未验证 提交 9f7d0759 编写于 作者: F Feng Ni 提交者: GitHub

update yoloseries doc (#7005)

* update yoloseries doc, test=document_fix

* fix link, test=document_fix
上级 9ca2fd65
......@@ -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
......@@ -108,7 +108,7 @@
<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>
......
......@@ -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).
......
......@@ -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
......
......@@ -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)
......
......@@ -99,9 +99,9 @@ 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[YOLOv5](https://github.com/nemonameless/PaddleDetection_YOLOSeries/tree/develop/configs/yolov5)
### MT-YOLOv6
### YOLOv6
请参考[MT-YOLOv6](https://github.com/nemonameless/PaddleDetection_YOLOSeries/tree/develop/configs/yolov6mt)
请参考[YOLOv6](https://github.com/nemonameless/PaddleDetection_YOLOSeries/tree/develop/configs/yolov6)
### YOLOv7
......
......@@ -98,9 +98,9 @@ Please refer to[YOLOX](https://github.com/PaddlePaddle/PaddleDetection/tree/deve
Please refer to[YOLOv5](https://github.com/nemonameless/PaddleDetection_YOLOSeries/tree/develop/configs/yolov5)
### MT-YOLOv6
### YOLOv6
Please refer to[MT-YOLOv6](https://github.com/nemonameless/PaddleDetection_YOLOSeries/tree/develop/configs/yolov6mt)
Please refer to[YOLOv6](https://github.com/nemonameless/PaddleDetection_YOLOSeries/tree/develop/configs/yolov6)
### YOLOv7
......
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``YOLOv5`等模型,支持PaddleSlim无损加速量化训练`PP-YOLOE`,`YOLOv5`,`MT-YOLOv6``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)[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)[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)[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)[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">
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</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)[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)[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)[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**
......
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