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PaddleDetection
未验证 提交 9f5d141b 编写于 作者: F Feng Ni 提交者： GitHub
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[cherry-pick] add yoloseries doc (#6874) 

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README_cn.md
浏览文件 @ 9f5d141b
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- 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
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- **高性能**: 基于飞桨的高性能内核，模型训练速度及显存占用优势明显。支持FP16训练, 支持多机训练。
<div align="center">
<img src="https://user-images.githubusercontent.com/22989727/186703085-8740e135-d61f-41df-9a29-30273285baa7.png" width="800"/>
<img src="https://user-images.githubusercontent.com/22989727/186810676-29078214-27ab-45eb-9adb-5dea2b0d035b.png" width="800"/>
</div>
## <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
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**说明：**
- `CBResNet`为`Cascade-Faster-RCNN-CBResNet200vd-FPN`模型，COCO数据集mAP高达53.3%
- `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-YOLO`在COCO数据集精度45.9%，Tesla V100预测速度72.9FPS，精度速度均优于[YOLOv4](https://arxiv.org/abs/2004.10934)
- `PP-YOLO v2`是对`PP-YOLO`模型的进一步优化，在COCO数据集精度49.5%，Tesla V100预测速度68.9FPS
- `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
- [`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>
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| [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>
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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 |
|:--------- |:------------------------ |:---------------------------------- |:----------------------:|:---------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------:|
| 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)
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docs/feature_models/YOLOSERIES_MODEL.md 0 → 100644
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简体中文 | [English](YOLOSERIES_MODEL_en.md)
# YOLOSeries
## 内容
- [简介](#简介)
- [模型库](#模型库)
- [PP-YOLOE](#PP-YOLOE)
- [YOLOX](#YOLOX)
- [YOLOv5](#YOLOv5)
- [MT-YOLOv6](#MT-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)
- [自定义数据集](#自定义数据集)
## 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！
**注意:**
- 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。
## 模型库
### [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 |
| :------------- | :------- | :-------: | :------: | :------------: | :---------------------: | :----------------: |:---------: | :------: |:---------------: |:-----: |
| PP-YOLOE-s | 640 | 32 | 400e | 2.9 | 43.4 | 60.0 | 7.93 | 17.36 | [model](https://paddledet.bj.bcebos.com/models/ppyoloe_crn_s_400e_coco.pdparams) | [config](../../configs/ppyoloe/ppyoloe_crn_s_400e_coco.yml) |
| PP-YOLOE-s | 640 | 32 | 300e | 2.9 | 43.0 | 59.6 | 7.93 | 17.36 | [model](https://paddledet.bj.bcebos.com/models/ppyoloe_crn_s_300e_coco.pdparams) | [config](../../configs/ppyoloe/ppyoloe_crn_s_300e_coco.yml) |
| PP-YOLOE-m | 640 | 28 | 300e | 6.0 | 49.0 | 65.9 | 23.43 | 49.91 | [model](https://paddledet.bj.bcebos.com/models/ppyoloe_crn_m_300e_coco.pdparams) | [config](../../configs/ppyoloe/ppyoloe_crn_m_300e_coco.yml) |
| PP-YOLOE-l | 640 | 20 | 300e | 8.7 | 51.4 | 68.6 | 52.20 | 110.07 | [model](https://paddledet.bj.bcebos.com/models/ppyoloe_crn_l_300e_coco.pdparams) | [config](../../configs/ppyoloe/ppyoloe_crn_l_300e_coco.yml) |
| PP-YOLOE-x | 640 | 16 | 300e | 14.9 | 52.3 | 69.5 | 98.42 | 206.59 |[model](https://paddledet.bj.bcebos.com/models/ppyoloe_crn_x_300e_coco.pdparams) | [config](../../configs/ppyoloe/ppyoloe_crn_x_300e_coco.yml) |
| PP-YOLOE-tiny ConvNeXt| 640 | 16 | 36e | - | 44.6 | 63.3 | 33.04 | 13.87 | [model](https://paddledet.bj.bcebos.com/models/ppyoloe_convnext_tiny_36e_coco.pdparams) | [config](../../configs/convnext/ppyoloe_convnext_tiny_36e_coco.yml) |
| **PP-YOLOE+_s** | 640 | 8 | 80e | 2.9 | **43.7** | **60.6** | 7.93 | 17.36 | [model](https://paddledet.bj.bcebos.com/models/ppyoloe_plus_crn_s_80e_coco.pdparams) | [config](../../configs/ppyoloe/ppyoloe_plus_crn_s_80e_coco.yml) |
| **PP-YOLOE+_m** | 640 | 8 | 80e | 6.0 | **49.8** | **67.1** | 23.43 | 49.91 | [model](https://paddledet.bj.bcebos.com/models/ppyoloe_plus_crn_m_80e_coco.pdparams) | [config](../../configs/ppyoloe/ppyoloe_plus_crn_m_80e_coco.yml) |
| **PP-YOLOE+_l** | 640 | 8 | 80e | 8.7 | **52.9** | **70.1** | 52.20 | 110.07 | [model](https://paddledet.bj.bcebos.com/models/ppyoloe_plus_crn_l_80e_coco.pdparams) | [config](../../configs/ppyoloe/ppyoloe_plus_crn_l_80e_coco.yml) |
| **PP-YOLOE+_x** | 640 | 8 | 80e | 14.9 | **54.7** | **72.0** | 98.42 | 206.59 |[model](https://paddledet.bj.bcebos.com/models/ppyoloe_plus_crn_x_80e_coco.pdparams) | [config](../../configs/ppyoloe/ppyoloe_plus_crn_x_80e_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) |
| :-------- | :--------: | :---------------------: | :----------------: |
| PP-YOLOE-s(400epoch) | 640 | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/ppyoloe/ppyoloe_crn_s_400e_coco_w_nms.zip) &#124; [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/ppyoloe/ppyoloe_crn_s_400e_coco_wo_nms.zip) | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/ppyoloe/ppyoloe_crn_s_400e_coco_w_nms.onnx) &#124; [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/ppyoloe/ppyoloe_crn_s_400e_coco_wo_nms.onnx) |
| PP-YOLOE-s | 640 | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/ppyoloe/ppyoloe_crn_s_300e_coco_w_nms.zip) &#124; [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/ppyoloe/ppyoloe_crn_s_300e_coco_wo_nms.zip) | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/ppyoloe/ppyoloe_crn_s_300e_coco_w_nms.onnx) &#124; [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/ppyoloe/ppyoloe_crn_s_300e_coco_wo_nms.onnx) |
| PP-YOLOE-m | 640 | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/ppyoloe/ppyoloe_crn_m_300e_coco_w_nms.zip) &#124; [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/ppyoloe/ppyoloe_crn_m_300e_coco_wo_nms.zip) | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/ppyoloe/ppyoloe_crn_m_300e_coco_w_nms.onnx) &#124; [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/ppyoloe/ppyoloe_crn_m_300e_coco_wo_nms.onnx) |
| PP-YOLOE-l | 640 | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/ppyoloe/ppyoloe_crn_l_300e_coco_w_nms.zip) &#124; [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/ppyoloe/ppyoloe_crn_l_300e_coco_wo_nms.zip) | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/ppyoloe/ppyoloe_crn_l_300e_coco_w_nms.onnx) &#124; [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/ppyoloe/ppyoloe_crn_l_300e_coco_wo_nms.onnx) |
| PP-YOLOE-x | 640 | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/ppyoloe/ppyoloe_crn_x_300e_coco_w_nms.zip) &#124; [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/ppyoloe/ppyoloe_crn_x_300e_coco_wo_nms.zip) | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/ppyoloe/ppyoloe_crn_x_300e_coco_w_nms.onnx) &#124; [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/ppyoloe/ppyoloe_crn_x_300e_coco_wo_nms.onnx) |
| **PP-YOLOE+_s** | 640 | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/ppyoloe/ppyoloe_plus_crn_s_80e_coco_w_nms.zip) &#124; [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/ppyoloe/ppyoloe_plus_crn_s_80e_coco_wo_nms.zip) | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/ppyoloe/ppyoloe_plus_crn_s_80e_coco_w_nms.onnx) &#124; [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/ppyoloe/ppyoloe_plus_crn_s_80e_coco_wo_nms.onnx) |
| **PP-YOLOE+_m** | 640 | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/ppyoloe/ppyoloe_plus_crn_m_80e_coco_w_nms.zip) &#124; [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/ppyoloe/ppyoloe_plus_crn_m_80e_coco_wo_nms.zip) | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/ppyoloe/ppyoloe_plus_crn_m_80e_coco_w_nms.onnx) &#124; [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/ppyoloe/ppyoloe_plus_crn_m_80e_coco_wo_nms.onnx) |
| **PP-YOLOE+_l** | 640 | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/ppyoloe/ppyoloe_plus_crn_l_80e_coco_w_nms.zip) &#124; [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/ppyoloe/ppyoloe_plus_crn_l_80e_coco_wo_nms.zip) | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/ppyoloe/ppyoloe_plus_crn_l_80e_coco_w_nms.onnx) &#124; [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/ppyoloe/ppyoloe_plus_crn_l_80e_coco_wo_nms.onnx) |
| **PP-YOLOE+_x** | 640 | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/ppyoloe/ppyoloe_plus_crn_x_80e_coco_w_nms.zip) &#124; [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/ppyoloe/ppyoloe_plus_crn_x_80e_coco_wo_nms.zip) | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/ppyoloe/ppyoloe_plus_crn_x_80e_coco_w_nms.onnx) &#124; [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/ppyoloe/ppyoloe_plus_crn_x_80e_coco_wo_nms.onnx) |
### [YOLOX](../../configs/yolox)
| 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 |
| :------------- | :------- | :-------: | :------: | :------------: | :---------------------: | :----------------: |:---------: | :------: |:---------------: |:-----: |
| YOLOX-nano | 416 | 8 | 300e | 2.3 | 26.1 | 42.0 | 0.91 | 1.08 | [model](https://paddledet.bj.bcebos.com/models/yolox_nano_300e_coco.pdparams) | [config](../../configs/yolox/yolox_nano_300e_coco.yml) |
| YOLOX-tiny | 416 | 8 | 300e | 2.8 | 32.9 | 50.4 | 5.06 | 6.45 | [model](https://paddledet.bj.bcebos.com/models/yolox_tiny_300e_coco.pdparams) | [config](../../configs/yolox/yolox_tiny_300e_coco.yml) |
| YOLOX-s | 640 | 8 | 300e | 3.0 | 40.4 | 59.6 | 9.0 | 26.8 | [model](https://paddledet.bj.bcebos.com/models/yolox_s_300e_coco.pdparams) | [config](../../configs/yolox/yolox_s_300e_coco.yml) |
| YOLOX-m | 640 | 8 | 300e | 5.8 | 46.9 | 65.7 | 25.3 | 73.8 | [model](https://paddledet.bj.bcebos.com/models/yolox_m_300e_coco.pdparams) | [config](../../configs/yolox/yolox_m_300e_coco.yml) |
| YOLOX-l | 640 | 8 | 300e | 9.3 | 50.1 | 68.8 | 54.2 | 155.6 | [model](https://paddledet.bj.bcebos.com/models/yolox_l_300e_coco.pdparams) | [config](../../configs/yolox/yolox_l_300e_coco.yml) |
| YOLOX-x | 640 | 8 | 300e | 16.6 | **51.8** | **70.6** | 99.1 | 281.9 | [model](https://paddledet.bj.bcebos.com/models/yolox_x_300e_coco.pdparams) | [config](../../configs/yolox/yolox_x_300e_coco.yml) |
YOLOX-cdn-tiny | 416 | 8 | 300e | 1.9 | 32.4 | 50.2 | 5.03 | 6.33 | [model](https://paddledet.bj.bcebos.com/models/yolox_cdn_tiny_300e_coco.pdparams) | [config](c../../onfigs/yolox/yolox_cdn_tiny_300e_coco.yml) |
| YOLOX-crn-s | 640 | 8 | 300e | 3.0 | 40.4 | 59.6 | 7.7 | 24.69 | [model](https://paddledet.bj.bcebos.com/models/yolox_crn_s_300e_coco.pdparams) | [config](../../configs/yolox/yolox_crn_s_300e_coco.yml) |
| YOLOX-s ConvNeXt| 640 | 8 | 36e | - | 44.6 | 65.3 | 36.2 | 27.52 | [model](https://paddledet.bj.bcebos.com/models/yolox_convnext_s_36e_coco.pdparams) | [config](../../configs/convnext/yolox_convnext_s_36e_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) |
| :-------- | :--------: | :---------------------: | :----------------: |
| YOLOx-nano | 416 | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolox/yolox_nano_300e_coco_w_nms.zip) &#124; [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolox/yolox_nano_300e_coco_wo_nms.zip) | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolox/yolox_nano_300e_coco_w_nms.onnx) &#124; [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolox/yolox_nano_300e_coco_wo_nms.onnx) |
| YOLOx-tiny | 416 | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolox/yolox_tiny_300e_coco_w_nms.zip) &#124; [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolox/yolox_tiny_300e_coco_wo_nms.zip) | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolox/yolox_tiny_300e_coco_w_nms.onnx) &#124; [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolox/yolox_tiny_300e_coco_wo_nms.onnx) |
| YOLOx-s | 640 | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolox/yolox_s_300e_coco_w_nms.zip) &#124; [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolox/yolox_s_300e_coco_wo_nms.zip) | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolox/yolox_s_300e_coco_w_nms.onnx) &#124; [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolox/yolox_s_300e_coco_wo_nms.onnx) |
| YOLOx-m | 640 | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolox/yolox_m_300e_coco_w_nms.zip) &#124; [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolox/yolox_m_300e_coco_wo_nms.zip) | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolox/yolox_m_300e_coco_w_nms.onnx) &#124; [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolox/yolox_m_300e_coco_wo_nms.onnx) |
| YOLOx-l | 640 | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolox/yolox_l_300e_coco_w_nms.zip) &#124; [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolox/yolox_l_300e_coco_wo_nms.zip) | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolox/yolox_l_300e_coco_w_nms.onnx) &#124; [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolox/yolox_l_300e_coco_wo_nms.onnx) |
| YOLOx-x | 640 | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolox/yolox_x_300e_coco_w_nms.zip) &#124; [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolox/yolox_x_300e_coco_wo_nms.zip) | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolox/yolox_x_300e_coco_w_nms.onnx) &#124; [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolox/yolox_x_300e_coco_wo_nms.onnx) |
### [YOLOv5](https://github.com/nemonameless/PaddleDetection_YOLOSeries/tree/develop/configs/yolov5)
| 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 |
| :------------- | :------- | :-------: | :------: | :------------: | :---------------------: | :----------------: |:---------: | :------: |:---------------: |:-----: |
| YOLOv5-n | 640 | 16 | 300e | 2.6 | 28.0 | 45.7 | 1.87 | 4.52 | [model](https://paddledet.bj.bcebos.com/models/yolov5_n_300e_coco.pdparams) | [config](https://github.com/nemonameless/PaddleDetection_YOLOSeries/tree/develop/configs/yolov5/yolov5_n_300e_coco.yml) |
| YOLOv5-s | 640 | 8 | 300e | 3.2 | 37.0 | 55.9 | 7.24 | 16.54 | [model](https://paddledet.bj.bcebos.com/models/yolov5_s_300e_coco.pdparams) | [config](https://github.com/nemonameless/PaddleDetection_YOLOSeries/tree/develop/configs/yolov5/yolov5_s_300e_coco.yml) |
| YOLOv5-m | 640 | 5 | 300e | 5.2 | 45.3 | 63.8 | 21.19 | 49.08 | [model](https://paddledet.bj.bcebos.com/models/yolov5_m_300e_coco.pdparams) | [config](https://github.com/nemonameless/PaddleDetection_YOLOSeries/tree/develop/configs/yolov5/yolov5_m_300e_coco.yml) |
| YOLOv5-l | 640 | 3 | 300e | 7.9 | 48.6 | 66.9 | 46.56 | 109.32 | [model](https://paddledet.bj.bcebos.com/models/yolov5_l_300e_coco.pdparams) | [config](https://github.com/nemonameless/PaddleDetection_YOLOSeries/tree/develop/configs/yolov5/yolov5_l_300e_coco.yml) |
| YOLOv5-x | 640 | 2 | 300e | 13.7 | **50.6** | **68.7** | 86.75 | 205.92 | [model](https://paddledet.bj.bcebos.com/models/yolov5_x_300e_coco.pdparams) | [config](https://github.com/nemonameless/PaddleDetection_YOLOSeries/tree/develop/configs/yolov5/yolov5_x_300e_coco.yml) |
| YOLOv5-s ConvNeXt| 640 | 8 | 36e | - | 42.4 | 65.3 | 34.54 | 17.96 | [model](https://paddledet.bj.bcebos.com/models/yolov5_convnext_s_36e_coco.pdparams) | [config](https://github.com/nemonameless/PaddleDetection_YOLOSeries/tree/develop/configs/yolov5/yolov5_convnext_s_36e_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) |
| :-------- | :--------: | :---------------------: | :----------------: |
| YOLOv5-n | 640 | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov5/yolov5_n_300e_coco_w_nms.zip) &#124; [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov5/yolov5_n_300e_coco_wo_nms.zip) | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov5/yolov5_n_300e_coco_w_nms.onnx) &#124; [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov5/yolov5_n_300e_coco_wo_nms.onnx) |
| YOLOv5-s | 640 | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov5/yolov5_s_300e_coco_w_nms.zip) &#124; [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov5/yolov5_s_300e_coco_wo_nms.zip) | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov5/yolov5_s_300e_coco_w_nms.onnx) &#124; [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov5/yolov5_s_300e_coco_wo_nms.onnx) |
| YOLOv5-m | 640 | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov5/yolov5_m_300e_coco_w_nms.zip) &#124; [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov5/yolov5_m_300e_coco_wo_nms.zip) | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov5/yolov5_m_300e_coco_w_nms.onnx) &#124; [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov5/yolov5_m_300e_coco_wo_nms.onnx) |
| 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 |
| :------------- | :------- | :-------: | :------: | :---------: | :-----: |:-----: | :-----: |:-----: | :-------------: | :-----: |
| *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) |
#### 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) |
### [YOLOv7](https://github.com/nemonameless/PaddleDetection_YOLOSeries/tree/develop/configs/yolov7)
| 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 |
| :------------- | :------- | :-------: | :------: | :------------: | :---------------------: | :----------------: |:---------: | :------: |:---------------: |:-----: |
| YOLOv7-L | 640 | 32 | 300e | 7.4 | 51.0 | 70.2 | 37.62 | 106.08 |[model](https://paddledet.bj.bcebos.com/models/yolov7_l_300e_coco.pdparams) | [config](https://github.com/nemonameless/PaddleDetection_YOLOSeries/tree/develop/configs/yolov7/yolov7_l_300e_coco.yml) |
| *YOLOv7-X | 640 | 32 | 300e | 12.2 | 53.0 | 70.8 | 71.34 | 190.08 | [model](https://paddledet.bj.bcebos.com/models/yolov7_x_300e_coco.pdparams) | [config](https://github.com/nemonameless/PaddleDetection_YOLOSeries/tree/develop/configs/yolov7/yolov7_x_300e_coco.yml) |
| *YOLOv7P6-W6 | 1280 | 16 | 300e | 25.5 | 54.4 | 71.8 | 70.43 | 360.26 | [model](https://paddledet.bj.bcebos.com/models/yolov7p6_w6_300e_coco.pdparams) | [config](https://github.com/nemonameless/PaddleDetection_YOLOSeries/tree/develop/configs/yolov7/yolov7p6_w6_300e_coco.yml) |
| *YOLOv7P6-E6 | 1280 | 10 | 300e | 31.1 | 55.7 | 73.0 | 97.25 | 515.4 | [model](https://paddledet.bj.bcebos.com/models/yolov7p6_e6_300e_coco.pdparams) | [config](https://github.com/nemonameless/PaddleDetection_YOLOSeries/tree/develop/configs/yolov7/yolov7p6_e6_300e_coco.yml) |
| *YOLOv7P6-D6 | 1280 | 8 | 300e | 37.4 | 56.1 | 73.3 | 133.81 | 702.92 | [model](https://paddledet.bj.bcebos.com/models/yolov7p6_d6_300e_coco.pdparams) | [config](https://github.com/nemonameless/PaddleDetection_YOLOSeries/tree/develop/configs/yolov7/yolov7p6_d6_300e_coco.yml) |
| *YOLOv7P6-E6E | 1280 | 6 | 300e | 48.7 | 56.5 | 73.7 | 151.76 | 843.52 | [model](https://paddledet.bj.bcebos.com/models/yolov7p6_e6e_300e_coco.pdparams) | [config](https://github.com/nemonameless/PaddleDetection_YOLOSeries/tree/develop/configs/yolov7/yolov7p6_e6e_300e_coco.yml) |
| YOLOv7-tiny | 640 | 32 | 300e | - | 37.3 | 54.5 | 6.23 | 6.90 |[model](https://paddledet.bj.bcebos.com/models/yolov7_tiny_300e_coco.pdparams) | [config](https://github.com/nemonameless/PaddleDetection_YOLOSeries/tree/develop/configs/yolov7/yolov7_tiny_300e_coco.yml) |
| YOLOv7-tiny | 416 | 32 | 300e | - | 33.3 | 49.5 | 6.23 | 2.91 |[model](https://paddledet.bj.bcebos.com/models/yolov7_tiny_416_300e_coco.pdparams) | [config](https://github.com/nemonameless/PaddleDetection_YOLOSeries/tree/develop/configs/yolov7/yolov7_tiny_416_300e_coco.yml) |
| YOLOv7-tiny | 320 | 32 | 300e | - | 29.1 | 43.8 | 6.23 | 1.73 |[model](https://paddledet.bj.bcebos.com/models/yolov7_tiny_320_300e_coco.pdparams) | [config](https://github.com/nemonameless/PaddleDetection_YOLOSeries/tree/develop/configs/yolov7/yolov7_tiny_320_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) |
| :-------- | :--------: | :---------------------: | :----------------: |
| YOLOv7-l | 640 | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov7/yolov7_l_300e_coco_w_nms.zip) &#124; [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov7/yolov7_l_300e_coco_wo_nms.zip) | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov7/yolov7_l_300e_coco_w_nms.onnx) &#124; [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov7/yolov7_l_300e_coco_wo_nms.onnx) |
| YOLOv7-x | 640 | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov7/yolov7_x_300e_coco_w_nms.zip) &#124; [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov7/yolov7_x_300e_coco_wo_nms.zip) | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov7/yolov7_x_300e_coco_w_nms.onnx) &#124; [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov7/yolov7_x_300e_coco_wo_nms.onnx) |
| YOLOv7P6-W6 | 1280 | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov7/yolov7p6_w6_300e_coco_w_nms.zip) &#124; [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov7/yolov7p6_w6_300e_coco_wo_nms.zip) | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov7/yolov7p6_w6_300e_coco_w_nms.onnx) &#124; [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov7/yolov7p6_w6_300e_coco_wo_nms.onnx) |
| YOLOv7P6-E6 | 1280 | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov7/yolov7p6_e6_300e_coco_w_nms.zip) &#124; [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov7/yolov7p6_e6_300e_coco_wo_nms.zip) | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov7/yolov7p6_e6_300e_coco_w_nms.onnx) &#124; [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov7/yolov7p6_e6_300e_coco_wo_nms.onnx) |
| YOLOv7P6-D6 | 1280 | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov7/yolov7p6_d6_300e_coco_w_nms.zip) &#124; [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov7/yolov7p6_d6_300e_coco_wo_nms.zip) | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov7/yolov7p6_d6_300e_coco_w_nms.onnx) &#124; [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov7/yolov7p6_d6_300e_coco_wo_nms.onnx) |
| YOLOv7P6-E6E | 1280 | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov7/yolov7p6_e6e_300e_coco_w_nms.zip) &#124; [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov7/yolov7p6_e6e_300e_coco_wo_nms.zip) | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov7/yolov7p6_e6e_300e_coco_w_nms.onnx) &#124; [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov7/yolov7p6_e6e_300e_coco_wo_nms.onnx) |
| YOLOv7-tiny | 640 | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov7/yolov7_tiny_300e_coco_w_nms.zip) &#124; [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov7/yolov7_tiny_300e_coco_wo_nms.zip) | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov7/yolov7_tiny_300e_coco_w_nms.onnx) &#124; [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov7/yolov7_tiny_300e_coco_wo_nms.onnx) |
| YOLOv7-tiny | 416 | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov7/yolov7_tiny_416_300e_coco_w_nms.zip) &#124; [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov7/yolov7_tiny_416_300e_coco_wo_nms.zip) | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov7/yolov7_tiny_416_300e_coco_w_nms.onnx) &#124; [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov7/yolov7_tiny_416_300e_coco_wo_nms.onnx) |
| YOLOv7-tiny | 320 | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov7/yolov7_tiny_320_300e_coco_w_nms.zip) &#124; [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov7/yolov7_tiny_320_300e_coco_wo_nms.zip) | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov7/yolov7_tiny_320_300e_coco_w_nms.onnx) &#124; [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov7/yolov7_tiny_320_300e_coco_wo_nms.onnx) |
### **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)。
- 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
params = sum([
p.numel() for n, p in self.model.named_parameters()
if all([x not in n for x in ['_mean', '_variance']])
]) # exclude BatchNorm running status
print('Params: ', params / 1e6)
```
- **统计FLOPs(G)**，首先安装[PaddleSlim](https://github.com/PaddlePaddle/PaddleSlim), `pip install paddleslim`，然后设置[runtime.yml](../../configs/runtime.yml)里`print_flops: True`，并且注意确保是**单尺度**下如640x640，**打印的是MACs，FLOPs=2*MACs**。
- 各模型导出后的权重以及ONNX，分为**带(w)**和**不带(wo)**后处理NMS，都提供了下载链接，请参考各自模型主页下载。`w_nms`表示**带NMS后处理**，可以直接使用预测出最终检测框结果如```python deploy/python/infer.py --model_dir=ppyoloe_crn_l_300e_coco_w_nms/ --image_file=demo/000000014439.jpg --device=GPU```；`wo_nms`表示**不带NMS后处理**，是**测速**时使用，如需预测出检测框结果需要找到**对应head中的后处理相关代码**并修改为如下：
```
if self.exclude_nms:
# `exclude_nms=True` just use in benchmark for speed test
# return pred_bboxes.sum(), pred_scores.sum() # 原先是这行，现在注释
return pred_bboxes, pred_scores # 新加这行，表示保留进NMS前的原始结果
else:
bbox_pred, bbox_num, _ = self.nms(pred_bboxes, pred_scores)
return bbox_pred, bbox_num
```
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。
- **paddlepaddle版本推荐使用2.3.0版本以上**。
## 使用指南
下载MS-COCO数据集，[官网](https://cocodataset.org)下载地址为: [annotations](http://images.cocodataset.org/annotations/annotations_trainval2017.zip), [train2017](http://images.cocodataset.org/zips/train2017.zip), [val2017](http://images.cocodataset.org/zips/val2017.zip), [test2017](http://images.cocodataset.org/zips/test2017.zip)。
PaddleDetection团队提供的下载链接为：[coco](https://bj.bcebos.com/v1/paddledet/data/coco.tar)(共约22G)和[test2017](https://bj.bcebos.com/v1/paddledet/data/cocotest2017.zip)，注意test2017可不下载，评估是使用的val2017。
### **一键运行全流程**
```
model_type=ppyoloe # 可修改，如 yolov7
job_name=ppyoloe_crn_l_300e_coco # 可修改，如 yolov7_l_300e_coco
config=configs/${model_type}/${job_name}.yml
log_dir=log_dir/${job_name}
# weights=https://bj.bcebos.com/v1/paddledet/models/${job_name}.pdparams
weights=output/${job_name}/model_final.pdparams
# 1.训练（单卡/多卡）
# CUDA_VISIBLE_DEVICES=0 python3.7 tools/train.py -c ${config} --eval --amp
python3.7 -m paddle.distributed.launch --log_dir=${log_dir} --gpus 0,1,2,3,4,5,6,7 tools/train.py -c ${config} --eval --amp
# 2.评估
CUDA_VISIBLE_DEVICES=0 python3.7 tools/eval.py -c ${config} -o weights=${weights} --classwise
# 3.直接预测
CUDA_VISIBLE_DEVICES=0 python3.7 tools/infer.py -c ${config} -o weights=${weights} --infer_img=demo/000000014439_640x640.jpg --draw_threshold=0.5
# 4.导出模型
CUDA_VISIBLE_DEVICES=0 python3.7 tools/export_model.py -c ${config} -o weights=${weights} # exclude_nms=True trt=True
# 5.部署预测
CUDA_VISIBLE_DEVICES=0 python3.7 deploy/python/infer.py --model_dir=output_inference/${job_name} --image_file=demo/000000014439_640x640.jpg --device=GPU
# 6.部署测速
CUDA_VISIBLE_DEVICES=0 python3.7 deploy/python/infer.py --model_dir=output_inference/${job_name} --image_file=demo/000000014439_640x640.jpg --device=GPU --run_benchmark=True # --run_mode=trt_fp16
# 7.onnx导出
paddle2onnx --model_dir output_inference/${job_name} --model_filename model.pdmodel --params_filename model.pdiparams --opset_version 12 --save_file ${job_name}.onnx
# 8.onnx测速
/usr/local/TensorRT-8.0.3.4/bin/trtexec --onnx=${job_name}.onnx --workspace=4096 --avgRuns=10 --shapes=input:1x3x640x640 --fp16
```
**注意:**
- 将以上命令写在一个脚本文件里如```run.sh```，一键运行命令为：```sh run.sh```，也可命令行一句句去运行。
- 如果想切换模型，只要修改开头两行即可，如:
```
model_type=yolov7
job_name=yolov7_l_300e_coco
```
- **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
params = sum([
p.numel() for n, p in self.model.named_parameters()
if all([x not in n for x in ['_mean', '_variance']])
]) # exclude BatchNorm running status
print('Params: ', params / 1e6)
```
- **统计FLOPs(G)**，首先安装[PaddleSlim](https://github.com/PaddlePaddle/PaddleSlim), `pip install paddleslim`，然后设置[runtime.yml](../../configs/runtime.yml)里`print_flops: True`，并且注意确保是**单尺度**下如640x640，**打印的是MACs，FLOPs=2*MACs**。
### 自定义数据集
#### 数据集准备：
1.自定义数据集的标注制作，请参考[DetAnnoTools](../tutorials/data/DetAnnoTools.md);
2.自定义数据集的训练准备，请参考[PrepareDataSet](../tutorials/PrepareDataSet.md)。
#### fintune训练：
除了更改数据集的路径外，训练一般推荐加载**对应模型的COCO预训练权重**去fintune，会更快收敛和达到更高精度，如：
```base
# 单卡fintune训练：
# CUDA_VISIBLE_DEVICES=0 python3.7 tools/train.py -c ${config} --eval --amp -o pretrain_weights=https://paddledet.bj.bcebos.com/models/ppyoloe_crn_l_300e_coco.pdparams
# 多卡fintune训练：
python3.7 -m paddle.distributed.launch --log_dir=./log_dir --gpus 0,1,2,3,4,5,6,7 tools/train.py -c ${config} --eval --amp -o pretrain_weights=https://paddledet.bj.bcebos.com/models/ppyoloe_crn_l_300e_coco.pdparams
```
**注意:**
- fintune训练一般会提示head分类分支最后一层卷积的通道数没对应上，属于正常情况，是由于自定义数据集一般和COCO数据集种类数不一致；
- fintune训练一般epoch数可以设置更少，lr设置也更小点如1/10，最高精度可能出现在中间某个epoch；
#### 预测和导出：
使用自定义数据集预测和导出模型时，如果TestDataset数据集路径设置不正确会默认使用COCO 80类。
除了TestDataset数据集路径设置正确外，也可以自行修改和添加对应的label_list.txt文件(一行记录一个对应种类)，TestDataset中的anno_path也可设置为绝对路径，如：
```
TestDataset:
!ImageFolder
anno_path: label_list.txt # 如不使用dataset_dir，则anno_path即为相对于PaddleDetection主目录的相对路径
# dataset_dir: dataset/my_coco # 如使用dataset_dir，则dataset_dir/anno_path作为新的anno_path
```
label_list.txt里的一行记录一个对应种类，如下所示：
```
person
vehicle
```
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