From f5c48604ec2b76e05e3f410a3c0ec87d23c61b6e Mon Sep 17 00:00:00 2001 From: wangguanzhong Date: Tue, 30 Aug 2022 16:42:36 +0800 Subject: [PATCH] update vehicle doc, test=document_fix (#6808) --- README_cn.md | 22 ++++++++++++++--- README_en.md | 47 ++++++++++++++++++++++++------------ deploy/pipeline/README_en.md | 37 ++++++++++++---------------- 3 files changed, 67 insertions(+), 39 deletions(-) diff --git a/README_cn.md b/README_cn.md index d22a30f15..6956e8df6 100644 --- a/README_cn.md +++ b/README_cn.md @@ -354,9 +354,7 @@ | 模型名称 | 模型简介 | 推荐场景 | 精度 | 配置文件 | 模型下载 | |:--------- |:------------------------ |:---------------------------------- |:----------------------:|:---------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------:| -| DeepSORT | SDE多目标跟踪算法 检测、ReID模型相互独立 |
云边端
| MOT-17 half val: 66.9 | [链接](configs/mot/deepsort/deepsort_jde_yolov3_pcb_pyramid.yml) | [下载地址](https://paddledet.bj.bcebos.com/models/mot/deepsort/deepsort_pcb_pyramid_r101.pdparams) | | ByteTrack | SDE多目标跟踪算法 仅包含检测模型 | 云边端 | MOT-17 half val: 77.3 | [链接](configs/mot/bytetrack/detector/yolox_x_24e_800x1440_mix_det.yml) | [下载地址](https://paddledet.bj.bcebos.com/models/mot/deepsort/yolox_x_24e_800x1440_mix_det.pdparams) | -| JDE | JDE多目标跟踪算法 多任务联合学习方法 | 云边端 | MOT-16 test: 64.6 | [链接](configs/mot/jde/jde_darknet53_30e_1088x608.yml) | [下载地址](https://paddledet.bj.bcebos.com/models/mot/jde_darknet53_30e_1088x608.pdparams) | | FairMOT | JDE多目标跟踪算法 多任务联合学习方法 | 云边端 | MOT-16 test: 75.0 | [链接](configs/mot/fairmot/fairmot_dla34_30e_1088x608.yml) | [下载地址](https://paddledet.bj.bcebos.com/models/mot/fairmot_dla34_30e_1088x608.pdparams) | | OC-SORT | SDE多目标跟踪算法 仅包含检测模型 | 云边端 | MOT-17 half val: 75.5 | [链接](configs/mot/ocsort/ocsort_yolox.yml) | - | @@ -365,7 +363,7 @@
- 5. 产业级实时行人分析工具 + 5. 产业级实时行人分析工具PP-Human | 任务 | 端到端速度（ms）| 模型方案 | 模型体积 | @@ -383,6 +381,24 @@ | 打电话识别 | 单人ms | [目标检测](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_l_36e_pipeline.zip)
[基于人体id的图像分类](https://bj.bcebos.com/v1/paddledet/models/pipeline/PPHGNet_tiny_calling_halfbody.zip) | 目标检测：182M
基于人体id的图像分类：45M | +点击模型方案中的模型即可下载指定模型 + +详细信息参考[文档](deploy/pipeline) + +
+ +
+ 6. 产业级实时车辆分析工具PP-Vehicle + +| 任务 | 端到端速度（ms）| 模型方案 | 模型体积 | +| :---------: | :-------: | :------: |:------: | +| 车辆检测（高精度） | 25.7ms | [目标检测](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_l_36e_ppvehicle.zip) | 182M | +| 车辆检测（轻量级） | 13.2ms | [目标检测](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_s_36e_ppvehicle.zip) | 27M | +| 车辆跟踪（高精度） | 40ms | [多目标跟踪](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_l_36e_ppvehicle.zip) | 182M | +| 车辆跟踪（轻量级） | 25ms | [多目标跟踪](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_s_36e_ppvehicle.zip) | 27M | +| 车牌识别 | 4.68ms | [车牌检测](https://bj.bcebos.com/v1/paddledet/models/pipeline/ch_PP-OCRv3_det_infer.tar.gz)
[车牌识别](https://bj.bcebos.com/v1/paddledet/models/pipeline/ch_PP-OCRv3_rec_infer.tar.gz) | 车牌检测：3.9M
车牌字符识别： 12M | +| 车辆属性 | 7.31ms | [属性识别](https://bj.bcebos.com/v1/paddledet/models/pipeline/vehicle_attribute_model.zip) | 7.2M | + 点击模型方案中的模型即可下载指定模型 详细信息参考[文档](deploy/pipeline) diff --git a/README_en.md b/README_en.md index c10787939..adbffa72b 100644 --- a/README_en.md +++ b/README_en.md @@ -76,10 +76,9 @@ - If you have any question or suggestion, please give us your valuable input via [GitHub Issues](https://github.com/PaddlePaddle/PaddleDetection/issues) - Welcome to join PaddleDetection user groups on QQ, WeChat (scan the QR code, add and reply "D" to the assistant) + Welcome to join PaddleDetection user groups on WeChat (scan the QR code, add and reply "D" to the assistant)
-
@@ -354,9 +353,7 @@ The comparison between COCO mAP and FPS on Qualcomm Snapdragon 865 processor of | Model | Introduction | Recommended scenarios | Accuracy | Configuration | Download | |:--------- |:------------------------------------------------------------- |:--------------------- |:----------------------:|:-----------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------:| -| DeepSORT | SDE Multi-object tracking algorithm, independent ReID models | Edge-Cloud end | MOT-17 half val: 66.9 | [Link](configs/mot/deepsort/deepsort_jde_yolov3_pcb_pyramid.yml) | [Download](https://paddledet.bj.bcebos.com/models/mot/deepsort/deepsort_pcb_pyramid_r101.pdparams) | | ByteTrack | SDE Multi-object tracking algorithm with detection model only | Edge-Cloud end | MOT-17 half val: 77.3 | [Link](configs/mot/bytetrack/detector/yolox_x_24e_800x1440_mix_det.yml) | [Download](https://paddledet.bj.bcebos.com/models/mot/deepsort/yolox_x_24e_800x1440_mix_det.pdparams) | -| JDE | JDE multi-object tracking algorithm multi-task learning | Edge-Cloud end | MOT-16 test: 64.6 | [Link](configs/mot/jde/jde_darknet53_30e_1088x608.yml) | [Download](https://paddledet.bj.bcebos.com/models/mot/jde_darknet53_30e_1088x608.pdparams) | | FairMOT | JDE multi-object tracking algorithm multi-task learning | Edge-Cloud end | MOT-16 test: 75.0 | [Link](configs/mot/fairmot/fairmot_dla34_30e_1088x608.yml) | [Download](https://paddledet.bj.bcebos.com/models/mot/fairmot_dla34_30e_1088x608.pdparams) | | OC-SORT | SDE multi-object tracking algorithm with detection model only | Edge-Cloud end | MOT-16 half val: 75.5 | [Link](configs/mot/ocsort/ocsort_yolox.yml) | - | @@ -367,20 +364,40 @@ The comparison between COCO mAP and FPS on Qualcomm Snapdragon 865 processor of
5. Industrial real-time pedestrain analysis tool-PP Human -| Function \ Model | Obejct detection | Multi- object tracking | Attribute recognition | Keypoint detection | Action recognition | ReID | -|:------------------------------------ |:-------------------------------------------------------------------------------------- |:-------------------------------------------------------------------------------------- |:-----------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------:|:-----------------------------------------------------------------:|:----------------------------------------------------------------------:| -| Pedestrian Detection | [✅](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_l_36e_pipeline.zip) | | | | | | -| Pedestrian Tracking | | [✅](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_l_36e_pipeline.zip) | | | | | -| Attribute Recognition (Image) | [✅](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_l_36e_pipeline.zip) | | [✅](https://bj.bcebos.com/v1/paddledet/models/pipeline/strongbaseline_r50_30e_pa100k.zip) | | | | -| Attribute Recognition (Video) | | [✅](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_l_36e_pipeline.zip) | | | | | -| Falling Detection | | [✅](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_l_36e_pipeline.zip) | | [✅](https://bj.bcebos.com/v1/paddledet/models/pipeline/strongbaseline_r50_30e_pa100k.zip) | [✅](https://bj.bcebos.com/v1/paddledet/models/pipeline/STGCN.zip) | | -| ReID | | [✅](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_l_36e_pipeline.zip) | | | | [✅](https://bj.bcebos.com/v1/paddledet/models/pipeline/reid_model.zip) | -| **Accuracy** | mAP 56.3 | MOTA 72.0 | mA 94.86 | AP 87.1 | AP 96.43 | mAP 98.8 | -| **T4 TensorRT FP16 Inference speed** | 28.0ms | 33.1ms | Single person 2ms | Single person 2.9ms | Single person 2.7ms | Single person 1.5ms | +| Task | End-to-End Speed（ms） | Model | Size | +|:--------------------------------------:|:--------------------:|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------:| +| Pedestrian detection (high precision) | 25.1ms | [Multi-object tracking](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_l_36e_pipeline.zip) | 182M | +| Pedestrian detection (lightweight) | 16.2ms | [Multi-object tracking](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_s_36e_pipeline.zip) | 27M | +| Pedestrian tracking (high precision) | 31.8ms | [Multi-object tracking](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_l_36e_pipeline.zip) | 182M | +| Pedestrian tracking (lightweight) | 21.0ms | [Multi-object tracking](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_s_36e_pipeline.zip) | 27M | +| Attribute recognition (high precision) | Single person8.5ms | [Object detection](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_l_36e_pipeline.zip)
[Attribute recognition](https://bj.bcebos.com/v1/paddledet/models/pipeline/strongbaseline_r50_30e_pa100k.zip) | Object detection：182M
Attribute recognition：86M | +| Attribute recognition (lightweight) | Single person 7.1ms | [Object detection](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_l_36e_pipeline.zip)
[Attribute recognition](https://bj.bcebos.com/v1/paddledet/models/pipeline/strongbaseline_r50_30e_pa100k.zip) | Object detection：182M
Attribute recognition：86M | +| Falling detection | Single person 10ms | [Multi-object tracking](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_l_36e_pipeline.zip)
[Keypoint detection](https://bj.bcebos.com/v1/paddledet/models/pipeline/dark_hrnet_w32_256x192.zip)
[Behavior detection based on key points](https://bj.bcebos.com/v1/paddledet/models/pipeline/STGCN.zip) | Multi-object tracking：182M
Keypoint detection：101M
Behavior detection based on key points: 21.8M | +| Intrusion detection | 31.8ms | [Multi-object tracking](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_l_36e_pipeline.zip) | 182M | +| Fighting detection | 19.7ms | [Video classification](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_l_36e_pipeline.zip) | 90M | +| Smoking detection | Single person 15.1ms | [Object detection](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_l_36e_pipeline.zip)
[Object detection based on Human Id](https://bj.bcebos.com/v1/paddledet/models/pipeline/ppyoloe_crn_s_80e_smoking_visdrone.zip) | Object detection：182M
Object detection based on Human ID: 27M | +| Phoning detection | Single person ms | [Object detection](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_l_36e_pipeline.zip)
[Image classification based on Human ID](https://bj.bcebos.com/v1/paddledet/models/pipeline/PPHGNet_tiny_calling_halfbody.zip) | Object detection：182M
Image classification based on Human ID：45M | + +Please refer to [docs](deploy/pipeline/README_en.md) for details. + +
+ +
+ 6. Industrial real-time vehicle analysis tool-PP Vehicle + +| Task | End-to-End Speed（ms） | Model | Size | +|:--------------------------------------:|:--------------------:|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------:| +| Vehicle detection (high precision) | 25.7ms | [object detection](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_l_36e_ppvehicle.zip) | 182M | +| Vehicle detection (lightweight) | 13.2ms | [object detection](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_s_36e_ppvehicle.zip) | 27M | +| Vehicle tracking (high precision) | 40ms | [multi-object tracking](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_l_36e_ppvehicle.zip) | 182M | +| Vehicle tracking (lightweight) | 25ms | [multi-object tracking](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_s_36e_pipeline.zip) | 27M | +| Plate Recognition | 4.68ms | [plate detection](https://bj.bcebos.com/v1/paddledet/models/pipeline/ch_PP-OCRv3_det_infer.tar.gz)
[plate recognition](https://bj.bcebos.com/v1/paddledet/models/pipeline/ch_PP-OCRv3_rec_infer.tar.gz) | Plate detection：3.9M
Plate recognition：12M | +| Vehicle attribute | 7.31ms | [attribute recognition](https://bj.bcebos.com/v1/paddledet/models/pipeline/vehicle_attribute_model.zip) | 7.2M | + +Please refer to [docs](deploy/pipeline/README_en.md) for details.
-**Click “ ✅ ” to download** ## Document tutorials diff --git a/deploy/pipeline/README_en.md b/deploy/pipeline/README_en.md index 05e8b38fb..64383da09 100644 --- a/deploy/pipeline/README_en.md +++ b/deploy/pipeline/README_en.md @@ -32,27 +32,7 @@ PP-Human supports various inputs such as images, single-camera, and multi-camera ## 🗳 Model Zoo
- Single model results (click to expand) - -| Task | Application | Accuracy | Inference speed（ms） | Model size | Inference deployment model | -|:-------------------------------------------:|:---------------------------------------:|:--------------- |:--------------------:|:----------:|:-------------------------------------------------------------------------------------------------------:| -| Object detection (high precision) | Image input | mAP: 57.8 | 25.1ms | 182M | [Link](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_l_36e_pipeline.zip) | -| Object detection (Lightweight) | Image input | mAP: 53.2 | 16.2ms | 27M | [Link](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_s_36e_pipeline.zip) | -| Object tracking (high precision) | Video input | MOTA: 82.2 | 31.8ms | 182M | [Link](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_l_36e_pipeline.zip) | -| Object tracking (high precision) | Video input | MOTA: 73.9 | 21.0ms | 27M | [Link](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_s_36e_pipeline.zip) | -| Attribute recognition (high precision) | Image/Video input Attribute recognition | mA: 95.4 | Single person 4.2ms | 86M | [Link](https://bj.bcebos.com/v1/paddledet/models/pipeline/PPHGNet_small_person_attribute_954_infer.zip) | -| Attribute recognition (Lightweight) | Image/Video input Attribute recognition | mA: 94.5 | Single person 2.9ms | 7.2M | [Link](https://bj.bcebos.com/v1/paddledet/models/pipeline/PPLCNet_x1_0_person_attribute_945_infer.zip) | -| Keypoint detection | Video input Attribute recognition | AP: 87.1 | Single person 5.7ms | 101M | [Link](https://bj.bcebos.com/v1/paddledet/models/pipeline/dark_hrnet_w32_256x192.zip) | -| Classification based on key point sequences | Video input Attribute recognition | Accuracy: 96.43 | Single person 0.07ms | 21.8M | [Link](https://bj.bcebos.com/v1/paddledet/models/pipeline/STGCN.zip) | -| Detection based on Human ID | Video input Attribute recognition | Accuracy: 86.85 | Single person 1.8ms | 45M | [Link](https://bj.bcebos.com/v1/paddledet/models/pipeline/PPHGNet_tiny_calling_halfbody.zip) | -| Detection based on Human ID | Video input Attribute recognition | AP50: 79.5 | Single person 10.9ms | 27M | [Link](https://bj.bcebos.com/v1/paddledet/models/pipeline/ppyoloe_crn_s_80e_smoking_visdrone.zip) | -| Video classification | Video input Attribute recognition | Accuracy： 89.0 | 19.7ms/1s Video | 90M | [Link](https://videotag.bj.bcebos.com/PaddleVideo-release2.3/ppTSM_fight.pdparams) | -| ReID | Video input ReID | mAP: 98.8 | Single person 0.23ms | 85M | [Link](https://bj.bcebos.com/v1/paddledet/models/pipeline/reid_model.zip) | - -
- -
-End-to-end model results (click to expand) +PP-Human End-to-end model results (click to expand) | Task | End-to-End Speed（ms） | Model | Size | |:--------------------------------------:|:--------------------:|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------:| @@ -70,6 +50,21 @@ PP-Human supports various inputs such as images, single-camera, and multi-camera
+
+PP-Vehicle End-to-end model results (click to expand) + +| Task | End-to-End Speed（ms） | Model | Size | +|:--------------------------------------:|:--------------------:|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------:| +| Vehicle detection (high precision) | 25.7ms | [object detection](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_l_36e_ppvehicle.zip) | 182M | +| Vehicle detection (lightweight) | 13.2ms | [object detection](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_s_36e_ppvehicle.zip) | 27M | +| Vehicle tracking (high precision) | 40ms | [multi-object tracking](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_l_36e_ppvehicle.zip) | 182M | +| Vehicle tracking (lightweight) | 25ms | [multi-object tracking](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_s_36e_pipeline.zip) | 27M | +| Plate Recognition | 4.68ms | [plate detection](https://bj.bcebos.com/v1/paddledet/models/pipeline/ch_PP-OCRv3_det_infer.tar.gz)
[plate recognition](https://bj.bcebos.com/v1/paddledet/models/pipeline/ch_PP-OCRv3_rec_infer.tar.gz) | Plate detection：3.9M
Plate recognition：12M | +| Vehicle attribute | 7.31ms | [attribute recognition](https://bj.bcebos.com/v1/paddledet/models/pipeline/vehicle_attribute_model.zip) | 7.2M | + +
+ + Click to download the model, then unzip and save it in the `. /output_inference`. ## 📚 Doc Tutorials -- GitLab