File size: 43,684 Bytes
7b7527a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 | [简体中文](README_cn.md) | English
<div align="center">
<p align="center">
<img src="https://user-images.githubusercontent.com/48054808/160532560-34cf7a1f-d950-435e-90d2-4b0a679e5119.png" align="middle" width = "800" />
</p>
**A High-Efficient Development Toolkit for Object Detection based on [PaddlePaddle](https://github.com/paddlepaddle/paddle)**
<p align="center">
<a href="./LICENSE"><img src="https://img.shields.io/badge/license-Apache%202-dfd.svg"></a>
<a href="https://github.com/PaddlePaddle/PaddleDetection/releases"><img src="https://img.shields.io/github/v/release/PaddlePaddle/PaddleDetection?color=ffa"></a>
<a href=""><img src="https://img.shields.io/badge/python-3.7+-aff.svg"></a>
<a href=""><img src="https://img.shields.io/badge/os-linux%2C%20win%2C%20mac-pink.svg"></a>
<a href="https://github.com/PaddlePaddle/PaddleDetection/stargazers"><img src="https://img.shields.io/github/stars/PaddlePaddle/PaddleDetection?color=ccf"></a>
</p>
</div>
<div align="center">
<img src="https://user-images.githubusercontent.com/22989727/205581915-aa8d6bee-5624-4aec-8059-76b5ebaf96f1.gif" width="800"/>
</div>
## <img src="https://user-images.githubusercontent.com/48054808/157793354-6e7f381a-0aa6-4bb7-845c-9acf2ecc05c3.png" width="20"/> Product Update
- 🔥 **2022.11.15：SOTA rotated object detector and small object detector based on PP-YOLOE**
- Rotated object detector [PP-YOLOE-R](configs/rotate/ppyoloe_r)
- SOTA Anchor-free rotated object detection model with high accuracy and efficiency
- A series of models, named s/m/l/x, for cloud and edge devices
- Avoiding using special operators to be deployed friendly with TensorRT.
- Small object detector [PP-YOLOE-SOD](configs/smalldet)
- End-to-end detection pipeline based on sliced images
- SOTA model on VisDrone based on original images.
- 2022.8.26：PaddleDetection releases[release/2.5 version](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.5)
- 🗳 Model features：
- Release [PP-YOLOE+](configs/ppyoloe): Increased accuracy by a maximum of 2.4% mAP to 54.9% mAP, 3.75 times faster model training convergence rate, and up to 2.3 times faster end-to-end inference speed; improved generalization for multiple downstream tasks
- Release [PicoDet-NPU](configs/picodet) model which supports full quantization deployment of models; add [PicoDet](configs/picodet) layout analysis model
- Release [PP-TinyPose Plus](./configs/keypoint/tiny_pose/). With 9.1% AP accuracy improvement in physical exercise, dance, and other scenarios, our PP-TinyPose Plus supports unconventional movements such as turning to one side, lying down, jumping, and high lifts
- 🔮 Functions in different scenarios
- Release the pedestrian analysis tool [PP-Human v2](./deploy/pipeline). It introduces four new behavior recognition: fighting, telephoning, smoking, and trespassing. The underlying algorithm performance is optimized, covering three core algorithm capabilities: detection, tracking, and attributes of pedestrians. Our model provides end-to-end development and model optimization strategies for beginners and supports online video streaming input.
- First release [PP-Vehicle](./deploy/pipeline), which has four major functions: license plate recognition, vehicle attribute analysis (color, model), traffic flow statistics, and violation detection. It is compatible with input formats, including pictures, online video streaming, and video. And we also offer our users a comprehensive set of tutorials for customization.
- 💡 Cutting-edge algorithms：
- Release [PaddleYOLO](https://github.com/PaddlePaddle/PaddleYOLO) which overs classic and latest models of [YOLO family](https://github.com/PaddlePaddle/PaddleYOLO/tree/develop/docs/MODEL_ZOO_en.md): YOLOv3, PP-YOLOE (a real-time high-precision object detection model developed by Baidu PaddlePaddle), and cutting-edge detection algorithms such as YOLOv4, YOLOv5, YOLOX, YOLOv6, YOLOv7 and YOLOv8
- Newly add high precision detection model based on [ViT](configs/vitdet) backbone network, with a 55.7% mAP accuracy on COCO dataset; newly add multi-object tracking model [OC-SORT](configs/mot/ocsort); newly add [ConvNeXt](configs/convnext) backbone network.
- 📋 Industrial applications: Newly add [Smart Fitness](https://aistudio.baidu.com/aistudio/projectdetail/4385813), [Fighting recognition](https://aistudio.baidu.com/aistudio/projectdetail/4086987?channelType=0&channel=0),[ and Visitor Analysis](https://aistudio.baidu.com/aistudio/projectdetail/4230123?channelType=0&channel=0).
- 2022.3.24：PaddleDetection released[release/2.4 version](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4)
- Release high-performanace SOTA object detection model [PP-YOLOE](configs/ppyoloe). It integrates cloud and edge devices and provides S/M/L/X versions. In particular, Verson L has the accuracy as 51.4% on COCO test 2017 dataset, inference speed as 78.1 FPS on a single Test V100. It supports mixed precision training, 33% faster than PP-YOLOv2. Its full range of multi-sized models can meet different hardware arithmetic requirements, and adaptable to server, edge-device GPU and other AI accelerator cards on servers.
- Release ultra-lightweight SOTA object detection model [PP-PicoDet Plus](configs/picodet) with 2% improvement in accuracy and 63% improvement in CPU inference speed. Add PicoDet-XS model with a 0.7M parameter, providing model sparsification and quantization functions for model acceleration. No specific post processing module is required for all the hardware, simplifying the deployment.
- Release the real-time pedestrian analysis tool [PP-Human](deploy/pphuman). It has four major functions: pedestrian tracking, visitor flow statistics, human attribute recognition and falling detection. For falling detection, it is optimized based on real-life data with accurate recognition of various types of falling posture. It can adapt to different environmental background, light and camera angle.
- Add [YOLOX](configs/yolox) object detection model with nano/tiny/S/M/L/X. X version has the accuracy as 51.8% on COCO Val2017 dataset.
- [More releases](https://github.com/PaddlePaddle/PaddleDetection/releases)
## <img title="" src="https://user-images.githubusercontent.com/48054808/157795569-9fc77c85-732f-4870-9be0-99a7fe2cff27.png" alt="" width="20"> Brief Introduction
**PaddleDetection** is an end-to-end object detection development kit based on PaddlePaddle. Providing **over 30 model algorithm** and **over 300 pre-trained models**, it covers object detection, instance segmentation, keypoint detection, multi-object tracking. In particular, PaddleDetection offers **high- performance & light-weight** industrial SOTA models on **servers and mobile** devices, champion solution and cutting-edge algorithm. PaddleDetection provides various data augmentation methods, configurable network components, loss functions and other advanced optimization & deployment schemes. In addition to running through the whole process of data processing, model development, training, compression and deployment, PaddlePaddle also provides rich cases and tutorials to accelerate the industrial application of algorithm.
<div align="center">
<img src="https://user-images.githubusercontent.com/22989727/189122825-ee1c1db2-b5f9-42c0-88b4-7975e1ec239d.gif" width="800"/>
</div>
## <img src="https://user-images.githubusercontent.com/48054808/157799599-e6a66855-bac6-4e75-b9c0-96e13cb9612f.png" width="20"/> Features
- **Rich model library**: PaddleDetection provides over 250 pre-trained models including **object detection, instance segmentation, face recognition, multi-object tracking**. It covers a variety of **global competition champion** schemes.
- **Simple to use**: Modular design, decoupling each network component, easy for developers to build and try various detection models and optimization strategies, quick access to high-performance, customized algorithm.
- **Getting Through End to End**: PaddlePaddle gets through end to end from data augmentation, constructing models, training, compression, depolyment. It also supports multi-architecture, multi-device deployment for **cloud and edge** device.
- **High Performance**: Due to the high performance core, PaddlePaddle has clear advantages in training speed and memory occupation. It also supports FP16 training and multi-machine training.
<div align="center">
<img src="https://user-images.githubusercontent.com/22989727/202131382-45fd2de6-3805-460e-a70c-66db7188d37c.png" width="800"/>
</div>
## <img title="" src="https://user-images.githubusercontent.com/48054808/157800467-2a9946ad-30d1-49a9-b9db-ba33413d9c90.png" alt="" width="20"> Exchanges
- 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 WeChat (scan the QR code, add and reply "D" to the assistant)
<div align="center">
<img src="https://user-images.githubusercontent.com/34162360/177678712-4655747d-4290-4ad9-b7a1-4564a5418ac6.jpg" width = "200" />
</div>
## <img src="https://user-images.githubusercontent.com/48054808/157827140-03ffaff7-7d14-48b4-9440-c38986ea378c.png" width="20"/> Kit Structure
<table align="center">
<tbody>
<tr align="center" valign="bottom">
<td>
<b>Architectures</b>
</td>
<td>
<b>Backbones</b>
</td>
<td>
<b>Components</b>
</td>
<td>
<b>Data Augmentation</b>
</td>
</tr>
<tr valign="top">
<td>
<ul>
<details><summary><b>Object Detection</b></summary>
<ul>
<li>Faster RCNN</li>
<li>FPN</li>
<li>Cascade-RCNN</li>
<li>PSS-Det</li>
<li>RetinaNet</li>
<li>YOLOv3</li>
<li>YOLOF</li>
<li>YOLOX</li>
<li>YOLOv5</li>
<li>YOLOv6</li>
<li>YOLOv7</li>
<li>YOLOv8</li>
<li>RTMDet</li>
<li>PP-YOLO</li>
<li>PP-YOLO-Tiny</li>
<li>PP-PicoDet</li>
<li>PP-YOLOv2</li>
<li>PP-YOLOE</li>
<li>PP-YOLOE+</li>
<li>PP-YOLOE-SOD</li>
<li>PP-YOLOE-R</li>
<li>SSD</li>
<li>CenterNet</li>
<li>FCOS</li>
<li>FCOSR</li>
<li>TTFNet</li>
<li>TOOD</li>
<li>GFL</li>
<li>GFLv2</li>
<li>DETR</li>
<li>Deformable DETR</li>
<li>Swin Transformer</li>
<li>Sparse RCNN</li>
</ul></details>
<details><summary><b>Instance Segmentation</b></summary>
<ul>
<li>Mask RCNN</li>
<li>Cascade Mask RCNN</li>
<li>SOLOv2</li>
</ul></details>
<details><summary><b>Face Detection</b></summary>
<ul>
<li>BlazeFace</li>
</ul></details>
<details><summary><b>Multi-Object-Tracking</b></summary>
<ul>
<li>JDE</li>
<li>FairMOT</li>
<li>DeepSORT</li>
<li>ByteTrack</li>
<li>OC-SORT</li>
<li>BoT-SORT</li>
<li>CenterTrack</li>
</ul></details>
<details><summary><b>KeyPoint-Detection</b></summary>
<ul>
<li>HRNet</li>
<li>HigherHRNet</li>
<li>Lite-HRNet</li>
<li>PP-TinyPose</li>
</ul></details>
</ul>
</td>
<td>
<details><summary><b>Details</b></summary>
<ul>
<li>ResNet(&vd)</li>
<li>Res2Net(&vd)</li>
<li>CSPResNet</li>
<li>SENet</li>
<li>Res2Net</li>
<li>HRNet</li>
<li>Lite-HRNet</li>
<li>DarkNet</li>
<li>CSPDarkNet</li>
<li>MobileNetv1/v3</li>
<li>ShuffleNet</li>
<li>GhostNet</li>
<li>BlazeNet</li>
<li>DLA</li>
<li>HardNet</li>
<li>LCNet</li>
<li>ESNet</li>
<li>Swin-Transformer</li>
<li>ConvNeXt</li>
<li>Vision Transformer</li>
</ul></details>
</td>
<td>
<details><summary><b>Common</b></summary>
<ul>
<li>Sync-BN</li>
<li>Group Norm</li>
<li>DCNv2</li>
<li>EMA</li>
</ul> </details>
</ul>
<details><summary><b>KeyPoint</b></summary>
<ul>
<li>DarkPose</li>
</ul></details>
</ul>
<details><summary><b>FPN</b></summary>
<ul>
<li>BiFPN</li>
<li>CSP-PAN</li>
<li>Custom-PAN</li>
<li>ES-PAN</li>
<li>HRFPN</li>
</ul> </details>
</ul>
<details><summary><b>Loss</b></summary>
<ul>
<li>Smooth-L1</li>
<li>GIoU/DIoU/CIoU</li>
<li>IoUAware</li>
<li>Focal Loss</li>
<li>CT Focal Loss</li>
<li>VariFocal Loss</li>
</ul> </details>
</ul>
<details><summary><b>Post-processing</b></summary>
<ul>
<li>SoftNMS</li>
<li>MatrixNMS</li>
</ul> </details>
</ul>
<details><summary><b>Speed</b></summary>
<ul>
<li>FP16 training</li>
<li>Multi-machine training </li>
</ul> </details>
</ul>
</td>
<td>
<details><summary><b>Details</b></summary>
<ul>
<li>Resize</li>
<li>Lighting</li>
<li>Flipping</li>
<li>Expand</li>
<li>Crop</li>
<li>Color Distort</li>
<li>Random Erasing</li>
<li>Mixup </li>
<li>AugmentHSV</li>
<li>Mosaic</li>
<li>Cutmix </li>
<li>Grid Mask</li>
<li>Auto Augment</li>
<li>Random Perspective</li>
</ul> </details>
</td>
</tr>
</td>
</tr>
</tbody>
</table>
## <img src="https://user-images.githubusercontent.com/48054808/157801371-9a9a8c65-1690-4123-985a-e0559a7f9494.png" width="20"/> Model Performance
<details>
<summary><b> Performance comparison of Cloud models</b></summary>
The comparison between COCO mAP and FPS on Tesla V100 of representative models of each architectures and backbones.
<div align="center">
<img src="docs/images/fps_map.png" />
</div>
**Clarification：**
- `ViT` stands for `ViT-Cascade-Faster-RCNN`, which has highest mAP on COCO as 55.7%
- `Cascade-Faster-RCNN`stands for `Cascade-Faster-RCNN-ResNet50vd-DCN`, which has been optimized to 20 FPS inference speed when COCO mAP as 47.8% in PaddleDetection models
- `PP-YOLOE` are optimized `PP-YOLO v2`. It reached accuracy as 51.4% on COCO dataset, inference speed as 78.1 FPS on Tesla V100
- `PP-YOLOE+` are optimized `PP-YOLOE`. It reached accuracy as 53.3% on COCO dataset, inference speed as 78.1 FPS on Tesla V100
- The models in the figure are available in the[ model library](#模型库)
</details>
<details>
<summary><b> Performance omparison on mobiles</b></summary>
The comparison between COCO mAP and FPS on Qualcomm Snapdragon 865 processor of models on mobile devices.
<div align="center">
<img src="docs/images/mobile_fps_map.png" width=600/>
</div>
**Clarification：**
- Tests were conducted on Qualcomm Snapdragon 865 (4 \*A77 + 4 \*A55) batch_size=1, 4 thread, and NCNN inference library, test script see [MobileDetBenchmark](https://github.com/JiweiMaster/MobileDetBenchmark)
- [PP-PicoDet](configs/picodet) and [PP-YOLO-Tiny](configs/ppyolo) are self-developed models of PaddleDetection, and other models are not tested yet.
</details>
## <img src="https://user-images.githubusercontent.com/48054808/157829890-a535b8a6-631c-4c87-b861-64d4b32b2d6a.png" width="20"/> Model libraries
<details>
<summary><b> 1. General detection</b></summary>
#### PP-YOLOE series Recommended scenarios: Cloud GPU such as Nvidia V100, T4 and edge devices such as Jetson series
| Model | COCO Accuracy（mAP） | V100 TensorRT FP16 Speed(FPS) | Configuration | Download |
|:---------- |:------------------:|:-----------------------------:|:-------------------------------------------------------:|:----------------------------------------------------------------------------------------:|
| PP-YOLOE+_s | 43.9 | 333.3 | [link](configs/ppyoloe/ppyoloe_plus_crn_s_80e_coco.yml) | [download](https://paddledet.bj.bcebos.com/models/ppyoloe_plus_crn_s_80e_coco.pdparams) |
| PP-YOLOE+_m | 50.0 | 208.3 | [link](configs/ppyoloe/ppyoloe_plus_crn_m_80e_coco.yml) | [download](https://paddledet.bj.bcebos.com/models/ppyoloe_plus_crn_m_80e_coco.pdparams) |
| PP-YOLOE+_l | 53.3 | 149.2 | [link](configs/ppyoloe/ppyoloe_plus_crn_l_80e_coco.yml) | [download](https://paddledet.bj.bcebos.com/models/ppyoloe_plus_crn_l_80e_coco.pdparams) |
| PP-YOLOE+_x | 54.9 | 95.2 | [link](configs/ppyoloe/ppyoloe_plus_crn_x_80e_coco.yml) | [download](https://paddledet.bj.bcebos.com/models/ppyoloe_plus_crn_x_80e_coco.pdparams) |
#### PP-PicoDet series Recommended scenarios: Mobile chips and x86 CPU devices, such as ARM CPU(RK3399, Raspberry Pi) and NPU(BITMAIN)
| Model | COCO Accuracy（mAP） | Snapdragon 865 four-thread speed (ms) | Configuration | Download |
|:---------- |:------------------:|:-------------------------------------:|:-----------------------------------------------------:|:-------------------------------------------------------------------------------------:|
| PicoDet-XS | 23.5 | 7.81 | [Link](configs/picodet/picodet_xs_320_coco_lcnet.yml) | [Download](https://paddledet.bj.bcebos.com/models/picodet_xs_320_coco_lcnet.pdparams) |
| PicoDet-S | 29.1 | 9.56 | [Link](configs/picodet/picodet_s_320_coco_lcnet.yml) | [Download](https://paddledet.bj.bcebos.com/models/picodet_s_320_coco_lcnet.pdparams) |
| PicoDet-M | 34.4 | 17.68 | [Link](configs/picodet/picodet_m_320_coco_lcnet.yml) | [Download](https://paddledet.bj.bcebos.com/models/picodet_m_320_coco_lcnet.pdparams) |
| PicoDet-L | 36.1 | 25.21 | [Link](configs/picodet/picodet_l_320_coco_lcnet.yml) | [Download](https://paddledet.bj.bcebos.com/models/picodet_l_320_coco_lcnet.pdparams) |
#### [Frontier detection algorithm](docs/feature_models/PaddleYOLO_MODEL.md)
| Model | COCO Accuracy（mAP） | V100 TensorRT FP16 speed(FPS) | Configuration | Download |
|:-------- |:------------------:|:-----------------------------:|:--------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------:|
| [YOLOX-l](configs/yolox) | 50.1 | 107.5 | [Link](configs/yolox/yolox_l_300e_coco.yml) | [Download](https://paddledet.bj.bcebos.com/models/yolox_l_300e_coco.pdparams) |
| [YOLOv5-l](https://github.com/PaddlePaddle/PaddleYOLO/tree/develop/configs/yolov5) | 48.6 | 136.0 | [Link](https://github.com/PaddlePaddle/PaddleYOLO/tree/develop/configs/yolov5/yolov5_l_300e_coco.yml) | [Download](https://paddledet.bj.bcebos.com/models/yolov5_l_300e_coco.pdparams) |
| [YOLOv7-l](https://github.com/PaddlePaddle/PaddleYOLO/tree/develop/configs/yolov7) | 51.0 | 135.0 | [链接](https://github.com/PaddlePaddle/PaddleYOLO/tree/develop/configs/yolov7/yolov7_l_300e_coco.yml) | [下载地址](https://paddledet.bj.bcebos.com/models/yolov7_l_300e_coco.pdparams) |
#### Other general purpose models [doc](docs/MODEL_ZOO_en.md)
</details>
<details>
<summary><b> 2. Instance segmentation</b></summary>
| Model | Introduction | Recommended Scenarios | COCO Accuracy(mAP) | Configuration | Download |
|:----------------- |:-------------------------------------------------------- |:--------------------------------------------- |:--------------------------------:|:-----------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------:|
| Mask RCNN | Two-stage instance segmentation algorithm | <div style="width: 50pt">Edge-Cloud end</div> | box AP: 41.4 <br/> mask AP: 37.5 | [Link](configs/mask_rcnn/mask_rcnn_r50_vd_fpn_2x_coco.yml) | [Download](https://paddledet.bj.bcebos.com/models/mask_rcnn_r50_vd_fpn_2x_coco.pdparams) |
| Cascade Mask RCNN | Two-stage instance segmentation algorithm | <div style="width: 50pt">Edge-Cloud end</div> | box AP: 45.7 <br/> mask AP: 39.7 | [Link](configs/mask_rcnn/cascade_mask_rcnn_r50_vd_fpn_ssld_2x_coco.yml) | [Download](https://paddledet.bj.bcebos.com/models/cascade_mask_rcnn_r50_vd_fpn_ssld_2x_coco.pdparams) |
| SOLOv2 | Lightweight single-stage instance segmentation algorithm | <div style="width: 50pt">Edge-Cloud end</div> | mask AP: 38.0 | [Link](configs/solov2/solov2_r50_fpn_3x_coco.yml) | [Download](https://paddledet.bj.bcebos.com/models/solov2_r50_fpn_3x_coco.pdparams) |
</details>
<details>
<summary><b> 3. Keypoint detection</b></summary>
| Model | Introduction | Recommended scenarios | COCO Accuracy（AP） | Speed | Configuration | Download |
|:-------------------- |:--------------------------------------------------------------------------------------------- |:--------------------------------------------- |:-----------------:|:---------------------------------:|:---------------------------------------------------------:|:-------------------------------------------------------------------------------------------:|
| HRNet-w32 + DarkPose | <div style="width: 130pt">Top-down Keypoint detection algorithm<br/>Input size: 384x288</div> | <div style="width: 50pt">Edge-Cloud end</div> | 78.3 | T4 TensorRT FP16 2.96ms | [Link](configs/keypoint/hrnet/dark_hrnet_w32_384x288.yml) | [Download](https://paddledet.bj.bcebos.com/models/keypoint/dark_hrnet_w32_384x288.pdparams) |
| HRNet-w32 + DarkPose | Top-down Keypoint detection algorithm<br/>Input size: 256x192 | Edge-Cloud end | 78.0 | T4 TensorRT FP16 1.75ms | [Link](configs/keypoint/hrnet/dark_hrnet_w32_256x192.yml) | [Download](https://paddledet.bj.bcebos.com/models/keypoint/dark_hrnet_w32_256x192.pdparams) |
| PP-TinyPose | Light-weight keypoint algorithm<br/>Input size: 256x192 | Mobile | 68.8 | Snapdragon 865 four-thread 6.30ms | [Link](configs/keypoint/tiny_pose/tinypose_256x192.yml) | [Download](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_256x192.pdparams) |
| PP-TinyPose | Light-weight keypoint algorithm<br/>Input size: 128x96 | Mobile | 58.1 | Snapdragon 865 four-thread 2.37ms | [Link](configs/keypoint/tiny_pose/tinypose_128x96.yml) | [Download](https://bj.bcebos.com/v1/paddledet/models/keypoint/tinypose_128x96.pdparams) |
#### Other keypoint detection models [doc](configs/keypoint)
</details>
<details>
<summary><b> 4. Multi-object tracking PP-Tracking</b></summary>
| Model | Introduction | Recommended scenarios | Accuracy | Configuration | Download |
|:--------- |:------------------------------------------------------------- |:--------------------- |:----------------------:|:-----------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------:|
| 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) |
| 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) | - |
#### Other multi-object tracking models [docs](configs/mot)
</details>
<details>
<summary><b> 5. Industrial real-time pedestrain analysis tool-PP Human</b></summary>
| 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>
## <img src="https://user-images.githubusercontent.com/48054808/157828296-d5eb0ccb-23ea-40f5-9957-29853d7d13a9.png" width="20"/>Document tutorials
### Introductory tutorials
- [Installation](docs/tutorials/INSTALL_cn.md)
- [Quick start](docs/tutorials/QUICK_STARTED_cn.md)
- [Data preparation](docs/tutorials/data/README.md)
- [Geting Started on PaddleDetection](docs/tutorials/GETTING_STARTED_cn.md)
- [FAQ](docs/tutorials/FAQ)
### Advanced tutorials
- Configuration
- [RCNN Configuration](docs/tutorials/config_annotation/faster_rcnn_r50_fpn_1x_coco_annotation.md)
- [PP-YOLO Configuration](docs/tutorials/config_annotation/ppyolo_r50vd_dcn_1x_coco_annotation.md)
- Compression based on [PaddleSlim](https://github.com/PaddlePaddle/PaddleSlim)
- [Pruning/Quantization/Distillation Tutorial](configs/slim)
- [Inference deployment](deploy/README.md)
- [Export model for inference](deploy/EXPORT_MODEL.md)
- [Paddle Inference deployment](deploy/README.md)
- [Inference deployment with Python](deploy/python)
- [Inference deployment with C++](deploy/cpp)
- [Paddle-Lite deployment](deploy/lite)
- [Paddle Serving deployment](deploy/serving)
- [ONNX model export](deploy/EXPORT_ONNX_MODEL.md)
- [Inference benchmark](deploy/BENCHMARK_INFER.md)
- Advanced development
- [Data processing module](docs/advanced_tutorials/READER.md)
- [New object detection models](docs/advanced_tutorials/MODEL_TECHNICAL.md)
- Custumization
- [Object detection](docs/advanced_tutorials/customization/detection.md)
- [Keypoint detection](docs/advanced_tutorials/customization/keypoint_detection.md)
- [Multiple object tracking](docs/advanced_tutorials/customization/pphuman_mot.md)
- [Action recognition](docs/advanced_tutorials/customization/action_recognotion/)
- [Attribute recognition](docs/advanced_tutorials/customization/pphuman_attribute.md)
### Courses
- **[Theoretical foundation] [Object detection 7-day camp](https://aistudio.baidu.com/aistudio/education/group/info/1617):** Overview of object detection tasks, details of RCNN series object detection algorithm and YOLO series object detection algorithm, PP-YOLO optimization strategy and case sharing, introduction and practice of AnchorFree series algorithm
- **[Industrial application] [AI Fast Track industrial object detection technology and application](https://aistudio.baidu.com/aistudio/education/group/info/23670):** Super object detection algorithms, real-time pedestrian analysis system PP-Human, breakdown and practice of object detection industrial application
- **[Industrial features] 2022.3.26** **[Smart City Industry Seven-Day Class](https://aistudio.baidu.com/aistudio/education/group/info/25620)** : Urban planning, Urban governance, Smart governance service, Traffic management, community governance.
- **[Academic exchange] 2022.9.27 [YOLO Vision Event](https://www.youtube.com/playlist?list=PL1FZnkj4ad1NHVC7CMc3pjSQ-JRK-Ev6O):** As the first YOLO-themed event, PaddleDetection was invited to communicate with the experts in the field of Computer Vision around the world.
### [Industrial tutorial examples](./industrial_tutorial/README.md)
- [Rotated object detection based on PP-YOLOE-R](https://aistudio.baidu.com/aistudio/projectdetail/5058293)
- [Aerial image detection based on PP-YOLOE-SOD](https://aistudio.baidu.com/aistudio/projectdetail/5036782)
- [Fall down recognition based on PP-Human v2](https://aistudio.baidu.com/aistudio/projectdetail/4606001)
- [Intelligent fitness recognition based on PP-TinyPose Plus](https://aistudio.baidu.com/aistudio/projectdetail/4385813)
- [Road litter detection based on PP-PicoDet Plus](https://aistudio.baidu.com/aistudio/projectdetail/3561097)
- [Visitor flow statistics based on FairMOT](https://aistudio.baidu.com/aistudio/projectdetail/2421822)
- [Guest analysis based on PP-Human](https://aistudio.baidu.com/aistudio/projectdetail/4537344)
- [More examples](./industrial_tutorial/README.md)
## <img title="" src="https://user-images.githubusercontent.com/48054808/157836473-1cf451fa-f01f-4148-ba68-b6d06d5da2f9.png" alt="" width="20"> Applications
- [Fitness app on android mobile](https://github.com/zhiboniu/pose_demo_android)
- [PP-Tracking GUI Visualization Interface](https://github.com/yangyudong2020/PP-Tracking_GUi)
## Recommended third-party tutorials
- [Deployment of PaddleDetection for Windows I ](https://zhuanlan.zhihu.com/p/268657833)
- [Deployment of PaddleDetection for Windows II](https://zhuanlan.zhihu.com/p/280206376)
- [Deployment of PaddleDetection on Jestson Nano](https://zhuanlan.zhihu.com/p/319371293)
- [How to deploy YOLOv3 model on Raspberry Pi for Helmet detection](https://github.com/PaddleCV-FAQ/PaddleDetection-FAQ/blob/main/Lite%E9%83%A8%E7%BD%B2/yolov3_for_raspi.md)
- [Use SSD-MobileNetv1 for a project -- From dataset to deployment on Raspberry Pi](https://github.com/PaddleCV-FAQ/PaddleDetection-FAQ/blob/main/Lite%E9%83%A8%E7%BD%B2/ssd_mobilenet_v1_for_raspi.md)
## <img src="https://user-images.githubusercontent.com/48054808/157835981-ef6057b4-6347-4768-8fcc-cd07fcc3d8b0.png" width="20"/> Version updates
Please refer to the[ Release note ](https://github.com/PaddlePaddle/Paddle/wiki/PaddlePaddle-2.3.0-Release-Note-EN)for more details about the updates
## <img title="" src="https://user-images.githubusercontent.com/48054808/157835345-f5d24128-abaf-4813-b793-d2e5bdc70e5a.png" alt="" width="20"> License
PaddlePaddle is provided under the [Apache 2.0 license](LICENSE)
## <img src="https://user-images.githubusercontent.com/48054808/157835796-08d4ffbc-87d9-4622-89d8-cf11a44260fc.png" width="20"/> Contribute your code
We appreciate your contributions and your feedback！
- Thank [Mandroide](https://github.com/Mandroide) for code cleanup and
- Thank [FL77N](https://github.com/FL77N/) for `Sparse-RCNN`model
- Thank [Chen-Song](https://github.com/Chen-Song) for `Swin Faster-RCNN`model
- Thank [yangyudong](https://github.com/yangyudong2020), [hchhtc123](https://github.com/hchhtc123) for developing PP-Tracking GUI interface
- Thank Shigure19 for developing PP-TinyPose fitness APP
- Thank [manangoel99](https://github.com/manangoel99) for Wandb visualization methods
## <img src="https://user-images.githubusercontent.com/48054808/157835276-9aab9d1c-1c46-446b-bdd4-5ab75c5cfa48.png" width="20"/> Quote
```
@misc{ppdet2019,
title={PaddleDetection, Object detection and instance segmentation toolkit based on PaddlePaddle.},
author={PaddlePaddle Authors},
howpublished = {\url{https://github.com/PaddlePaddle/PaddleDetection}},
year={2019}
}
```
|