siton bug

522a602f · wangkx1 · abb99c90 · 522a602f · 522a602f · 522a602f
Commit 522a602f authored Jul 22, 2024 by wangkx1
20 changed files
--- a/configs/pphuman/pedestrian_yolov3/demo/004.png
+++ b/configs/pphuman/pedestrian_yolov3/demo/004.png
--- a/configs/pphuman/pedestrian_yolov3/pedestrian_yolov3_darknet.yml
+++ b/configs/pphuman/pedestrian_yolov3/pedestrian_yolov3_darknet.yml
+_BASE_: [
+  '../../datasets/coco_detection.yml',
+  '../../runtime.yml',
+  '../../yolov3/_base_/optimizer_270e.yml',
+  '../../yolov3/_base_/yolov3_darknet53.yml',
+  '../../yolov3/_base_/yolov3_reader.yml',
+]
+
+snapshot_epoch: 5
+weights: https://paddledet.bj.bcebos.com/models/pedestrian_yolov3_darknet.pdparams
+
+num_classes: 1
+
+TrainDataset:
+  !COCODataSet
+    dataset_dir: dataset/pedestrian
+    anno_path: annotations/instances_train2017.json
+    image_dir: train2017
+    data_fields: ['image', 'gt_bbox', 'gt_class', 'is_crowd']
+
+EvalDataset:
+  !COCODataSet
+    dataset_dir: dataset/pedestrian
+    anno_path: annotations/instances_val2017.json
+    image_dir: val2017
+
+TestDataset:
+  !ImageFolder
+    anno_path: configs/pphuman/pedestrian_yolov3/pedestrian.json
--- a/configs/pphuman/ppyoloe_crn_l_36e_crowdhuman.yml
+++ b/configs/pphuman/ppyoloe_crn_l_36e_crowdhuman.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_crowdhuman/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/crowdhuman
+    data_fields: ['image', 'gt_bbox', 'gt_class', 'is_crowd']
+
+EvalDataset:
+  !COCODataSet
+    image_dir: ""
+    anno_path: annotations/val.json
+    dataset_dir: dataset/crowdhuman
+
+TestDataset:
+  !ImageFolder
+    anno_path: annotations/val.json
+    dataset_dir: dataset/crowdhuman
+
+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
--- a/configs/pphuman/ppyoloe_crn_l_36e_pphuman.yml
+++ b/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
--- a/configs/pphuman/ppyoloe_crn_s_36e_crowdhuman.yml
+++ b/configs/pphuman/ppyoloe_crn_s_36e_crowdhuman.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_crowdhuman/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/crowdhuman
+    data_fields: ['image', 'gt_bbox', 'gt_class', 'is_crowd']
+
+EvalDataset:
+  !COCODataSet
+    image_dir: ""
+    anno_path: annotations/val.json
+    dataset_dir: dataset/crowdhuman
+
+TestDataset:
+  !ImageFolder
+    anno_path: annotations/val.json
+    dataset_dir: dataset/crowdhuman
+
+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
--- a/configs/pphuman/ppyoloe_crn_s_36e_pphuman.yml
+++ b/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
--- a/configs/pphuman/ppyoloe_crn_s_80e_smoking_visdrone.yml
+++ b/configs/pphuman/ppyoloe_crn_s_80e_smoking_visdrone.yml
+_BASE_: [
+  '../runtime.yml',
+  '../ppyoloe/_base_/optimizer_300e.yml',
+  '../ppyoloe/_base_/ppyoloe_crn.yml',
+  '../ppyoloe/_base_/ppyoloe_reader.yml',
+]
+
+log_iter: 100
+snapshot_epoch: 10
+weights: output/ppyoloe_crn_s_80e_smoking_visdrone/model_final
+
+pretrain_weights: https://paddledet.bj.bcebos.com/models/ppyoloe_crn_s_80e_visdrone.pdparams
+depth_mult: 0.33
+width_mult: 0.50
+
+TrainReader:
+  batch_size: 16
+
+LearningRate:
+  base_lr: 0.01
+
+epoch: 80
+LearningRate:
+  base_lr: 0.01
+  schedulers:
+    - !CosineDecay
+      max_epochs: 80
+    - !LinearWarmup
+      start_factor: 0.
+      epochs: 1
+
+PPYOLOEHead:
+  static_assigner_epoch: -1
+
+metric: COCO
+num_classes: 1
+
+TrainDataset:
+  !COCODataSet
+    image_dir: ""
+    anno_path: smoking_train_cocoformat.json
+    dataset_dir: dataset/smoking
+    data_fields: ['image', 'gt_bbox', 'gt_class', 'is_crowd']
+
+EvalDataset:
+  !COCODataSet
+    image_dir: ""
+    anno_path: smoking_test_cocoformat.json
+    dataset_dir: dataset/smoking
+
+TestDataset:
+  !ImageFolder
+    anno_path: smoking_test_cocoformat.json
+    dataset_dir: dataset/smoking
--- a/configs/pphuman/ppyoloe_plus_crn_t_auxhead_320_60e_pphuman.yml
+++ b/configs/pphuman/ppyoloe_plus_crn_t_auxhead_320_60e_pphuman.yml
+_BASE_: [
+  '../datasets/coco_detection.yml',
+  '../runtime.yml',
+  '../ppyoloe/_base_/optimizer_300e.yml',
+  '../ppyoloe/_base_/ppyoloe_plus_crn_tiny_auxhead.yml',
+  '../ppyoloe/_base_/ppyoloe_plus_reader_320.yml',
+]
+
+log_iter: 100
+snapshot_epoch: 4
+weights: output/ppyoloe_plus_crn_t_auxhead_320_60e_pphuman/model_final
+
+pretrain_weights: https://paddledet.bj.bcebos.com/models/ppyoloe_plus_crn_t_auxhead_300e_coco.pdparams # 640*640 COCO mAP 39.7
+depth_mult: 0.33
+width_mult: 0.375
+
+
+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: 60
+LearningRate:
+  base_lr: 0.001
+  schedulers:
+    - !CosineDecay
+      max_epochs: 72
+    - !LinearWarmup
+      start_factor: 0.
+      epochs: 1
+
+
+PPYOLOEHead:
+  static_assigner_epoch: -1
+  nms:
+    name: MultiClassNMS
+    nms_top_k: 1000
+    keep_top_k: 300
+    score_threshold: 0.01
+    nms_threshold: 0.7
--- a/configs/ppvehicle/README.md
+++ b/configs/ppvehicle/README.md
+简体中文 | [English](README.md)
+
+## 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)`。
+- BDD100K-MOT具体类别为8类，包括`pedestrian(1), rider(2), car(3), truck(4), bus(5), train(6), motorcycle(7), bicycle(8)`，但数据集比BDD100K-DET更大更多。
+- UA-DETRAC具体类别为4类，包括`car(1), bus(2), van(3), others(4)`。
+- PPVehicle9cls数据集整合了BDD100K-MOT和UA-DETRAC，具体类别为9类，包括`pedestrian(1), rider(2), car(3), truck(4), bus(5), van(6), motorcycle(7), bicycle(8), others(9)`。
+- PPVehicle数据集整合了BDD100K-MOT和UA-DETRAC，是将BDD100K-MOT中的`car, truck, bus, van`和UA-DETRAC中的`car, bus, van`都合并为1类`vehicle(1)`后的数据集。
+
+相关模型的部署模型均在[PP-Vehicle](../../deploy/pipeline/)项目中使用。
+
+|    模型   |       数据集     | 类别数  | mAP<sup>val<br>0.5:0.95 |  下载链接  | 配置文件 |
+|:---------|:---------------:|:------:|:-----------------------:|:---------:| :-----: |
+|PP-YOLOE-l|   BDD100K-DET   |   10   |  35.6 | [下载链接](https://paddledet.bj.bcebos.com/models/ppyoloe_crn_l_36e_bdd100kdet.pdparams) | [配置文件](./ppyoloe_crn_l_36e_bdd100kdet.yml) |
+|PP-YOLOE-l|   BDD100K-MOT   |   8    |  33.7 | [下载链接](https://paddledet.bj.bcebos.com/models/ppyoloe_crn_l_36e_bdd100kmot.pdparams) | [配置文件](./ppyoloe_crn_l_36e_bdd100kmot.yml) |
+|PP-YOLOE-l|   UA-DETRAC     |   4    |  51.4 | [下载链接](https://paddledet.bj.bcebos.com/models/ppyoloe_crn_l_36e_uadetrac.pdparams) | [配置文件](./ppyoloe_crn_l_36e_uadetrac.yml) |
+|PP-YOLOE-l|   PPVehicle9cls |   9    |  40.0 | [下载链接](https://paddledet.bj.bcebos.com/models/mot_ppyoloe_l_36e_ppvehicle9cls.pdparams) | [配置文件](./mot_ppyoloe_l_36e_ppvehicle9cls.yml) |
+|PP-YOLOE-s|   PPVehicle9cls |   9    |  35.3 | [下载链接](https://paddledet.bj.bcebos.com/models/mot_ppyoloe_s_36e_ppvehicle9cls.pdparams) | [配置文件](./mot_ppyoloe_s_36e_ppvehicle9cls.yml) |
+|PP-YOLOE-l|   PPVehicle     |   1    |  63.9 | [下载链接](https://paddledet.bj.bcebos.com/models/mot_ppyoloe_l_36e_ppvehicle.pdparams) | [配置文件](./mot_ppyoloe_l_36e_ppvehicle.yml) |
+|PP-YOLOE-s|   PPVehicle     |   1    |  61.3  | [下载链接](https://paddledet.bj.bcebos.com/models/mot_ppyoloe_s_36e_ppvehicle.pdparams) | [配置文件](./mot_ppyoloe_s_36e_ppvehicle.yml) |
+|PP-YOLOE+_t-aux(320)|   PPVehicle   |   1    |  53.5  | [下载链接](https://paddledet.bj.bcebos.com/models/ppyoloe_plus_crn_t_auxhead_320_60e_ppvehicle.pdparams) | [配置文件](./ppyoloe_plus_crn_t_auxhead_320_60e_ppvehicle.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)。
+- 如需预测出对应类别，可自行修改和添加对应的label_list.txt文件(一行记录一个对应种类)，TestDataset中的anno_path为绝对路径，如：
+```
+TestDataset:
+  !ImageFolder
+    anno_path: label_list.txt # 如不使用dataset_dir，则anno_path即为相对于PaddleDetection主目录的相对路径
+    # dataset_dir: dataset/ppvehicle # 如使用dataset_dir，则dataset_dir/anno_path作为新的anno_path
+```
+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,
+    author = {Yu, Fisher and Chen, Haofeng and Wang, Xin and Xian, Wenqi and Chen,
+              Yingying and Liu, Fangchen and Madhavan, Vashisht and Darrell, Trevor},
+    title = {BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning},
+    booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
+    month = {June},
+    year = {2020}
+}
+
+@article{CVIU_UA-DETRAC,
+    author = {Longyin Wen and Dawei Du and Zhaowei Cai and Zhen Lei and Ming{-}Ching Chang and
+              Honggang Qi and Jongwoo Lim and Ming{-}Hsuan Yang and Siwei Lyu},
+    title = {{UA-DETRAC:} {A} New Benchmark and Protocol for Multi-Object Detection and Tracking},
+    journal = {Computer Vision and Image Understanding},
+    year = {2020}
+}  
+```
--- a/configs/ppvehicle/mot_ppyoloe_l_36e_ppvehicle.yml
+++ b/configs/ppvehicle/mot_ppyoloe_l_36e_ppvehicle.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/mot_ppyoloe_l_36e_ppvehicle/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_all.json
+    dataset_dir: dataset/ppvehicle
+    data_fields: ['image', 'gt_bbox', 'gt_class', 'is_crowd']
+    allow_empty: true
+
+EvalDataset:
+  !COCODataSet
+    image_dir: ""
+    anno_path: annotations/val_all.json
+    dataset_dir: dataset/ppvehicle
+
+TestDataset:
+  !ImageFolder
+    anno_path: annotations/val_all.json
+    dataset_dir: dataset/ppvehicle
+
+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
--- a/configs/ppvehicle/mot_ppyoloe_l_36e_ppvehicle9cls.yml
+++ b/configs/ppvehicle/mot_ppyoloe_l_36e_ppvehicle9cls.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/mot_ppyoloe_l_36e_ppvehicle9cls/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: 9
+
+TrainDataset:
+  !COCODataSet
+    image_dir: ""
+    anno_path: annotations/train_all_9cls.json
+    dataset_dir: dataset/ppvehicle
+    data_fields: ['image', 'gt_bbox', 'gt_class', 'is_crowd']
+
+EvalDataset:
+  !COCODataSet
+    image_dir: ""
+    anno_path: annotations/val_all_9cls.json
+    dataset_dir: dataset/ppvehicle
+
+TestDataset:
+  !ImageFolder
+    anno_path: annotations/val_all_9cls.json
+    dataset_dir: dataset/ppvehicle
+
+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
--- a/configs/ppvehicle/mot_ppyoloe_s_36e_ppvehicle.yml
+++ b/configs/ppvehicle/mot_ppyoloe_s_36e_ppvehicle.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/mot_ppyoloe_s_36e_ppvehicle/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_all.json
+    dataset_dir: dataset/ppvehicle
+    data_fields: ['image', 'gt_bbox', 'gt_class', 'is_crowd']
+    allow_empty: true
+
+EvalDataset:
+  !COCODataSet
+    image_dir: ""
+    anno_path: annotations/val_all.json
+    dataset_dir: dataset/ppvehicle
+
+TestDataset:
+  !ImageFolder
+    anno_path: annotations/val_all.json
+    dataset_dir: dataset/ppvehicle
+
+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
--- a/configs/ppvehicle/mot_ppyoloe_s_36e_ppvehicle9cls.yml
+++ b/configs/ppvehicle/mot_ppyoloe_s_36e_ppvehicle9cls.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/mot_ppyoloe_s_36e_ppvehicle9cls/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: 9
+
+TrainDataset:
+  !COCODataSet
+    image_dir: ""
+    anno_path: annotations/train_all_9cls.json
+    dataset_dir: dataset/ppvehicle
+    data_fields: ['image', 'gt_bbox', 'gt_class', 'is_crowd']
+
+EvalDataset:
+  !COCODataSet
+    image_dir: ""
+    anno_path: annotations/val_all_9cls.json
+    dataset_dir: dataset/ppvehicle
+
+TestDataset:
+  !ImageFolder
+    anno_path: annotations/val_all_9cls.json
+    dataset_dir: dataset/ppvehicle
+
+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
--- a/configs/ppvehicle/ppyoloe_crn_l_36e_bdd100kdet.yml
+++ b/configs/ppvehicle/ppyoloe_crn_l_36e_bdd100kdet.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_bdd100kdet/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: 10
+
+TrainDataset:
+  !COCODataSet
+    image_dir: images/100k/train
+    anno_path: labels/det_20/det_train_cocofmt.json
+    dataset_dir: dataset/bdd100k
+    data_fields: ['image', 'gt_bbox', 'gt_class', 'is_crowd']
+
+EvalDataset:
+  !COCODataSet
+    image_dir: images/100k/val
+    anno_path: labels/det_20/det_val_cocofmt.json
+    dataset_dir: dataset/bdd100k
+
+TestDataset:
+  !ImageFolder
+    anno_path: labels/det_20/det_val_cocofmt.json
+    dataset_dir: dataset/bdd100k
+
+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
--- a/configs/ppvehicle/ppyoloe_crn_l_36e_bdd100kmot.yml
+++ b/configs/ppvehicle/ppyoloe_crn_l_36e_bdd100kmot.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_bdd100kmot/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: 8
+
+TrainDataset:
+  !COCODataSet
+    image_dir: ""
+    anno_path: annotations/train.json
+    dataset_dir: dataset/bdd100k
+    data_fields: ['image', 'gt_bbox', 'gt_class', 'is_crowd']
+
+EvalDataset:
+  !COCODataSet
+    image_dir: ""
+    anno_path: annotations/val.json
+    dataset_dir: dataset/bdd100k
+
+TestDataset:
+  !ImageFolder
+    anno_path: annotations/val.json
+    dataset_dir: dataset/bdd100k
+
+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
--- a/configs/ppvehicle/ppyoloe_crn_l_36e_uadetrac.yml
+++ b/configs/ppvehicle/ppyoloe_crn_l_36e_uadetrac.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_uadetrac/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: 4
+
+TrainDataset:
+  !COCODataSet
+    image_dir: train
+    anno_path: annotations/train.json
+    dataset_dir: dataset/uadetrac
+    data_fields: ['image', 'gt_bbox', 'gt_class', 'is_crowd']
+
+EvalDataset:
+  !COCODataSet
+    image_dir: val
+    anno_path: annotations/test.json
+    dataset_dir: dataset/uadetrac
+
+TestDataset:
+  !ImageFolder
+    anno_path: annotations/test.json
+    dataset_dir: dataset/uadetrac
+
+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
--- a/configs/ppvehicle/ppyoloe_plus_crn_t_auxhead_320_60e_ppvehicle.yml
+++ b/configs/ppvehicle/ppyoloe_plus_crn_t_auxhead_320_60e_ppvehicle.yml
+_BASE_: [
+  '../datasets/coco_detection.yml',
+  '../runtime.yml',
+  '../ppyoloe/_base_/optimizer_300e.yml',
+  '../ppyoloe/_base_/ppyoloe_plus_crn_tiny_auxhead.yml',
+  '../ppyoloe/_base_/ppyoloe_plus_reader_320.yml',
+]
+
+log_iter: 100
+snapshot_epoch: 4
+weights: output/ppyoloe_plus_crn_t_auxhead_320_60e_ppvehicle/model_final
+
+pretrain_weights: https://paddledet.bj.bcebos.com/models/ppyoloe_plus_crn_t_auxhead_300e_coco.pdparams # 640*640 COCO mAP 39.7
+depth_mult: 0.33
+width_mult: 0.375
+
+
+num_classes: 1
+TrainDataset:
+  !COCODataSet
+    image_dir: ""
+    anno_path: annotations/train_all.json
+    dataset_dir: dataset/ppvehicle
+    data_fields: ['image', 'gt_bbox', 'gt_class', 'is_crowd']
+    allow_empty: true
+
+EvalDataset:
+  !COCODataSet
+    image_dir: ""
+    anno_path: annotations/val_all.json
+    dataset_dir: dataset/ppvehicle
+
+TestDataset:
+  !ImageFolder
+    anno_path: annotations/val_all.json
+    dataset_dir: dataset/ppvehicle
+
+
+TrainReader:
+  batch_size: 8
+
+
+epoch: 60
+LearningRate:
+  base_lr: 0.001
+  schedulers:
+    - !CosineDecay
+      max_epochs: 72
+    - !LinearWarmup
+      start_factor: 0.
+      epochs: 1
+
+
+PPYOLOEHead:
+  static_assigner_epoch: -1
+  nms:
+    name: MultiClassNMS
+    nms_top_k: 1000
+    keep_top_k: 300
+    score_threshold: 0.01
+    nms_threshold: 0.7
--- a/configs/ppvehicle/vehicle_yolov3/README.md
+++ b/configs/ppvehicle/vehicle_yolov3/README.md
+English | [简体中文](README_cn.md)
+# PaddleDetection applied for specific scenarios
+
+We provide some models implemented by PaddlePaddle to detect objects in specific scenarios, users can download the models and use them in these scenarios.
+
+| Task                 | Algorithm | Box AP | Download                                                                                | Configs |
+|:---------------------|:---------:|:------:| :-------------------------------------------------------------------------------------: |:------:|
+| Vehicle Detection    |  YOLOv3  |  54.5  | [model](https://paddledet.bj.bcebos.com/models/vehicle_yolov3_darknet.pdparams) | [config](./vehicle_yolov3_darknet.yml) |
+
+## Vehicle Detection
+
+One of major applications of vehichle detection is traffic monitoring. In this scenary, vehicles to be detected are mostly captured by the cameras mounted on top of traffic light columns.
+
+### 1. Network
+
+The network for detecting vehicles is YOLOv3, the backbone of which is Dacknet53.
+
+### 2. Configuration for training
+
+PaddleDetection provides users with a configuration file [yolov3_darknet53_270e_coco.yml](https://github.com/PaddlePaddle/PaddleDetection/blob/develop/configs/yolov3/yolov3_darknet53_270e_coco.yml) to train YOLOv3 on the COCO dataset, compared with this file, we modify some parameters as followed to conduct the training for vehicle detection:
+
+* num_classes: 6
+* anchors: [[8, 9], [10, 23], [19, 15], [23, 33], [40, 25], [54, 50], [101, 80], [139, 145], [253, 224]]
+* nms/nms_top_k: 400
+* nms/score_threshold: 0.005
+* dataset_dir: dataset/vehicle
+
+### 3. Accuracy
+
+The accuracy of the model trained and evaluated on our private data is shown as followed:
+
+AP at IoU=.50:.05:.95 is 0.545.
+
+AP at IoU=.50 is 0.764.
+
+### 4. Inference
+
+Users can employ the model to conduct the inference:
+
+```
+export CUDA_VISIBLE_DEVICES=0
+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/ppvehicle/vehicle_yolov3/demo \
+                         --draw_threshold 0.2 \
+                         --output_dir configs/ppvehicle/vehicle_yolov3/demo/output
+```
+
+Some inference results are visualized below:
+
+![](../../../docs/images/VehicleDetection_001.jpeg)
+
+![](../../../docs/images/VehicleDetection_005.png)
--- a/configs/ppvehicle/vehicle_yolov3/README_cn.md
+++ b/configs/ppvehicle/vehicle_yolov3/README_cn.md
+[English](README.md) | 简体中文
+# 特色垂类检测模型
+
+我们提供了针对不同场景的基于PaddlePaddle的检测模型，用户可以下载模型进行使用。
+
+| 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](./vehicle_yolov3_darknet.yml) |
+
+
+## 车辆检测（Vehicle Detection）
+
+车辆检测的主要应用之一是交通监控。在这样的监控场景中，待检测的车辆多为道路红绿灯柱上的摄像头拍摄所得。
+
+### 1. 模型结构
+
+Backbone为Dacknet53的YOLOv3。
+
+### 2. 训练参数配置
+
+PaddleDetection提供了使用COCO数据集对YOLOv3进行训练的参数配置文件[yolov3_darknet53_270e_coco.yml](https://github.com/PaddlePaddle/PaddleDetection/blob/develop/configs/yolov3/yolov3_darknet53_270e_coco.yml)，与之相比，在进行车辆检测的模型训练时，我们对以下参数进行了修改：
+
+* num_classes: 6
+* anchors: [[8, 9], [10, 23], [19, 15], [23, 33], [40, 25], [54, 50], [101, 80], [139, 145], [253, 224]]
+* nms/nms_top_k: 400
+* nms/score_threshold: 0.005
+* dataset_dir: dataset/vehicle
+
+### 3. 精度指标
+
+模型在我们内部数据上的精度指标为：
+
+IOU=.50:.05:.95时的AP为 0.545。
+
+IOU=.5时的AP为 0.764。
+
+### 4. 预测
+
+用户可以使用我们训练好的模型进行车辆检测：
+
+```
+export CUDA_VISIBLE_DEVICES=0
+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/ppvehicle/vehicle_yolov3/demo \
+                         --draw_threshold 0.2 \
+                         --output_dir configs/ppvehicle/vehicle_yolov3/demo/output
+```
+
+预测结果示例：
+
+![](../../../docs/images/VehicleDetection_001.jpeg)
+
+![](../../../docs/images/VehicleDetection_005.png)
--- a/configs/ppvehicle/vehicle_yolov3/demo/001.jpeg
+++ b/configs/ppvehicle/vehicle_yolov3/demo/001.jpeg