.com/models/ppyoloe_crn_l_300e_coco.pdparams
+depth_mult: 1.0
+width_mult: 1.0
+
+num_classes: 1
+TrainDataset:
+  !COCODataSet
+    image_dir: ""
+    anno_path: annotations/train.json
+    dataset_dir: dataset/pphuman
+    data_fields: ['image', 'gt_bbox', 'gt_class', 'is_crowd']
+
+EvalDataset:
+  !COCODataSet
+    image_dir: ""
+    anno_path: annotations/val.json
+    dataset_dir: dataset/pphuman
+
+TestDataset:
+  !ImageFolder
+    anno_path: annotations/val.json
+    dataset_dir: dataset/pphuman
+
+TrainReader:
+  batch_size: 8
+
+epoch: 36
+LearningRate:
+  base_lr: 0.001
+  schedulers:
+    - !CosineDecay
+      max_epochs: 43
+    - !LinearWarmup
+      start_factor: 0.
+      epochs: 1
+
+PPYOLOEHead:
+  static_assigner_epoch: -1
+  nms:
+    name: MultiClassNMS
+    nms_top_k: 1000
+    keep_top_k: 100
+    score_threshold: 0.01
+    nms_threshold: 0.6

configs/pphuman/ppyoloe_crn_s_36e_pphuman.yml

+_BASE_: [
+  '../datasets/coco_detection.yml',
+  '../runtime.yml',
+  '../ppyoloe/_base_/optimizer_300e.yml',
+  '../ppyoloe/_base_/ppyoloe_crn.yml',
+  '../ppyoloe/_base_/ppyoloe_reader.yml',
+]
+log_iter: 100
+snapshot_epoch: 4
+weights: output/ppyoloe_crn_s_36e_pphuman/model_final
+
+pretrain_weights: https://paddledet.bj.bcebos.com/models/ppyoloe_crn_s_300e_coco.pdparams
+depth_mult: 0.33
+width_mult: 0.50
+
+num_classes: 1
+TrainDataset:
+  !COCODataSet
+    image_dir: ""
+    anno_path: annotations/train.json
+    dataset_dir: dataset/pphuman
+    data_fields: ['image', 'gt_bbox', 'gt_class', 'is_crowd']
+
+EvalDataset:
+  !COCODataSet
+    image_dir: ""
+    anno_path: annotations/val.json
+    dataset_dir: dataset/pphuman
+
+TestDataset:
+  !ImageFolder
+    anno_path: annotations/val.json
+    dataset_dir: dataset/pphuman
+
+TrainReader:
+  batch_size: 8
+
+epoch: 36
+LearningRate:
+  base_lr: 0.001
+  schedulers:
+    - !CosineDecay
+      max_epochs: 43
+    - !LinearWarmup
+      start_factor: 0.
+      epochs: 1
+
+PPYOLOEHead:
+  static_assigner_epoch: -1
+  nms:
+    name: MultiClassNMS
+    nms_top_k: 1000
+    keep_top_k: 100
+    score_threshold: 0.01
+    nms_threshold: 0.6

configs/ppvehicle/README.md

 简体中文 | [English](README.md)
 
-# PP-YOLOE Vehicle 检测模型
+## PP-YOLOE Vehicle 检测模型
 
 PaddleDetection团队提供了针对自动驾驶场景的基于PP-YOLOE的检测模型，用户可以下载模型进行使用，主要包含5个数据集(BDD100K-DET、BDD100K-MOT、UA-DETRAC、PPVehicle9cls、PPVehicle)。其中前3者为公开数据集，后两者为整合数据集。
 - BDD100K-DET具体类别为10类，包括`pedestrian(1), rider(2), car(3), truck(4), bus(5), train(6), motorcycle(7), bicycle(8), traffic light(9), traffic sign(10)`。
@@ -35,6 +35,28 @@ label_list.txt里的一行记录一个对应种类，如下所示：
 vehicle
 ```
 
+## YOLOv3 Vehicle 检测模型
+
+请参考[Vehicle_YOLOv3页面](./vehicle_yolov3/README_cn.md)
+
+## PP-OCRv3 车牌识别模型
+
+车牌识别采用Paddle自研超轻量级模型PP-OCRv3_det、PP-OCRv3_rec。在[CCPD数据集](https://github.com/detectRecog/CCPD)（CCPD2019+CCPD2020车牌数据集）上进行了fine-tune。模型训练基于[PaddleOCR](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.6/applications/%E8%BD%BB%E9%87%8F%E7%BA%A7%E8%BD%A6%E7%89%8C%E8%AF%86%E5%88%AB.md)完成，我们提供了预测模型下载：
+
+|    模型   |  数据集  | 精度 | 下载  | 配置文件 |
+|:---------|:-------:|:------:| :----: | :------:|
+| PP-OCRv3_det | CCPD组合数据集 |  hmean:0.979 |[下载链接](https://bj.bcebos.com/v1/paddledet/models/pipeline/ch_PP-OCRv3_det_infer.tar.gz) | [配置文件](https://github.com/PaddlePaddle/PaddleOCR/blob/dygraph/configs/det/ch_PP-OCRv3/ch_PP-OCRv3_det_cml.yml) |
+| PP-OCRv3_rec | CCPD组合数据集 |  acc:0.773 |[下载链接](https://bj.bcebos.com/v1/paddledet/models/pipeline/ch_PP-OCRv3_rec_infer.tar.gz) | [配置文件](https://github.com/PaddlePaddle/PaddleOCR/blob/dygraph/configs/rec/PP-OCRv3/ch_PP-OCRv3_rec_distillation.yml) |
+
+## PP-LCNet 车牌属性模型
+
+车牌属性采用Paddle自研超轻量级模型PP-LCNet。在[VeRi数据集](https://www.v7labs.com/open-datasets/veri-dataset)进行训练。模型训练基于[PaddleClas](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.4/docs/en/PULC/PULC_vehicle_attribute_en.md)完成，我们提供了预测模型下载：
+
+|    模型   |  数据集  | 精度 | 下载  | 配置文件 |
+|:---------|:-------:|:------:| :----: | :------:|
+| PP-LCNet_x1_0 | VeRi数据集 |  90.81 |[下载链接](https://bj.bcebos.com/v1/paddledet/models/pipeline/vehicle_attribute_model.zip) | [配置文件](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.4/ppcls/configs/PULC/vehicle_attribute/PPLCNet_x1_0.yaml) |
+
+
 ## 引用
 ```
 @InProceedings{bdd100k,

configs/vehicle/README.md → configs/ppvehicle/vehicle_yolov3/README.md

@@ -5,7 +5,7 @@ We provide some models implemented by PaddlePaddle to detect objects in specific
 
 | Task                 | Algorithm | Box AP | Download                                                                                | Configs |
 |:---------------------|:---------:|:------:| :-------------------------------------------------------------------------------------: |:------:|
-| Vehicle Detection    |  YOLOv3  |  54.5  | [model](https://paddledet.bj.bcebos.com/models/vehicle_yolov3_darknet.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.5/configs/vehicle/vehicle_yolov3_darknet.yml) |
+| Vehicle Detection    |  YOLOv3  |  54.5  | [model](https://paddledet.bj.bcebos.com/models/vehicle_yolov3_darknet.pdparams) | [config](./vehicle_yolov3_darknet.yml) |
 
 ## Vehicle Detection
 
@@ -39,15 +39,15 @@ Users can employ the model to conduct the inference:
 
 ```
 export CUDA_VISIBLE_DEVICES=0
-python -u tools/infer.py -c configs/vehicle/vehicle_yolov3_darknet.yml \
+python -u tools/infer.py -c configs/ppvehicle/vehicle_yolov3/vehicle_yolov3_darknet.yml \
                          -o weights=https://paddledet.bj.bcebos.com/models/vehicle_yolov3_darknet.pdparams \
-                         --infer_dir configs/vehicle/demo \
+                         --infer_dir configs/ppvehicle/vehicle_yolov3/demo \
                          --draw_threshold 0.2 \
-                         --output_dir configs/vehicle/demo/output
+                         --output_dir configs/ppvehicle/vehicle_yolov3/demo/output
 ```
 
 Some inference results are visualized below:
 
-![](../../docs/images/VehicleDetection_001.jpeg)
+![](../../../docs/images/VehicleDetection_001.jpeg)
 
-![](../../docs/images/VehicleDetection_005.png)
+![](../../../docs/images/VehicleDetection_005.png)

configs/vehicle/README_cn.md → configs/ppvehicle/vehicle_yolov3/README_cn.md

@@ -5,7 +5,7 @@
 
 | 任务                 | 算法 | 精度(Box AP) | 下载                                                                                | 配置文件 |
 |:---------------------|:---------:|:------:| :---------------------------------------------------------------------------------: | :------:|
-| 车辆检测    |  YOLOv3  |  54.5  | [下载链接](https://paddledet.bj.bcebos.com/models/vehicle_yolov3_darknet.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.5/configs/vehicle/vehicle_yolov3_darknet.yml) |
+| 车辆检测    |  YOLOv3  |  54.5  | [下载链接](https://paddledet.bj.bcebos.com/models/vehicle_yolov3_darknet.pdparams) | [配置文件](./vehicle_yolov3_darknet.yml) |
 
 
 ## 车辆检测（Vehicle Detection）
@@ -40,15 +40,15 @@ IOU=.5时的AP为 0.764。
 
 ```
 export CUDA_VISIBLE_DEVICES=0
-python -u tools/infer.py -c configs/vehicle/vehicle_yolov3_darknet.yml \
+python -u tools/infer.py -c configs/ppvehicle/vehicle_yolov3/vehicle_yolov3_darknet.yml \
                          -o weights=https://paddledet.bj.bcebos.com/models/vehicle_yolov3_darknet.pdparams \
-                         --infer_dir configs/vehicle/demo \
+                         --infer_dir configs/ppvehicle/vehicle_yolov3/demo \
                          --draw_threshold 0.2 \
-                         --output_dir configs/vehicle/demo/output
+                         --output_dir configs/ppvehicle/vehicle_yolov3/demo/output
 ```
 
 预测结果示例：
 
-![](../../docs/images/VehicleDetection_001.jpeg)
+![](../../../docs/images/VehicleDetection_001.jpeg)
 
-![](../../docs/images/VehicleDetection_005.png)
+![](../../../docs/images/VehicleDetection_005.png)

configs/vehicle/vehicle_yolov3_darknet.yml → configs/ppvehicle/vehicle_yolov3/vehicle_yolov3_darknet.yml

@@ -39,4 +39,4 @@ EvalDataset:
 
 TestDataset:
   !ImageFolder
-    anno_path: configs/vehicle/vehicle.json
+    anno_path: configs/ppvehicle/vehicle_yolov3/vehicle.json

deploy/pipeline/docs/tutorials/PPHuman_QUICK_STARTED.md

@@ -129,6 +129,18 @@ python deploy/pipeline/pipeline.py --config deploy/pipeline/config/infer_cfg_pph
 python deploy/pipeline/pipeline.py --config deploy/pipeline/config/examples/infer_cfg_human_attr.yml -o visual=False --video_file=rtsp://[YOUR_RTSP_SITE] --device=gpu
 ```
 
+### Jetson部署说明
+
+由于Jetson平台算力相比服务器有较大差距，有如下使用建议：
+
+1. 模型选择轻量级版本，特别是跟踪模型，推荐使用`ppyoloe_s: https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_s_36e_pipeline.zip`
+2. 开启跟踪跳帧功能，推荐使用2或者3: `skip_frame_num: 3`
+
+使用该推荐配置，在TX2平台上可以达到较高速率，经测试属性案例达到20fps。
+
+可以直接修改配置文件（推荐），也可以在命令行中修改（字段较长，不推荐）。
+
+
 ### 参数说明
 
 | 参数 | 是否必须|含义 |
@@ -147,6 +159,9 @@ python deploy/pipeline/pipeline.py --config deploy/pipeline/config/examples/infe
 | --trt_calib_mode | Option| TensorRT是否使用校准功能，默认为False。使用TensorRT的int8功能时，需设置为True，使用PaddleSlim量化后的模型时需要设置为False |
 | --do_entrance_counting | Option | 是否统计出入口流量，默认为False |
 | --draw_center_traj | Option | 是否绘制跟踪轨迹，默认为False |
+| --region_type | Option | 'horizontal'（默认值）、'vertical'：表示流量统计方向选择；'custom'：表示设置闯入区域 |
+| --region_polygon | Option | 设置闯入区域多边形多点的坐标，无默认值 |
+| --do_break_in_counting | Option | 此项表示做区域闯入检查 |
 
 ## 方案介绍
 

deploy/pipeline/docs/tutorials/PPVehicle_QUICK_STARTED.md

@@ -135,6 +135,18 @@ python deploy/pipeline/pipeline.py --config deploy/pipeline/config/examples/infe
 python deploy/pipeline/pipeline.py --config deploy/pipeline/config/examples/infer_cfg_vehicle_attr.yml  -o visual=False --video_file=rtsp://[YOUR_RTSP_SITE] --device=gpu
 ```
 
+### Jetson部署说明
+
+由于Jetson平台算力相比服务器有较大差距，有如下使用建议：
+
+1. 模型选择轻量级版本，特别是跟踪模型，推荐使用`ppyoloe_s: https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_s_36e_pipeline.zip`
+2. 开启跟踪跳帧功能，推荐使用2或者3. `skip_frame_num: 3`
+
+使用该推荐配置，在TX2平台上可以达到较高速率，经测试属性案例达到20fps。
+
+可以直接修改配置文件（推荐），也可以在命令行中修改（字段较长，不推荐）。
+
+
 ### 参数说明
 
 | 参数 | 是否必须|含义 |
@@ -153,6 +165,9 @@ python deploy/pipeline/pipeline.py --config deploy/pipeline/config/examples/infe
 | --trt_calib_mode | Option| TensorRT是否使用校准功能，默认为False。使用TensorRT的int8功能时，需设置为True，使用PaddleSlim量化后的模型时需要设置为False |
 | --do_entrance_counting | Option | 是否统计出入口流量，默认为False |
 | --draw_center_traj | Option | 是否绘制跟踪轨迹，默认为False |
+| --region_type | Option | 'horizontal'（默认值）、'vertical'：表示流量统计方向选择；'custom'：表示设置车辆禁停区域 |
+| --region_polygon | Option | 设置禁停区域多边形多点的坐标，无默认值 |
+| --illegal_parking_time | Option | 设置禁停时间阈值，单位秒（s），-1（默认值）表示不做检查 |
 
 ## 方案介绍
 

deploy/pipeline/ppvehicle/vehicle_plate.py

@@ -298,6 +298,7 @@ class PlateRecognizer(object):
             '甘': 'GS-',
             '青': 'QH-',
             '宁': 'NX-',
+            '闽': 'FJ-',
             '·': ' '
         }
         for _char in text: