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PaddleDetection
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README_cn.md
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| 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 | <div style="width: 50pt">云边端</div> | 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) | - |
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</details>
<details>
<summary><b> 5. 产业级实时行人分析工具</b></summary>
<summary><b> 5. 产业级实时行人分析工具PP-Human </b></summary>
| 任务 | 端到端速度（ms）| 模型方案 | 模型体积 |
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| 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)<br>[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<br>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)
</details>
<details>
<summary><b> 6. 产业级实时车辆分析工具PP-Vehicle </b></summary>
| 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) <br> [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 <br> 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)
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README_en.md
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- 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)
<div align="center">
<img src="https://user-images.githubusercontent.com/22989727/183843004-baebf75f-af7c-4a7c-8130-1497b9a3ec7e.png" width = "200" />
<img src="https://user-images.githubusercontent.com/34162360/177678712-4655747d-4290-4ad9-b7a1-4564a5418ac6.jpg" width = "200" />
</div>
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| 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) | - |
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<details>
<summary><b> 5. Industrial real-time pedestrain analysis tool-PP Human</b></summary>
| 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)<br> [Attribute recognition](https://bj.bcebos.com/v1/paddledet/models/pipeline/strongbaseline_r50_30e_pa100k.zip) | Object detection：182M<br>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)<br> [Attribute recognition](https://bj.bcebos.com/v1/paddledet/models/pipeline/strongbaseline_r50_30e_pa100k.zip) | Object detection：182M<br>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) <br> [Keypoint detection](https://bj.bcebos.com/v1/paddledet/models/pipeline/dark_hrnet_w32_256x192.zip) <br> [Behavior detection based on key points](https://bj.bcebos.com/v1/paddledet/models/pipeline/STGCN.zip) | Multi-object tracking：182M<br>Keypoint detection：101M<br>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)<br>[Object detection based on Human Id](https://bj.bcebos.com/v1/paddledet/models/pipeline/ppyoloe_crn_s_80e_smoking_visdrone.zip) | Object detection：182M<br>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)<br>[Image classification based on Human ID](https://bj.bcebos.com/v1/paddledet/models/pipeline/PPHGNet_tiny_calling_halfbody.zip) | Object detection：182M<br>Image classification based on Human ID：45M |
Please refer to [docs](deploy/pipeline/README_en.md) for details.
</details>
<details>
<summary><b> 6. Industrial real-time vehicle analysis tool-PP Vehicle</b></summary>
| 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)<br>[plate recognition](https://bj.bcebos.com/v1/paddledet/models/pipeline/ch_PP-OCRv3_rec_infer.tar.gz) | Plate detection：3.9M<br>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.
</details>
**Click “ ✅ ” to download**
## <img src="https://user-images.githubusercontent.com/48054808/157828296-d5eb0ccb-23ea-40f5-9957-29853d7d13a9.png" width="20"/>Document tutorials
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deploy/pipeline/README_en.md
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## 🗳 Model Zoo
<details>
<summary><b> Single model results (click to expand) </b></summary>
| 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) |
</details>
<details>
<summary><b>End-to-end model results (click to expand)</b></summary>
<summary><b>PP-Human End-to-end model results (click to expand)</b></summary>
| 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
</details>
<details>
<summary><b>PP-Vehicle End-to-end model results (click to expand)</b></summary>
| 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)<br>[plate recognition](https://bj.bcebos.com/v1/paddledet/models/pipeline/ch_PP-OCRv3_rec_infer.tar.gz) | Plate detection：3.9M<br>Plate recognition：12M |
| Vehicle attribute | 7.31ms | [attribute recognition](https://bj.bcebos.com/v1/paddledet/models/pipeline/vehicle_attribute_model.zip) | 7.2M |
</details>
Click to download the model, then unzip and save it in the `. /output_inference`.
## 📚 Doc Tutorials
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