add yoloseries doc (#6869)

* add yoloseries doc, test=document_fix * add modelzoo doc, test=document_fix * update yoloseries doc, test=document_fix

add yoloseries doc (#6869)
* add yoloseries doc, test=document_fix * add modelzoo doc, test=document_fix * update yoloseries doc, test=document_fix
30be0d43 · Feng Ni · GitHub · e32afd26 · 30be0d43 · 30be0d43
4 changed file
--- a/README_cn.md
+++ b/README_cn.md
@@ -33,7 +33,7 @@
    - 发布行人分析工具[PP-Human v2](./deploy/pipeline)，新增打架、打电话、抽烟、闯入四大行为识别，底层算法性能升级，覆盖行人检测、跟踪、属性三类核心算法能力，提供保姆级全流程开发及模型优化策略，支持在线视频流输入
    - 首次发布[PP-Vehicle](./deploy/pipeline)，提供车牌识别、车辆属性分析（颜色、车型）、车流量统计以及违章检测四大功能，兼容图片、在线视频流、视频输入，提供完善的二次开发文档教程
  - 💡 前沿算法：
-    - 全面覆盖的[YOLO家族](https://github.com/nemonameless/PaddleDetection_YOLOSeries)经典与最新模型: 包括YOLOv3，百度飞桨自研的实时高精度目标检测检测模型PP-YOLOE，以及前沿检测算法YOLOv4、YOLOv5、YOLOX，MT-YOLOv6及YOLOv7
+    - 全面覆盖的[YOLO家族](docs/feature_models/YOLOSERIES_MODEL.md)经典与最新模型代码库[PaddleDetection_YOLOSeries](https://github.com/nemonameless/PaddleDetection_YOLOSeries): 包括YOLOv3，百度飞桨自研的实时高精度目标检测模型PP-YOLOE，以及前沿检测算法YOLOv4、YOLOv5、YOLOX，MT-YOLOv6及YOLOv7
    - 新增基于[ViT](configs/vitdet)骨干网络高精度检测模型，COCO数据集精度达到55.7% mAP；新增[OC-SORT](configs/mot/ocsort)多目标跟踪模型；新增[ConvNeXt](configs/convnext)骨干网络
  - 📋 产业范例：新增[智能健身](https://aistudio.baidu.com/aistudio/projectdetail/4385813)、[打架识别](https://aistudio.baidu.com/aistudio/projectdetail/4086987?channelType=0&channel=0)、[来客分析](https://aistudio.baidu.com/aistudio/projectdetail/4230123?channelType=0&channel=0)、车辆结构化范例

@@ -266,9 +266,9 @@

 - `ViT`为`ViT-Cascade-Faster-RCNN`模型，COCO数据集mAP高达55.7%
 - `Cascade-Faster-RCNN`为`Cascade-Faster-RCNN-ResNet50vd-DCN`，PaddleDetection将其优化到COCO数据mAP为47.8%时推理速度为20FPS
- `PP-YOLOE`是对`PP-YOLO v2`模型的进一步优化，在COCO数据集精度51.6%，Tesla V100预测速度78.1FPS
- `PP-YOLOE+`是对`PPOLOE`模型的进一步优化，在COCO数据集精度53.3%，Tesla V100预测速度78.1FPS
- [`YOLOX`](configs/yolox)和[`YOLOv5`](https://github.com/nemonameless/PaddleDetection_YOLOSeries/tree/develop/configs/yolov5)均为基于PaddleDetection复现算法
+- `PP-YOLOE`是对`PP-YOLO v2`模型的进一步优化，L版本在COCO数据集mAP为51.6%，Tesla V100预测速度78.1FPS
+- `PP-YOLOE+`是对`PPOLOE`模型的进一步优化，L版本在COCO数据集mAP为53.3%，Tesla V100预测速度78.1FPS
+- [`YOLOX`](configs/yolox)和[`YOLOv5`](https://github.com/nemonameless/PaddleDetection_YOLOSeries/tree/develop/configs/yolov5)均为基于PaddleDetection复现算法，`YOLOv5`代码在[`PaddleDetection_YOLOSeries`](https://github.com/nemonameless/PaddleDetection_YOLOSeries)中，参照[YOLOSERIES_MODEL](docs/feature_models/YOLOSERIES_MODEL.md)
 - 图中模型均可在[模型库](#模型库)中获取

 </details>
@@ -320,6 +320,9 @@
 | [YOLOv5-l](https://github.com/nemonameless/PaddleDetection_YOLOSeries/tree/develop/configs/yolov5) | 48.6        | 136.0                     | [链接](https://github.com/nemonameless/PaddleDetection_YOLOSeries/blob/develop/configs/yolov5/yolov5_l_300e_coco.yml) | [下载地址](https://paddledet.bj.bcebos.com/models/yolov5_l_300e_coco.pdparams) |
 | [YOLOv7-l](https://github.com/nemonameless/PaddleDetection_YOLOSeries/tree/develop/configs/yolov7) | 51.0        | 135.0                     | [链接](https://github.com/nemonameless/PaddleDetection_YOLOSeries/blob/develop/configs/yolov7/yolov7_l_300e_coco.yml) | [下载地址](https://paddledet.bj.bcebos.com/models/yolov7_l_300e_coco.pdparams) |

+**注意:**
+- `YOLOv5`和`YOLOv7`代码在[`PaddleDetection_YOLOSeries`](https://github.com/nemonameless/PaddleDetection_YOLOSeries)中，为基于`PaddleDetection`复现的算法，可参照[YOLOSERIES_MODEL](docs/feature_models/YOLOSERIES_MODEL.md)。
+
 #### 其他通用检测模型 [文档链接](docs/MODEL_ZOO_cn.md)

 </details>
@@ -354,9 +357,9 @@

 | 模型名称      | 模型简介                     | 推荐场景                               | 精度                     | 配置文件                                                                  | 模型下载                                                                                              |
 |:--------- |:------------------------ |:---------------------------------- |:----------------------:|:---------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------:|
-| 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) |
+| ByteTrack | SDE多目标跟踪算法 仅包含检测模型       | 云边端                                | MOT-17 test:  78.4 | [链接](configs/mot/bytetrack/bytetrack_yolox.yml) | [下载地址](https://bj.bcebos.com/v1/paddledet/models/mot/yolox_x_24e_800x1440_mix_det.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) | - |
+| 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) | [下载地址](https://bj.bcebos.com/v1/paddledet/models/mot/yolox_x_24e_800x1440_mix_mot_ch.pdparams) |

 #### 其他多目标跟踪模型 [文档链接](configs/mot)


--- a/docs/MODEL_ZOO_cn.md
+++ b/docs/MODEL_ZOO_cn.md
@@ -87,7 +87,7 @@ Paddle提供基于ImageNet的骨架网络预训练模型。所有预训练模型

 请参考[PicoDet](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/picodet)

-### PP-YOLOE
+### PP-YOLOE/PP-YOLOE+

 请参考[PP-YOLOE](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ppyoloe)

@@ -95,6 +95,18 @@ Paddle提供基于ImageNet的骨架网络预训练模型。所有预训练模型

 请参考[YOLOX](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/yolox)

+### YOLOv5
+
+请参考[YOLOv5](https://github.com/nemonameless/PaddleDetection_YOLOSeries/tree/develop/configs/yolov5)
+
+### MT-YOLOv6
+
+请参考[MT-YOLOv6](https://github.com/nemonameless/PaddleDetection_YOLOSeries/tree/develop/configs/yolov6mt)
+
+### YOLOv7
+
+请参考[YOLOv7](https://github.com/nemonameless/PaddleDetection_YOLOSeries/tree/develop/configs/yolov7)
+

 ## 旋转框检测

@@ -135,3 +147,7 @@ Paddle提供基于ImageNet的骨架网络预训练模型。所有预训练模型
 ### ByteTrack

 请参考[ByteTrack](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/mot/bytetrack)
+
+### OC-SORT
+
+请参考[OC-SORT](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/mot/ocsort)
--- a/docs/MODEL_ZOO_en.md
+++ b/docs/MODEL_ZOO_en.md
@@ -86,7 +86,7 @@ Please refer to[GFL](https://github.com/PaddlePaddle/PaddleDetection/tree/develo

 Please refer to[PicoDet](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/picodet)

-### PP-YOLOE
+### PP-YOLOE/PP-YOLOE+

 Please refer to[PP-YOLOE](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ppyoloe)

@@ -94,6 +94,18 @@ Please refer to[PP-YOLOE](https://github.com/PaddlePaddle/PaddleDetection/tree/d

 Please refer to[YOLOX](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/yolox)

+### YOLOv5
+
+Please refer to[YOLOv5](https://github.com/nemonameless/PaddleDetection_YOLOSeries/tree/develop/configs/yolov5)
+
+### MT-YOLOv6
+
+Please refer to[MT-YOLOv6](https://github.com/nemonameless/PaddleDetection_YOLOSeries/tree/develop/configs/yolov6mt)
+
+### YOLOv7
+
+Please refer to[YOLOv7](https://github.com/nemonameless/PaddleDetection_YOLOSeries/tree/develop/configs/yolov7)
+

 ## Rotating frame detection

@@ -134,3 +146,7 @@ Please refer to [FairMOT](https://github.com/PaddlePaddle/PaddleDetection/tree/d
 ### ByteTrack

 Please refer to [ByteTrack](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/mot/bytetrack)
+
+### OC-SORT
+
+Please refer to [OC-SORT](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/mot/ocsort)
--- a/docs/feature_models/YOLOSERIES_MODEL.md
+++ b/docs/feature_models/YOLOSERIES_MODEL.md