[cherry-pick] update yoloseries doc (#7006)

f8e57aeb · Feng Ni · GitHub · de21c278 · f8e57aeb · f8e57aeb
7 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](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
+    - 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，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

@@ -124,7 +124,7 @@
            <li>RetinaNet</li>
            <li>YOLOv3</li>  
            <li>YOLOv5</li>  
-            <li>MT-YOLOv6</li>  
+            <li>YOLOv6</li>  
            <li>YOLOv7</li>  
            <li>PP-YOLOv1/v2</li>
            <li>PP-YOLO-Tiny</li>

--- a/README_en.md
+++ b/README_en.md
@@ -38,7 +38,7 @@

  - 💡 Cutting-edge algorithms：

-    - Covers [YOLO family](https://github.com/nemonameless/PaddleDetection_YOLOSeries) classic and latest models: 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, MT-YOLOv6, and YOLOv7
+    - Covers [YOLO family](https://github.com/nemonameless/PaddleDetection_YOLOSeries) classic and latest models: 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, and YOLOv7
    - 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).

--- a/docs/CHANGELOG.md
+++ b/docs/CHANGELOG.md
@@ -35,7 +35,7 @@

 - 前沿算法
  - YOLO家族全系列模型
-    - 发布YOLO家族全系列模型，覆盖前沿检测算法YOLOv5、MT-YOLOv6及YOLOv7
+    - 发布YOLO家族全系列模型，覆盖前沿检测算法YOLOv5、YOLOv6及YOLOv7
    - 基于ConvNext骨干网络，YOLO各算法训练周期缩5-8倍，精度普遍提升1%-5% mAP；使用模型压缩策略实现精度无损的同时速度提升30%以上
  - 新增基于ViT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多目标跟踪模型

--- a/docs/CHANGELOG_en.md
+++ b/docs/CHANGELOG_en.md
@@ -38,7 +38,7 @@ English | [简体中文](./CHANGELOG.md)
 - Cutting-edge algorithms

  - YOLO Family
-    - Release the full range of YOLO family models covering the cutting-edge detection algorithms YOLOv5, MT-YOLOv6 and YOLOv7
+    - Release the full range of YOLO family models covering the cutting-edge detection algorithms YOLOv5, YOLOv6 and YOLOv7
    - Based on the ConvNext backbone network, YOLO's algorithm training periods are reduced by 5-8 times with accuracy generally improving by 1%-5% mAP; Thanks to the model compression strategy, its speed increased by over 30% with no loss of precision.
  - Newly add high precision detection model based on [ViT](configs/vitdet) backbone network, with a 55.7% mAP accuracy on the COCO dataset
  - Newly add multi-object tracking model [OC-SORT](configs/mot/ocsort)

--- a/docs/MODEL_ZOO_cn.md
+++ b/docs/MODEL_ZOO_cn.md
@@ -93,7 +93,19 @@ Paddle提供基于ImageNet的骨架网络预训练模型。所有预训练模型

 ### YOLOX

-请参考[YOLOX](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.5/configs/yolox)
+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)
+
+### YOLOv6
+
+请参考[YOLOv6](https://github.com/nemonameless/PaddleDetection_YOLOSeries/tree/develop/configs/yolov6)
+
+### YOLOv7
+
+请参考[YOLOv7](https://github.com/nemonameless/PaddleDetection_YOLOSeries/tree/develop/configs/yolov7)


 ## 旋转框检测

--- a/docs/MODEL_ZOO_en.md
+++ b/docs/MODEL_ZOO_en.md
@@ -92,7 +92,19 @@ Please refer to[PP-YOLOE](https://github.com/PaddlePaddle/PaddleDetection/tree/r

 ### YOLOX

-Please refer to[YOLOX](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.5/configs/yolox)
+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)
+
+### YOLOv6
+
+Please refer to[YOLOv6](https://github.com/nemonameless/PaddleDetection_YOLOSeries/tree/develop/configs/yolov6)
+
+### YOLOv7
+
+Please refer to[YOLOv7](https://github.com/nemonameless/PaddleDetection_YOLOSeries/tree/develop/configs/yolov7)


 ## Rotating frame detection

--- a/docs/feature_models/YOLOSERIES_MODEL.md
+++ b/docs/feature_models/YOLOSERIES_MODEL.md
 简体中文 | [English](YOLOSERIES_MODEL_en.md)

-# YOLOSeries
+# [**YOLOSeries**](https://github.com/nemonameless/PaddleDetection_YOLOSeries)

 ## 内容
 - [简介](#简介)
@@ -8,7 +8,7 @@
    - [PP-YOLOE](#PP-YOLOE)
    - [YOLOX](#YOLOX)
    - [YOLOv5](#YOLOv5)
-    - [MT-YOLOv6](#MT-YOLOv6)
+    - [YOLOv6](#YOLOv6)
    - [YOLOv7](#YOLOv7)
 - [使用指南](#使用指南)
    - [一键运行全流程](#一键运行全流程)
@@ -16,20 +16,41 @@

 ## 简介

-[**YOLOSeries**](https://github.com/nemonameless/PaddleDetection_YOLOSeries)是基于[PaddleDetection](https://github.com/PaddlePaddle/PaddleDetection)的YOLO系列模型库，**由PaddleDetection团队成员建设和维护**，支持`YOLOv3`,`PP-YOLOE`,`PP-YOLOE+`,`YOLOX`,`YOLOv5`,`MT-YOLOv6`,`YOLOv7`等模型，其upstream为PaddleDetection的[develop](https://github.com/PaddlePaddle/PaddleDetection/tree/develop)分支，并与PaddleDetection主代码库分支保持同步更新，包括github和gitee的代码，欢迎一起使用和建设！
+[**YOLOSeries**](https://github.com/nemonameless/PaddleDetection_YOLOSeries)是基于[PaddleDetection](https://github.com/PaddlePaddle/PaddleDetection)的YOLO系列模型库，**由PaddleDetection团队成员建设和维护**，支持`YOLOv3`,`PP-YOLOE`,`PP-YOLOE+`,`YOLOX`,`YOLOv5`,`YOLOv6`,`YOLOv7`等模型，其upstream为PaddleDetection的[develop](https://github.com/PaddlePaddle/PaddleDetection/tree/develop)分支，并与PaddleDetection主代码库分支保持同步更新，包括github和gitee的代码，欢迎一起使用和建设！
+
+## Updates!
+* 【2022/09/21】精简代码库只保留主要的YOLO模型相关的代码(release/2.5 branch)；
+* 【2022/09/19】支持[`YOLOv6`](configs/yolov6)新版，包括n/t/s/m/l模型；
+* 【2022/08/23】发布`PaddleDetection_YOLOSeries`代码库: 支持`YOLOv3`,`PP-YOLOE`,`PP-YOLOE+`,`YOLOX`,`YOLOv5`,`MT-YOLOv6`,`YOLOv7`等YOLO模型，支持ConvNeXt骨干网络高精度版`PP-YOLOE`,`YOLOX`和`YOLOv5`等模型，支持PaddleSlim无损加速量化训练`PP-YOLOE`,`YOLOv5`,`MT-YOLOv6`和`YOLOv7`等模型，详情可阅读[此文章](https://mp.weixin.qq.com/s/Hki01Zs2lQgvLSLWS0btrA)；
+

 **注意:**
+ - 此代码库**推荐使用paddlepaddle-2.3.0以上的版本**，请参考[官网](https://www.paddlepaddle.org.cn/install/quick?docurl=/documentation/docs/zh/install/pip/linux-pip.html)下载对应适合版本，**其中develop分支代码请安装paddle develop版本，其余分支建议安装paddle 2.3.2版本**。
 - github链接为：https://github.com/nemonameless/PaddleDetection_YOLOSeries
 - gitee链接为：https://gitee.com/nemonameless/PaddleDetection_YOLOSeries
 - 提issue可以在此代码库的[issues](https://github.com/nemonameless/PaddleDetection_YOLOSeries/issues)页面中，也可以在[PaddleDetection issues](https://github.com/PaddlePaddle/PaddleDetection/issues)中，也欢迎提[PR](https://github.com/nemonameless/PaddleDetection_YOLOSeries/pulls)共同建设和维护。
- - [PP-YOLOE](../../configs/ppyoloe),[PP-YOLOE+](../../configs/ppyoloe),[PP-YOLO](../../configs/ppyolo),[PP-YOLOv2](../../configs/ppyolo),[YOLOv3](../../configs/yolov3)和[YOLOX](../../configs/yolox)等模型推荐在[PaddleDetection](https://github.com/PaddlePaddle/PaddleDetection)中使用，**会最先发布PP-YOLO系列特色检测模型的最新进展**。
- - [YOLOv5](https://github.com/nemonameless/PaddleDetection_YOLOSeries/tree/develop/configs/yolov5),[YOLOv7](https://github.com/nemonameless/PaddleDetection_YOLOSeries/tree/develop/configs/yolov7)和[MT-YOLOv6](https://github.com/nemonameless/PaddleDetection_YOLOSeries/tree/develop/configs/yolov6mt)模型推荐在此代码库中使用，**由于GPL开源协议而不合入[PaddleDetection](https://github.com/PaddlePaddle/PaddleDetection)主代码库**。
- - `YOLOSeries`代码库**推荐使用paddlepaddle-2.3.0及以上的版本**，请参考[官网](https://www.paddlepaddle.org.cn/install/quick?docurl=/documentation/docs/zh/install/pip/linux-pip.html)下载对应适合版本。
+ - [PP-YOLOE](configs/ppyoloe),[PP-YOLOE+](configs/ppyoloe),[PP-YOLO](configs/ppyolo),[PP-YOLOv2](configs/ppyolo),[YOLOv3](configs/yolov3)和[YOLOX](configs/yolox)等模型推荐在[PaddleDetection](https://github.com/PaddlePaddle/PaddleDetection)中使用，**会最先发布PP-YOLO系列特色检测模型的最新进展**。
+ - [YOLOv5](configs/yolov5),[YOLOv7](configs/yolov7)和[YOLOv6](configs/yolov6)模型推荐在此代码库中使用，**由于GPL开源协议而不合入[PaddleDetection](https://github.com/PaddlePaddle/PaddleDetection)主代码库**。
+
+
+## <img title="" src="https://user-images.githubusercontent.com/48054808/157800467-2a9946ad-30d1-49a9-b9db-ba33413d9c90.png" alt="" width="20"> 技术交流
+
+- 如果你发现任何PaddleDetection存在的问题或者是建议, 欢迎通过[GitHub Issues](https://github.com/PaddlePaddle/PaddleDetection/issues)给我们提issues。
+
+- **欢迎加入PaddleDetection 微信用户群（扫码填写问卷即可入群）**
+  - **入群福利 💎：获取PaddleDetection团队整理的重磅学习大礼包🎁**
+    - 📊 福利一：获取飞桨联合业界企业整理的开源数据集
+    - 👨‍🏫 福利二：获取PaddleDetection历次发版直播视频与最新直播咨询
+    - 🗳 福利三：获取垂类场景预训练模型集合，包括工业、安防、交通等5+行业场景
+    - 🗂 福利四：获取10+全流程产业实操范例，覆盖火灾烟雾检测、人流量计数等产业高频场景
+  <div align="center">
+  <img src="https://user-images.githubusercontent.com/34162360/177678712-4655747d-4290-4ad9-b7a1-4564a5418ac6.jpg"  width = "200" />  
+  </div>


 ## 模型库

-### [PP-YOLOE](../../configs/ppyoloe)
+### [PP-YOLOE, PP-YOLOE+](configs/ppyoloe)

 | 网络模型        | 输入尺寸   | 图片数/GPU | 学习率策略 | 推理耗时(ms) | mAP<sup>val<br>0.5:0.95 | mAP<sup>val<br>0.5 | Params(M) | FLOPs(G) |    下载链接       | 配置文件 |
 | :------------- | :------- | :-------: | :------: | :------------: | :---------------------: | :----------------: |:---------: | :------: |:---------------: |:-----: |
@@ -106,23 +127,32 @@
 | YOLOv5-l |  640   | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov5/yolov5_l_300e_coco_w_nms.zip) &#124; [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov5/yolov5_l_300e_coco_wo_nms.zip) | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov5/yolov5_l_300e_coco_w_nms.onnx) &#124; [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov5/yolov5_l_300e_coco_wo_nms.onnx) |
 | YOLOv5-x |  640   | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov5/yolov5_x_300e_coco_w_nms.zip) &#124; [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov5/yolov5_x_300e_coco_wo_nms.zip) | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov5/yolov5_x_300e_coco_w_nms.onnx) &#124; [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov5/yolov5_x_300e_coco_wo_nms.onnx) |

-### [MT-YOLOv6](https://github.com/nemonameless/PaddleDetection_YOLOSeries/tree/develop/configs/yolov6mt)

-| 网络模型        | 输入尺寸   | 图片数/GPU | 学习率策略 | 推理耗时(ms) | mAP<sup>val<br>0.5:0.95 | mAP<sup>val<br>0.5 | Params(M) | FLOPs(G) |    下载链接       | 配置文件 |
+### [YOLOv6](configs/yolov6)
+
+| 网络网络        | 输入尺寸   | 图片数/GPU | 学习率策略 | 模型推理耗时(ms) |   mAP  |   AP50  | Params(M) | FLOPs(G) |  下载链接       | 配置文件 |
 | :------------- | :------- | :-------: | :------: | :---------: | :-----: |:-----: | :-----: |:-----: | :-------------: | :-----: |
-| *YOLOv6mt-n       |  416     |    32      |   400e    |     2.5    | 30.5  |    46.8 |  4.74  | 5.16 |[model](https://paddledet.bj.bcebos.com/models/yolov6mt_n_416_400e_coco.pdparams) | [config](https://github.com/nemonameless/PaddleDetection_YOLOSeries/tree/develop/configs/yolov6mt/yolov6mt_n_416_400e_coco.yml) |
-| *YOLOv6mt-n       |  640     |    32      |   400e    |     2.8    |  34.7 |    52.7 |  4.74  |  12.2 |[model](https://paddledet.bj.bcebos.com/models/yolov6mt_n_400e_coco.pdparams) | [config](https://github.com/nemonameless/PaddleDetection_YOLOSeries/tree/develop/configs/yolov6mt/yolov6mt_n_400e_coco.yml) |
-| *YOLOv6mt-t       |  640     |    32      |   400e    |     2.9    |  40.8 |  60.4 |  16.36  | 39.94 |[model](https://paddledet.bj.bcebos.com/models/yolov6mt_t_400e_coco.pdparams) | [config](https://github.com/nemonameless/PaddleDetection_YOLOSeries/tree/develop/configs/yolov6mt/yolov6mt_t_400e_coco.yml) |
-| *YOLOv6mt-s       |  640     |    32      |   400e    |     3.0    | 42.5 |    61.7 |  18.87  | 48.36 |[model](https://paddledet.bj.bcebos.com/models/yolov6mt_s_400e_coco.pdparams) | [config](https://github.com/nemonameless/PaddleDetection_YOLOSeries/tree/develop/configs/yolov6mt/yolov6mt_s_400e_coco.yml) |
+| *YOLOv6-n       |  416     |    32      |   400e    |     1.0    |  31.1 |    45.3 |  4.74  | 5.16 |[model](https://paddledet.bj.bcebos.com/models/yolov6_n_416_400e_coco.pdparams) | [config](https://github.com/nemonameless/PaddleDetection_YOLOSeries/tree/develop/configs/yolov6/yolov6_n_416_400e_coco.yml) |
+| *YOLOv6-n       |  640     |    32      |   400e    |     1.3    |  36.1 |    51.9 |  4.74  | 12.21 |[model](https://paddledet.bj.bcebos.com/models/yolov6_n_400e_coco.pdparams) | [config](https://github.com/nemonameless/PaddleDetection_YOLOSeries/tree/develop/configs/yolov6/yolov6_n_400e_coco.yml) |
+| *YOLOv6-t       |  640     |    32      |   400e    |     2.1    |  40.7 |    57.4 |  10.63  | 27.29 |[model](https://paddledet.bj.bcebos.com/models/yolov6_t_400e_coco.pdparams) | [config](https://github.com/nemonameless/PaddleDetection_YOLOSeries/tree/develop/configs/yolov6/yolov6_t_400e_coco.yml) |
+| *YOLOv6-s       |  640     |    32      |   400e    |     2.6    |  43.4 |    60.5 |  18.87  | 48.35 |[model](https://paddledet.bj.bcebos.com/models/yolov6_s_400e_coco.pdparams) | [config](https://github.com/nemonameless/PaddleDetection_YOLOSeries/tree/develop/configs/yolov6/yolov6_s_400e_coco.yml) |
+| *YOLOv6-m       |  640     |    32      |   300e    |     5.0    |  49.0 |    66.5 |  37.17  | 88.82 |[model](https://paddledet.bj.bcebos.com/models/yolov6_m_300e_coco.pdparams) | [config](https://github.com/nemonameless/PaddleDetection_YOLOSeries/tree/develop/configs/yolov6/yolov6_m_300e_coco.yml) |
+| *YOLOv6-l       |  640     |    32      |   300e    |     7.9    |  51.0 |    68.9 |  63.54  | 155.89 |[model](https://paddledet.bj.bcebos.com/models/yolov6_l_300e_coco.pdparams) | [config](https://github.com/nemonameless/PaddleDetection_YOLOSeries/tree/develop/configs/yolov6/yolov6_l_300e_coco.yml) |
+| *YOLOv6-l-silu  |  640     |    32      |   300e    |     9.6    |  51.7 |    69.6 |  58.59  | 142.66 |[model](https://paddledet.bj.bcebos.com/models/yolov6_l_silu_300e_coco.pdparams) | [config](https://github.com/nemonameless/PaddleDetection_YOLOSeries/tree/develop/configs/yolov6/yolov6_l_silu_300e_coco.yml) |
+

 #### 部署模型

 | 网络模型     | 输入尺寸 | 导出后的权重(w/o NMS) | ONNX(w/o NMS)  |
 | :-------- | :--------: | :---------------------: | :----------------: |
-| YOLOv6mt-n |  416   | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov6mt/yolov6mt_n_416_400e_coco_w_nms.zip) &#124; [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov6mt/yolov6mt_n_416_400e_coco_wo_nms.zip) | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov6mt/yolov6mt_n_416_400e_coco_w_nms.onnx) &#124; [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov6mt/yolov6mt_n_416_400e_coco_wo_nms.onnx) |
-| YOLOv6mt-n |  640   | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov6mt/yolov6mt_n_400e_coco_w_nms.zip) &#124; [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov6mt/yolov6mt_n_400e_coco_wo_nms.zip) | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov6mt/yolov6mt_n_400e_coco_w_nms.onnx) &#124; [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov6mt/yolov6mt_n_400e_coco_wo_nms.onnx) |
-| YOLOv6mt-t |  640   | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov6mt/yolov6mt_t_400e_coco_w_nms.zip) &#124; [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov6mt/yolov6mt_t_400e_coco_wo_nms.zip) | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov6mt/yolov6mt_t_400e_coco_w_nms.onnx) &#124; [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov6mt/yolov6mt_t_400e_coco_wo_nms.onnx) |
-| YOLOv6mt-s |  640   | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov6mt/yolov6mt_s_400e_coco_w_nms.zip) &#124; [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov6mt/yolov6mt_s_400e_coco_wo_nms.zip) | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov6mt/yolov6mt_s_400e_coco_w_nms.onnx) &#124; [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov6mt/yolov6mt_s_400e_coco_wo_nms.onnx) |
+| yolov6-n |  416   | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov6/yolov6_n_416_400e_coco_w_nms.zip) &#124; [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov6/yolov6_n_416_400e_coco_wo_nms.zip) | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov6/yolov6_n_416_400e_coco_w_nms.onnx) &#124; [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov6/yolov6_n_416_400e_coco_wo_nms.onnx) |
+| yolov6-n |  640   | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov6/yolov6_n_400e_coco_w_nms.zip) &#124; [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov6/yolov6_n_400e_coco_wo_nms.zip) | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov6/yolov6_n_400e_coco_w_nms.onnx) &#124; [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov6/yolov6_n_400e_coco_wo_nms.onnx) |
+| yolov6-t |  640   | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov6/yolov6_t_400e_coco_w_nms.zip) &#124; [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov6/yolov6_t_400e_coco_wo_nms.zip) | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov6/yolov6_t_400e_coco_w_nms.onnx) &#124; [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov6/yolov6_t_400e_coco_wo_nms.onnx) |
+| yolov6-s |  640   | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov6/yolov6_s_400e_coco_w_nms.zip) &#124; [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov6/yolov6_s_400e_coco_wo_nms.zip) | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov6/yolov6_s_400e_coco_w_nms.onnx) &#124; [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov6/yolov6_s_400e_coco_wo_nms.onnx) |
+| yolov6-m |  640   | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov6/yolov6_m_300e_coco_w_nms.zip) &#124; [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov6/yolov6_m_300e_coco_wo_nms.zip) | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov6/yolov6_m_300e_coco_w_nms.onnx) &#124; [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov6/yolov6_m_300e_coco_wo_nms.onnx) |
+| yolov6-l |  640   | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov6/yolov6_l_300e_coco_w_nms.zip) &#124; [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov6/yolov6_l_300e_coco_wo_nms.zip) | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov6/yolov6_l_300e_coco_w_nms.onnx) &#124; [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov6/yolov6_l_300e_coco_wo_nms.onnx) |
+| yolov6-l-silu |  640   | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov6/yolov6_l_silu_300e_coco_w_nms.zip) &#124; [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov6/yolov6_l_silu_300e_coco_wo_nms.zip) | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov6/yolov6_l_silu_300e_coco_w_nms.onnx) &#124; [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov6/yolov6_l_silu_300e_coco_wo_nms.onnx) |
+

 ### [YOLOv7](https://github.com/nemonameless/PaddleDetection_YOLOSeries/tree/develop/configs/yolov7)

@@ -156,7 +186,7 @@

 ### **注意:**
 - 所有模型均使用COCO train2017作为训练集，在COCO val2017上验证精度，模型前带*表示训练更新中。
- - 具体精度和速度细节请查看[PP-YOLOE](../../configs/ppyoloe),[YOLOX](../../configs/yolox),[YOLOv5](https://github.com/nemonameless/PaddleDetection_YOLOSeries/tree/develop/configs/yolov5),[MT-YOLOv6](https://github.com/nemonameless/PaddleDetection_YOLOSeries/tree/develop/configs/yolov6mt),[YOLOv7](https://github.com/nemonameless/PaddleDetection_YOLOSeries/tree/develop/configs/yolov7)。
+ - 具体精度和速度细节请查看[PP-YOLOE](../../configs/ppyoloe),[YOLOX](../../configs/yolox),[YOLOv5](https://github.com/nemonameless/PaddleDetection_YOLOSeries/tree/develop/configs/yolov5),[YOLOv6](https://github.com/nemonameless/PaddleDetection_YOLOSeries/tree/develop/configs),[YOLOv7](https://github.com/nemonameless/PaddleDetection_YOLOSeries/tree/develop/configs/yolov7)。
 - 模型推理耗时(ms)为TensorRT-FP16下测试的耗时，不包含数据预处理和模型输出后处理(NMS)的耗时。测试采用单卡V100，batch size=1，测试环境为**paddlepaddle-2.3.0**, **CUDA 11.2**, **CUDNN 8.2**, **GCC-8.2**, **TensorRT 8.0.3.4**，具体请参考各自模型主页。
 - **统计参数量Params(M)**，可以将以下代码插入[trainer.py](https://github.com/nemonameless/PaddleDetection_YOLOSeries/blob/develop/ppdet/engine/trainer.py#L150)。
  ```python
@@ -180,7 +210,7 @@
 并重新导出，使用时再**另接自己写的NMS后处理**。
 - 基于[PaddleSlim](https://github.com/PaddlePaddle/PaddleSlim)对YOLO系列模型进行量化训练，可以实现精度基本无损，速度普遍提升30%以上，具体请参照[模型自动化压缩工具ACT](https://github.com/PaddlePaddle/PaddleSlim/tree/develop/example/auto_compression)。
 - [PP-YOLOE](../../configs/ppyoloe),[PP-YOLOE+](../../configs/ppyoloe),[YOLOv3](../../configs/yolov3)和[YOLOX](../../configs/yolox)推荐在[PaddleDetection](https://github.com/PaddlePaddle/PaddleDetection)里使用，会最先发布**PP-YOLO系列特色检测模型的最新进展**。
- - [YOLOv5](https://github.com/nemonameless/PaddleDetection_YOLOSeries/tree/develop/configs/yolov5),[YOLOv7](https://github.com/nemonameless/PaddleDetection_YOLOSeries/tree/develop/configs/yolov7)和[MT-YOLOv6](https://github.com/nemonameless/PaddleDetection_YOLOSeries/tree/develop/configs/yolov6mt)由于GPL协议而不合入[PaddleDetection](https://github.com/PaddlePaddle/PaddleDetection)主代码库。
+ - [YOLOv5](https://github.com/nemonameless/PaddleDetection_YOLOSeries/tree/develop/configs/yolov5),[YOLOv7](https://github.com/nemonameless/PaddleDetection_YOLOSeries/tree/develop/configs/yolov7)和[YOLOv6](https://github.com/nemonameless/PaddleDetection_YOLOSeries/tree/develop/configs/yolov6)由于GPL协议而不合入[PaddleDetection](https://github.com/PaddlePaddle/PaddleDetection)主代码库。
 - **paddlepaddle版本推荐使用2.3.0版本以上**。