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简体中文 | [English](PaddleYOLO_MODEL_en.md)
# [**PaddleYOLO**](https://github.com/PaddlePaddle/PaddleYOLO)
## 内容
- [**PaddleYOLO**](#paddleyolo)
- [内容](#内容)
- [简介](#简介)
- [更新日志](#更新日志)
- [模型库](#模型库)
- [PP-YOLOE](#pp-yoloe)
- [YOLOX](#yolox)
- [YOLOv5](#yolov5)
- [YOLOv6](#yolov6)
- [YOLOv7](#yolov7)
- [YOLOv8](#yolov8)
- [RTMDet](#rtmdet)
- [**注意:**](#注意)
- [VOC](#voc)
- [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)
- [数据集准备：](#数据集准备)
- [fintune训练：](#fintune训练)
- [预测和导出：](#预测和导出)
## 简介
**PaddleYOLO**是基于[PaddleDetection](https://github.com/PaddlePaddle/PaddleDetection)的YOLO系列模型库，**只包含YOLO系列模型的相关代码**，支持`YOLOv3`,`PP-YOLO`,`PP-YOLOv2`,`PP-YOLOE`,`PP-YOLOE+`,`YOLOX`,`YOLOv5`,`YOLOv6`,`YOLOv7`,`YOLOv8`,`RTMDet`等模型，欢迎一起使用和建设！
## 更新日志
* 【2022/01/10】支持[YOLOv8](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/yolov8)预测和部署；
* 【2022/09/29】支持[RTMDet](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/rtmdet)预测和部署；
* 【2022/09/26】发布[`PaddleYOLO`](https://github.com/PaddlePaddle/PaddleYOLO)模型套件；
* 【2022/09/19】支持[`YOLOv6`](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/yolov6)新版，包括n/t/s/m/l模型；
* 【2022/08/23】发布`YOLOSeries`代码库: 支持`YOLOv3`,`PP-YOLOE`,`PP-YOLOE+`,`YOLOX`,`YOLOv5`,`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`,`YOLOv6`和`YOLOv7`等模型，详情可阅读[此文章](https://mp.weixin.qq.com/s/Hki01Zs2lQgvLSLWS0btrA)；
**注意:**
- **PaddleYOLO**代码库协议为**GPL 3.0**，[YOLOv5](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/yolov5),[YOLOv6](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/yolov6),[YOLOv7](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/yolov7)和[YOLOv8](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/yolov8)这几类模型代码不合入[PaddleDetection](https://github.com/PaddlePaddle/PaddleDetection)，其余YOLO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**；；
- **PaddleYOLO**代码库**推荐使用paddlepaddle-2.3.2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，**Windows平台请安装paddle develop版本**；
- PaddleYOLO 的[Roadmap](https://github.com/PaddlePaddle/PaddleYOLO/issues/44) issue用于收集用户的需求，欢迎提出您的建议和需求。
- 训练**自定义数据集**请参照[文档](#自定义数据集)和[issue](https://github.com/PaddlePaddle/PaddleYOLO/issues/43)。请首先**确保加载了COCO权重作为预训练**，YOLO检测模型建议**总`batch_size`至少大于`64`**去训练，如果资源不够请**换小模型**或**减小模型的输入尺度**，为了保障较高检测精度，**尽量不要尝试单卡训和总`batch_size`小于`32`训**；
## 模型库
### [PP-YOLOE](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/ppyoloe)
<details>
<summary> 基础模型 </summary>
| 网络模型 | 输入尺寸 | 图片数/GPU | 学习率策略 | TRT-FP16-Latency(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](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/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](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/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](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/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](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/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](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/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](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/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](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/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](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/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](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/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](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/ppyoloe/ppyoloe_plus_crn_x_80e_coco.yml) |
</details>
<details>
<summary> 部署模型 </summary>
| 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) |
</details>
### [YOLOX](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/yolox)
<details>
<summary> 基础模型 </summary>
| 网络模型 | 输入尺寸 | 图片数/GPU | 学习率策略 | TRT-FP16-Latency(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](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/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](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/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](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/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](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/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](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/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](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/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](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/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](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/convnext/yolox_convnext_s_36e_coco.yml) |
</details>
<details>
<summary> 部署模型 </summary>
| 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) |
</details>
### [YOLOv5](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/yolov5)
<details>
<summary> 基础模型 </summary>
| 网络模型 | 输入尺寸 | 图片数/GPU | 学习率策略 | TRT-FP16-Latency(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/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/yolov5/yolov5_n_300e_coco.yml) |
| YOLOv5-s | 640 | 16 | 300e | 3.2 | 37.6 | 56.7 | 7.24 | 16.54 | [model](https://paddledet.bj.bcebos.com/models/yolov5_s_300e_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/yolov5/yolov5_s_300e_coco.yml) |
| YOLOv5-m | 640 | 16 | 300e | 5.2 | 45.4 | 64.1 | 21.19 | 49.08 | [model](https://paddledet.bj.bcebos.com/models/yolov5_m_300e_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/yolov5/yolov5_m_300e_coco.yml) |
| YOLOv5-l | 640 | 16 | 300e | 7.9 | 48.9 | 67.1 | 46.56 | 109.32 | [model](https://paddledet.bj.bcebos.com/models/yolov5_l_300e_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/yolov5/yolov5_l_300e_coco.yml) |
| YOLOv5-x | 640 | 16 | 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/PaddlePaddle/PaddleYOLO/tree/release/2.5/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/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/yolov5/yolov5_convnext_s_36e_coco.yml) |
| *YOLOv5p6-n | 1280 | 16 | 300e | - | 35.9 | 54.2 | 3.25 | 9.23 | [model](https://paddledet.bj.bcebos.com/models/yolov5p6_n_300e_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/yolov5/yolov5p6_n_300e_coco.yml) |
| *YOLOv5p6-s | 1280 | 16 | 300e | - | 44.5 | 63.3 | 12.63 | 33.81 | [model](https://paddledet.bj.bcebos.com/models/yolov5p6_s_300e_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/yolov5/yolov5p6_s_300e_coco.yml) |
| *YOLOv5p6-m | 1280 | 16 | 300e | - | 51.1 | 69.0 | 35.73 | 100.21 | [model](https://paddledet.bj.bcebos.com/models/yolov5p6_m_300e_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/yolov5/yolov5p6_m_300e_coco.yml) |
| *YOLOv5p6-l | 1280 | 8 | 300e | - | 53.4 | 71.0 | 76.77 | 223.09 | [model](https://paddledet.bj.bcebos.com/models/yolov5p6_l_300e_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/yolov5/yolov5p6_l_300e_coco.yml) |
| *YOLOv5p6-x | 1280 | 8 | 300e | - | 54.7 | 72.4 | 140.80 | 420.03 | [model](https://paddledet.bj.bcebos.com/models/yolov5p6_x_300e_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/yolov5/yolov5p6_x_300e_coco.yml) |
</details>
<details>
<summary> 部署模型 </summary>
| 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) |
</details>
### [YOLOv6](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/yolov6)
<details>
<summary> 基础模型 </summary>
| 网络网络 | 输入尺寸 | 图片数/GPU | 学习率策略 | TRT-FP16-Latency(ms) | mAP | AP50 | Params(M) | FLOPs(G) | 下载链接 | 配置文件 |
| :------------- | :------- | :-------: | :------: | :---------: | :-----: |:-----: | :-----: |:-----: | :-------------: | :-----: |
| *YOLOv6-n | 640 | 16 | 300e(+300e) | 2.0 | 37.5 | 53.1 | 5.07 | 12.49 |[model](https://paddledet.bj.bcebos.com/models/yolov6_n_300e_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/yolov6/yolov6_n_300e_coco.yml) |
| *YOLOv6-s | 640 | 32 | 300e(+300e) | 2.7 | 44.8 | 61.7 | 20.18 | 49.36 |[model](https://paddledet.bj.bcebos.com/models/yolov6_s_300e_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/yolov6/yolov6_s_300e_coco.yml) |
| *YOLOv6-m | 640 | 32 | 300e(+300e) | - | 49.5 | 66.9 | 37.74 | 92.47 |[model](https://paddledet.bj.bcebos.com/models/yolov6_m_300e_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/yolov6/yolov6_m_300e_coco.yml) |
| *YOLOv6-l(silu) | 640 | 32 | 300e(+300e) | - | 52.2 | 70.2 | 59.66 | 149.4 |[model](https://paddledet.bj.bcebos.com/models/yolov6_l_300e_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/yolov6/yolov6_l_300e_coco.yml) |
</details>
<details>
<summary> 部署模型 </summary>
| 网络模型 | 输入尺寸 | 导出后的权重(w/o NMS) | ONNX(w/o NMS) |
| :-------- | :--------: | :---------------------: | :----------------: |
| yolov6-n | 640 | [(w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov6/yolov6_n_300e_coco_w_nms.zip) &#124; [(w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov6/yolov6_n_300e_coco_wo_nms.zip) | [(w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov6/yolov6_n_300e_coco_w_nms.onnx) &#124; [(w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov6/yolov6_n_300e_coco_wo_nms.onnx) |
| yolov6-s | 640 | [(w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov6/yolov6_s_300e_coco_w_nms.zip) &#124; [(w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov6/yolov6_s_300e_coco_wo_nms.zip) | [(w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov6/yolov6_s_300e_coco_w_nms.onnx) &#124; [(w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov6/yolov6_s_300e_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(silu) | 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) |
</details>
### [YOLOv7](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/yolov7)
<details>
<summary> 基础模型 </summary>
| 网络模型 | 输入尺寸 | 图片数/GPU | 学习率策略 | TRT-FP16-Latency(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/PaddlePaddle/PaddleYOLO/tree/release/2.5/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/PaddlePaddle/PaddleYOLO/tree/release/2.5/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/PaddlePaddle/PaddleYOLO/tree/release/2.5/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/PaddlePaddle/PaddleYOLO/tree/release/2.5/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/PaddlePaddle/PaddleYOLO/tree/release/2.5/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/PaddlePaddle/PaddleYOLO/tree/release/2.5/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/PaddlePaddle/PaddleYOLO/tree/release/2.5/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/PaddlePaddle/PaddleYOLO/tree/release/2.5/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/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/yolov7/yolov7_tiny_320_300e_coco.yml) |
</details>
<details>
<summary> 部署模型 </summary>
| 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) |
</details>
### [YOLOv8](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/yolov8)
<details>
<summary> 基础模型 </summary>
| 网络网络 | 输入尺寸 | 图片数/GPU | 学习率策略 | TRT-FP16-Latency(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 |
| :------------- | :------- | :-------: | :------: | :------------: | :---------------------: | :----------------: |:---------: | :------: |:---------------: |:-----: |
| *YOLOv8-n | 640 | 16 | 500e | 2.4 | 37.3 | 53.0 | 3.16 | 8.7 | [model](https://paddledet.bj.bcebos.com/models/yolov8_n_300e_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/yolov8/yolov8_n_300e_coco.yml) |
| *YOLOv8-s | 640 | 16 | 500e | 3.4 | 44.9 | 61.8 | 11.17 | 28.6 | [model](https://paddledet.bj.bcebos.com/models/yolov8_s_300e_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/yolov8/yolov8_s_300e_coco.yml) |
| *YOLOv8-m | 640 | 16 | 500e | 6.5 | 50.2 | 67.3 | 25.90 | 78.9 | [model](https://paddledet.bj.bcebos.com/models/yolov8_m_300e_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/yolov8/yolov8_m_300e_coco.yml) |
| *YOLOv8-l | 640 | 16 | 500e | 10.0 | 52.8 | 69.6 | 43.69 | 165.2 | [model](https://paddledet.bj.bcebos.com/models/yolov8_l_300e_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/yolov8/yolov8_l_300e_coco.yml) |
| *YOLOv8-x | 640 | 16 | 500e | 15.1 | 53.8 | 70.6 | 68.23 | 257.8 | [model](https://paddledet.bj.bcebos.com/models/yolov8_x_300e_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/yolov8/yolov8_x_300e_coco.yml) |
| *YOLOv8-P6-x | 1280 | 16 | 500e | 55.0 | - | - | 97.42 | 522.93 | [model](https://paddledet.bj.bcebos.com/models/yolov8p6_x_500e_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/yolov8/yolov8p6_x_500e_coco.yml) |
</details>
<details>
<summary> 部署模型 </summary>
| 网络模型 | 输入尺寸 | 导出后的权重(w/o NMS) | ONNX(w/o NMS) |
| :-------- | :--------: | :---------------------: | :----------------: |
| YOLOv8-n | 640 | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov8/yolov8_n_500e_coco_w_nms.zip) &#124; [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov8/yolov8_n_500e_coco_wo_nms.zip) | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov8/yolov8_n_500e_coco_w_nms.onnx) &#124; [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov8/yolov8_n_500e_coco_wo_nms.onnx) |
| YOLOv8-s | 640 | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov8/yolov8_s_500e_coco_w_nms.zip) &#124; [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov8/yolov8_s_500e_coco_wo_nms.zip) | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov8/yolov8_s_500e_coco_w_nms.onnx) &#124; [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov8/yolov8_s_500e_coco_wo_nms.onnx) |
| YOLOv8-m | 640 | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov8/yolov8_m_500e_coco_w_nms.zip) &#124; [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov8/yolov8_m_500e_coco_wo_nms.zip) | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov8/yolov8_m_500e_coco_w_nms.onnx) &#124; [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov8/yolov8_m_500e_coco_wo_nms.onnx) |
| YOLOv8-l | 640 | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov8/yolov8_l_500e_coco_w_nms.zip) &#124; [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov8/yolov8_l_500e_coco_wo_nms.zip) | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov8/yolov8_l_500e_coco_w_nms.onnx) &#124; [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov8/yolov8_l_500e_coco_wo_nms.onnx) |
| YOLOv8-x | 640 | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov8/yolov8_x_500e_coco_w_nms.zip) &#124; [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov8/yolov8_x_500e_coco_wo_nms.zip) | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov8/yolov8_x_500e_coco_w_nms.onnx) &#124; [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov8/yolov8_x_500e_coco_wo_nms.onnx) |
</details>
### [RTMDet](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/rtmdet)
<details>
<summary> 基础模型 </summary>
| 网络网络 | 输入尺寸 | 图片数/GPU | 学习率策略 | TRT-FP16-Latency(ms) | mAP | AP50 | Params(M) | FLOPs(G) | 下载链接 | 配置文件 |
| :------------- | :------- | :-------: | :------: | :---------: | :-----: |:-----: | :-----: |:-----: | :-------------: | :-----: |
| *RTMDet-t | 640 | 32 | 300e | 2.8 | 40.9 | 57.9 | 4.90 | 16.21 |[model](https://paddledet.bj.bcebos.com/models/rtmdet_t_300e_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/rtmdet/rtmdet_t_300e_coco.yml) |
| *RTMDet-s | 640 | 32 | 300e | 3.3 | 44.5 | 62.0 | 8.89 | 29.71 |[model](https://paddledet.bj.bcebos.com/models/rtmdet_s_300e_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/rtmdet/rtmdet_s_300e_coco.yml) |
| *RTMDet-m | 640 | 32 | 300e | 6.4 | 49.1 | 66.8 | 24.71 | 78.47 |[model](https://paddledet.bj.bcebos.com/models/rtmdet_m_300e_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/rtmdet/rtmdet_m_300e_coco.yml) |
| *RTMDet-l | 640 | 32 | 300e | 10.2 | 51.2 | 68.8 | 52.31 | 160.32 |[model](https://paddledet.bj.bcebos.com/models/rtmdet_l_300e_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/rtmdet/rtmdet_l_300e_coco.yml) |
| *RTMDet-x | 640 | 32 | 300e | 18.0 | 52.6 | 70.4 | 94.86 | 283.12 |[model](https://paddledet.bj.bcebos.com/models/rtmdet_x_300e_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/rtmdet/rtmdet_x_300e_coco.yml) |
</details>
<details>
<summary> 部署模型 </summary>
| 网络模型 | 输入尺寸 | 导出后的权重(w/o NMS) | ONNX(w/o NMS) |
| :-------- | :--------: | :---------------------: | :----------------: |
| RTMDet-t | 640 | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/rtmdet/rtmdet_t_300e_coco_w_nms.zip) &#124; [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/rtmdet/rtmdet_t_300e_coco_wo_nms.zip) | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/rtmdet/rtmdet_t_300e_coco_w_nms.onnx) &#124; [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/rtmdet/rtmdet_t_300e_coco_wo_nms.onnx) |
| RTMDet-s | 640 | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/rtmdet/rtmdet_s_300e_coco_w_nms.zip) &#124; [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/rtmdet/rtmdet_s_300e_coco_wo_nms.zip) | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/rtmdet/rtmdet_s_300e_coco_w_nms.onnx) &#124; [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/rtmdet/rtmdet_s_300e_coco_wo_nms.onnx) |
| RTMDet-m | 640 | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/rtmdet/rtmdet_m_300e_coco_w_nms.zip) &#124; [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/rtmdet/rtmdet_m_300e_coco_wo_nms.zip) | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/rtmdet/rtmdet_m_300e_coco_w_nms.onnx) &#124; [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/rtmdet/rtmdet_m_300e_coco_wo_nms.onnx) |
| RTMDet-l | 640 | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/rtmdet/rtmdet_l_300e_coco_w_nms.zip) &#124; [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/rtmdet/rtmdet_l_300e_coco_wo_nms.zip) | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/rtmdet/rtmdet_l_300e_coco_w_nms.onnx) &#124; [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/rtmdet/rtmdet_l_300e_coco_wo_nms.onnx) |
| RTMDet-x | 640 | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/rtmdet/rtmdet_x_300e_coco_w_nms.zip) &#124; [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/rtmdet/rtmdet_x_300e_coco_wo_nms.zip) | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/rtmdet/rtmdet_x_300e_coco_w_nms.onnx) &#124; [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/rtmdet/rtmdet_x_300e_coco_wo_nms.onnx) |
</details>
### **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](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/ppyoloe),[YOLOX](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/yolox),[YOLOv5](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/yolov5),[YOLOv6](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/yolov6),[YOLOv7](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/yolov7)，**其中YOLOv5,YOLOv6,YOLOv7评估并未采用`multi_label`形式**。
- 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Tesla T4 GPU，batch size=1**，测试环境为**paddlepaddle-2.3.2**, **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FLOPs(G)和Params(M)**，首先安装[PaddleSlim](https://github.com/PaddlePaddle/PaddleSlim), `pip install paddleslim`，然后设置[runtime.yml](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/runtime.yml)里`print_flops: True`和`print_params: 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)。
### [VOC](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/voc)
<details>
<summary> 基础模型 </summary>
| 网络模型 | 输入尺寸 | 图片数/GPU | 学习率策略 | TRT-FP16-Latency(ms) | mAP(0.50,11point) | Params(M) | FLOPs(G) | 下载链接 | 配置文件 |
| :-----------: | :-------: | :-------: | :------: | :------------: | :---------------: | :------------------: |:-----------------: | :------: | :------: |
| YOLOv5-s | 640 | 16 | 60e | 3.2 | 80.3 | 7.24 | 16.54 | [下载链接](https://paddledet.bj.bcebos.com/models/yolov5_s_60e_voc.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/voc/yolov5_s_60e_voc.yml) |
| YOLOv7-tiny | 640 | 32 | 60e | 2.6 | 80.2 | 6.23 | 6.90 | [下载链接](https://paddledet.bj.bcebos.com/models/yolov7_tiny_60e_voc.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/voc/yolov7_tiny_60e_voc.yml) |
| YOLOX-s | 640 | 8 | 40e | 3.0 | 82.9 | 9.0 | 26.8 | [下载链接](https://paddledet.bj.bcebos.com/models/yolox_s_40e_voc.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/voc/yolox_s_40e_voc.yml) |
| PP-YOLOE+_s | 640 | 8 | 30e | 2.9 | 86.7 | 7.93 | 17.36 | [下载链接](https://paddledet.bj.bcebos.com/models/ppyoloe_plus_crn_s_30e_voc.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/voc/ppyoloe_plus_crn_s_30e_voc.yml) |
</details>
**注意:**
- VOC数据集训练的mAP为`mAP(IoU=0.5)`的结果，且评估未使用`multi_label`等trick；
- 所有YOLO VOC模型均加载各自模型的COCO权重作为预训练，各个配置文件的配置均为默认使用8卡GPU，可作为自定义数据集设置参考，具体精度会因数据集而异；
- YOLO检测模型建议**总`batch_size`至少大于`64`**去训练，如果资源不够请**换小模型**或**减小模型的输入尺度**，为了保障较高检测精度，**尽量不要尝试单卡训和总`batch_size`小于`64`训**；
- Params(M)和FLOPs(G)均为训练时所测，YOLOv7没有s模型，故选用tiny模型；
- TRT-FP16-Latency(ms)测速相关请查看各YOLO模型的config的主页；
## 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**
将以下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。
```bash
model_name=ppyoloe # 可修改，如 yolov7
job_name=ppyoloe_plus_crn_l_300e_coco # 可修改，如 yolov7_tiny_300e_coco
config=configs/${model_name}/${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 python tools/train.py -c ${config} --eval --amp
python -m paddle.distributed.launch --log_dir=${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 python tools/eval.py -c ${config} -o weights=${weights} --classwise
# 3.直接预测
CUDA_VISIBLE_DEVICES=0 python 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 python tools/export_model.py -c ${config} -o weights=${weights} # exclude_nms=True trt=True
# 5.部署预测
CUDA_VISIBLE_DEVICES=0 python deploy/python/infer.py --model_dir=output_inference/${job_name} --image_file=demo/000000014439_640x640.jpg --device=GPU
# 6.部署测速，加 “--run_mode=trt_fp16” 表示在TensorRT FP16模式下测速
CUDA_VISIBLE_DEVICES=0 python deploy/python/infer.py --model_dir=output_inference/${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
```
- 如果想切换模型，只要修改开头两行即可，如:
```
model_name=yolov7
job_name=yolov7_l_300e_coco
```
- 导出**onnx**，首先安装[Paddle2ONNX](https://github.com/PaddlePaddle/Paddle2ONNX)，`pip install paddle2onnx`；
- **统计FLOPs(G)和Params(M)**，首先安装[PaddleSlim](https://github.com/PaddlePaddle/PaddleSlim)，`pip install paddleslim`，然后设置[runtime.yml](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/runtime.yml)里`print_flops: True`和`print_params: 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 python 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：
python -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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[简体中文](PaddleYOLO_MODEL.md) | English
# [**PaddleYOLO**](https://github.com/PaddlePaddle/PaddleYOLO)
## Introduction
- [**PaddleYOLO**](#paddleyolo)
- [Introduction](#introduction)
- [Introduction](#introduction-1)
- [Updates](#updates)
- [ModelZoo](#modelzoo)
- [PP-YOLOE](#pp-yoloe)
- [YOLOX](#yolox)
- [YOLOv5](#yolov5)
- [YOLOv6](#yolov6)
- [YOLOv7](#yolov7)
- [YOLOv8](#yolov8)
- [RTMDet](#rtmdet)
- [**Notes：**](#notes)
- [VOC](#voc)
- [UserGuide](#userguide)
- [**Pipeline**](#pipeline)
- [CustomDataset](#customdataset)
- [preparation：](#preparation)
- [fintune：](#fintune)
- [Predict and export:](#predict-and-export)
## Introduction
**PaddleYOLO** is a YOLO Series toolbox based on [PaddleDetection](https://github.com/PaddlePaddle/PaddleDetection), **only relevant codes of YOLO series models are included**. It supports `YOLOv3`,`PP-YOLO`,`PP-YOLOv2`,`PP-YOLOE`,`PP-YOLOE+`,`YOLOX`,`YOLOv5`,`YOLOv6`,`YOLOv7`,`YOLOv8`,`RTMDet` and so on. Welcome to use and build it together!
## Updates
* 【2023/01/10】Support [YOLOv8](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/yolov8) inference and deploy;
* 【2022/09/29】Support [RTMDet](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/rtmdet) inference and deploy;
* 【2022/09/26】Release [`PaddleYOLO`](https://github.com/PaddlePaddle/PaddleYOLO);
* 【2022/09/19】Support the new version of [`YOLOv6`](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/yolov6), including n/t/s/m/l model;
* 【2022/08/23】Release `YOLOSeries` codebase: support `YOLOv3`,`PP-YOLOE`,`PP-YOLOE+`,`YOLOX`,`YOLOv5`,`YOLOv6` and `YOLOv7`; support using `ConvNeXt` backbone to get high-precision version of `PP-YOLOE`,`YOLOX` and `YOLOv5`; support PaddleSlim accelerated quantitative training `PP-YOLOE`,`YOLOv5`,`YOLOv6` and `YOLOv7`. For details, please read this [article](https://mp.weixin.qq.com/s/Hki01Zs2lQgvLSLWS0btrA)；
**Notes：**
- The Licence of **PaddleYOLO** is **GPL 3.0**, the codes of [YOLOv5](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/yolov5),[YOLOv6](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/yolov6),[YOLOv7](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/yolov7) and [YOLOv8](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/yolov8) will not be merged into [PaddleDetection](https://github.com/PaddlePaddle/PaddleDetection). Except for these three YOLO models, other YOLO models are recommended to use in [PaddleDetection](https://github.com/PaddlePaddle/PaddleDetection), **which will be the first to release the latest progress of PP-YOLO series detection model**;
- To use **PaddleYOLO**, **PaddlePaddle-2.3.2 or above is recommended**，please refer to the [official website](https://www.paddlepaddle.org.cn/install/quick?docurl=/documentation/docs/zh/install/pip/linux-pip.html) to download the appropriate version. **For Windows platforms, please install the paddle develop version**;
- Training **Custom dataset** please refer to [doc](#CustomDataset) and [issue](https://github.com/PaddlePaddle/PaddleYOLO/issues/43). Please **ensure COCO trained weights are loaded as pre-train** at first. We recommend to use YOLO detection model **with a total `batch_size` at least greater than `64` to train**. If the resources are insufficient, please **use the smaller model** or **reduce the input size of the model**. To ensure high detection accuracy, **you'd better never try to using single GPU or total `batch_size` less than `32` for training**;
## ModelZoo
### [PP-YOLOE](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/ppyoloe)
<details>
<summary> Baseline </summary>
| Model | Input Size | images/GPU | Epoch | TRT-FP16-Latency(ms) | mAP<sup>val<br>0.5:0.95 | mAP<sup>val<br>0.5 | Params(M) | FLOPs(G) | download | config |
| :------------- | :------- | :-------: | :------: | :------------: | :---------------------: | :----------------: |:---------: | :------: |:---------------: |:-----: |
| 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](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/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](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/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](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/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](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/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](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/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](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/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](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/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](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/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](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/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](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/ppyoloe/ppyoloe_plus_crn_x_80e_coco.yml) |
</details>
<details>
<summary> Deploy Models </summary>
| Model | Input Size | Exported weights(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) |
</details>
### [YOLOX](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/yolox)
<details>
<summary> Baseline </summary>
| Model | Input Size | images/GPU | Epoch | TRT-FP16-Latency(ms) | mAP<sup>val<br>0.5:0.95 | mAP<sup>val<br>0.5 | Params(M) | FLOPs(G) | download | config |
| :------------- | :------- | :-------: | :------: | :------------: | :---------------------: | :----------------: |:---------: | :------: |:---------------: |:-----: |
| 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](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/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](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/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](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/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](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/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](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/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](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/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](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/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](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/convnext/yolox_convnext_s_36e_coco.yml) |
</details>
<details>
<summary> Deploy Models </summary>
| Model | Input Size | Exported weights(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) |
</details>
### [YOLOv5](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/yolov5)
<details>
<summary> Baseline </summary>
| Model | Input Size | images/GPU | Epoch | TRT-FP16-Latency(ms) | mAP<sup>val<br>0.5:0.95 | mAP<sup>val<br>0.5 | Params(M) | FLOPs(G) | download | config |
| :------------- | :------- | :-------: | :------: | :------------: | :---------------------: | :----------------: |:---------: | :------: |:---------------: |:-----: |
| 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/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/yolov5/yolov5_n_300e_coco.yml) |
| YOLOv5-s | 640 | 16 | 300e | 3.2 | 37.6 | 56.7 | 7.24 | 16.54 | [model](https://paddledet.bj.bcebos.com/models/yolov5_s_300e_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/yolov5/yolov5_s_300e_coco.yml) |
| YOLOv5-m | 640 | 16 | 300e | 5.2 | 45.4 | 64.1 | 21.19 | 49.08 | [model](https://paddledet.bj.bcebos.com/models/yolov5_m_300e_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/yolov5/yolov5_m_300e_coco.yml) |
| YOLOv5-l | 640 | 16 | 300e | 7.9 | 48.9 | 67.1 | 46.56 | 109.32 | [model](https://paddledet.bj.bcebos.com/models/yolov5_l_300e_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/yolov5/yolov5_l_300e_coco.yml) |
| YOLOv5-x | 640 | 16 | 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/PaddlePaddle/PaddleYOLO/tree/release/2.5/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/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/yolov5/yolov5_convnext_s_36e_coco.yml) |
| *YOLOv5p6-n | 1280 | 16 | 300e | - | 35.9 | 54.2 | 3.25 | 9.23 | [model](https://paddledet.bj.bcebos.com/models/yolov5p6_n_300e_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/yolov5/yolov5p6_n_300e_coco.yml) |
| *YOLOv5p6-s | 1280 | 16 | 300e | - | 44.5 | 63.3 | 12.63 | 33.81 | [model](https://paddledet.bj.bcebos.com/models/yolov5p6_s_300e_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/yolov5/yolov5p6_s_300e_coco.yml) |
| *YOLOv5p6-m | 1280 | 16 | 300e | - | 51.1 | 69.0 | 35.73 | 100.21 | [model](https://paddledet.bj.bcebos.com/models/yolov5p6_m_300e_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/yolov5/yolov5p6_m_300e_coco.yml) |
| *YOLOv5p6-l | 1280 | 8 | 300e | - | 53.4 | 71.0 | 76.77 | 223.09 | [model](https://paddledet.bj.bcebos.com/models/yolov5p6_l_300e_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/yolov5/yolov5p6_l_300e_coco.yml) |
| *YOLOv5p6-x | 1280 | 8 | 300e | - | 54.7 | 72.4 | 140.80 | 420.03 | [model](https://paddledet.bj.bcebos.com/models/yolov5p6_x_300e_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/yolov5/yolov5p6_x_300e_coco.yml) |
</details>
<details>
<summary> Deploy Models </summary>
| Model | Input Size | Exported weights(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) |
</details>
### [YOLOv6](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/yolov6)
<details>
<summary> Baseline </summary>
| Model | Input Size | images/GPU | Epoch | TRT-FP16-Latency(ms) | mAP<sup>val<br>0.5:0.95 | mAP<sup>val<br>0.5 | Params(M) | FLOPs(G) | download | config |
| :------------- | :------- | :-------: | :------: | :---------: | :-----: |:-----: | :-----: |:-----: | :-------------: | :-----: |
| *YOLOv6-n | 640 | 16 | 300e(+300e) | 2.0 | 37.5 | 53.1 | 5.07 | 12.49 |[model](https://paddledet.bj.bcebos.com/models/yolov6_n_300e_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/yolov6/yolov6_n_300e_coco.yml) |
| *YOLOv6-s | 640 | 32 | 300e(+300e) | 2.7 | 44.8 | 61.7 | 20.18 | 49.36 |[model](https://paddledet.bj.bcebos.com/models/yolov6_s_300e_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/yolov6/yolov6_s_300e_coco.yml) |
| *YOLOv6-m | 640 | 32 | 300e(+300e) | - | 49.5 | 66.9 | 37.74 | 92.47 |[model](https://paddledet.bj.bcebos.com/models/yolov6_m_300e_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/yolov6/yolov6_m_300e_coco.yml) |
| *YOLOv6-l(silu) | 640 | 32 | 300e(+300e) | - | 52.2 | 70.2 | 59.66 | 149.4 |[model](https://paddledet.bj.bcebos.com/models/yolov6_l_300e_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/yolov6/yolov6_l_300e_coco.yml) |
</details>
<details>
<summary> Deploy Models </summary>
| Model | Input Size | Exported weights(w/o NMS) | ONNX(w/o NMS) |
| :-------- | :--------: | :---------------------: | :----------------: |
| yolov6-n | 640 | [(w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov6/yolov6_n_300e_coco_w_nms.zip) &#124; [(w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov6/yolov6_n_300e_coco_wo_nms.zip) | [(w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov6/yolov6_n_300e_coco_w_nms.onnx) &#124; [(w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov6/yolov6_n_300e_coco_wo_nms.onnx) |
| yolov6-s | 640 | [(w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov6/yolov6_s_300e_coco_w_nms.zip) &#124; [(w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov6/yolov6_s_300e_coco_wo_nms.zip) | [(w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov6/yolov6_s_300e_coco_w_nms.onnx) &#124; [(w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov6/yolov6_s_300e_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(silu) | 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) |
</details>
### [YOLOv7](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/yolov7)
<details>
<summary> Baseline </summary>
| Model | Input Size | images/GPU | Epoch | TRT-FP16-Latency(ms) | mAP<sup>val<br>0.5:0.95 | mAP<sup>val<br>0.5 | Params(M) | FLOPs(G) | download | config |
| :------------- | :------- | :-------: | :------: | :------------: | :---------------------: | :----------------: |:---------: | :------: |:---------------: |:-----: |
| 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/PaddlePaddle/PaddleYOLO/tree/release/2.5/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/PaddlePaddle/PaddleYOLO/tree/release/2.5/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/PaddlePaddle/PaddleYOLO/tree/release/2.5/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/PaddlePaddle/PaddleYOLO/tree/release/2.5/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/PaddlePaddle/PaddleYOLO/tree/release/2.5/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/PaddlePaddle/PaddleYOLO/tree/release/2.5/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/PaddlePaddle/PaddleYOLO/tree/release/2.5/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/PaddlePaddle/PaddleYOLO/tree/release/2.5/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/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/yolov7/yolov7_tiny_320_300e_coco.yml) |
</details>
<details>
<summary> Deploy Models </summary>
| Model | Input Size | Exported weights(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) |
</details>
### [YOLOv8](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/yolov8)
<details>
<summary> Baseline </summary>
| Model | Input Size | images/GPU | Epoch | TRT-FP16-Latency(ms) | mAP<sup>val<br>0.5:0.95 | mAP<sup>val<br>0.5 | Params(M) | FLOPs(G) | download | config |
| :------------- | :------- | :-------: | :------: | :------------: | :---------------------: | :----------------: |:---------: | :------: |:---------------: |:-----: |
| *YOLOv8-n | 640 | 16 | 500e | 2.4 | 37.3 | 53.0 | 3.16 | 8.7 | [model](https://paddledet.bj.bcebos.com/models/yolov8_n_300e_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/yolov8/yolov8_n_300e_coco.yml) |
| *YOLOv8-s | 640 | 16 | 500e | 3.4 | 44.9 | 61.8 | 11.17 | 28.6 | [model](https://paddledet.bj.bcebos.com/models/yolov8_s_300e_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/yolov8/yolov8_s_300e_coco.yml) |
| *YOLOv8-m | 640 | 16 | 500e | 6.5 | 50.2 | 67.3 | 25.90 | 78.9 | [model](https://paddledet.bj.bcebos.com/models/yolov8_m_300e_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/yolov8/yolov8_m_300e_coco.yml) |
| *YOLOv8-l | 640 | 16 | 500e | 10.0 | 52.8 | 69.6 | 43.69 | 165.2 | [model](https://paddledet.bj.bcebos.com/models/yolov8_l_300e_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/yolov8/yolov8_l_300e_coco.yml) |
| *YOLOv8-x | 640 | 16 | 500e | 15.1 | 53.8 | 70.6 | 68.23 | 257.8 | [model](https://paddledet.bj.bcebos.com/models/yolov8_x_300e_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/yolov8/yolov8_x_300e_coco.yml) |
| *YOLOv8-P6-x | 1280 | 16 | 500e | 55.0 | - | - | 97.42 | 522.93 | [model](https://paddledet.bj.bcebos.com/models/yolov8p6_x_500e_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/yolov8/yolov8p6_x_500e_coco.yml) |
</details>
<details>
<summary> Deploy Models </summary>
| Model | Input Size | Exported weights(w/o NMS) | ONNX(w/o NMS) |
| :-------- | :--------: | :---------------------: | :----------------: |
| YOLOv8-n | 640 | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov8/yolov8_n_500e_coco_w_nms.zip) &#124; [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov8/yolov8_n_500e_coco_wo_nms.zip) | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov8/yolov8_n_500e_coco_w_nms.onnx) &#124; [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov8/yolov8_n_500e_coco_wo_nms.onnx) |
| YOLOv8-s | 640 | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov8/yolov8_s_500e_coco_w_nms.zip) &#124; [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov8/yolov8_s_500e_coco_wo_nms.zip) | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov8/yolov8_s_500e_coco_w_nms.onnx) &#124; [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov8/yolov8_s_500e_coco_wo_nms.onnx) |
| YOLOv8-m | 640 | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov8/yolov8_m_500e_coco_w_nms.zip) &#124; [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov8/yolov8_m_500e_coco_wo_nms.zip) | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov8/yolov8_m_500e_coco_w_nms.onnx) &#124; [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov8/yolov8_m_500e_coco_wo_nms.onnx) |
| YOLOv8-l | 640 | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov8/yolov8_l_500e_coco_w_nms.zip) &#124; [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov8/yolov8_l_500e_coco_wo_nms.zip) | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov8/yolov8_l_500e_coco_w_nms.onnx) &#124; [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov8/yolov8_l_500e_coco_wo_nms.onnx) |
| YOLOv8-x | 640 | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov8/yolov8_x_500e_coco_w_nms.zip) &#124; [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov8/yolov8_x_500e_coco_wo_nms.zip) | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov8/yolov8_x_500e_coco_w_nms.onnx) &#124; [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov8/yolov8_x_500e_coco_wo_nms.onnx) |
</details>
### [RTMDet](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/rtmdet)
<details>
<summary> Baseline </summary>
| Model | Input Size | images/GPU | Epoch | TRT-FP16-Latency(ms) | mAP<sup>val<br>0.5:0.95 | mAP<sup>val<br>0.5 | Params(M) | FLOPs(G) | download | config |
| :------------- | :------- | :-------: | :------: | :------------: | :---------------------: | :----------------: |:---------: | :------: |:---------------: |:-----: |
| *RTMDet-t | 640 | 32 | 300e | 2.8 | 40.9 | 57.9 | 4.90 | 16.21 |[model](https://paddledet.bj.bcebos.com/models/rtmdet_t_300e_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/rtmdet/rtmdet_t_300e_coco.yml) |
| *RTMDet-s | 640 | 32 | 300e | 3.3 | 44.5 | 62.0 | 8.89 | 29.71 |[model](https://paddledet.bj.bcebos.com/models/rtmdet_s_300e_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/rtmdet/rtmdet_s_300e_coco.yml) |
| *RTMDet-m | 640 | 32 | 300e | 6.4 | 49.1 | 66.8 | 24.71 | 78.47 |[model](https://paddledet.bj.bcebos.com/models/rtmdet_m_300e_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/rtmdet/rtmdet_m_300e_coco.yml) |
| *RTMDet-l | 640 | 32 | 300e | 10.2 | 51.2 | 68.8 | 52.31 | 160.32 |[model](https://paddledet.bj.bcebos.com/models/rtmdet_l_300e_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/rtmdet/rtmdet_l_300e_coco.yml) |
| *RTMDet-x | 640 | 32 | 300e | 18.0 | 52.6 | 70.4 | 94.86 | 283.12 |[model](https://paddledet.bj.bcebos.com/models/rtmdet_x_300e_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/rtmdet/rtmdet_x_300e_coco.yml) |
</details>
<details>
<summary> Deploy Models </summary>
| Model | Input Size | Exported weights(w/o NMS) | ONNX(w/o NMS) |
| :-------- | :--------: | :---------------------: | :----------------: |
| RTMDet-t | 640 | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/rtmdet/rtmdet_t_300e_coco_w_nms.zip) &#124; [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/rtmdet/rtmdet_t_300e_coco_wo_nms.zip) | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/rtmdet/rtmdet_t_300e_coco_w_nms.onnx) &#124; [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/rtmdet/rtmdet_t_300e_coco_wo_nms.onnx) |
| RTMDet-s | 640 | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/rtmdet/rtmdet_s_300e_coco_w_nms.zip) &#124; [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/rtmdet/rtmdet_s_300e_coco_wo_nms.zip) | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/rtmdet/rtmdet_s_300e_coco_w_nms.onnx) &#124; [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/rtmdet/rtmdet_s_300e_coco_wo_nms.onnx) |
| RTMDet-m | 640 | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/rtmdet/rtmdet_m_300e_coco_w_nms.zip) &#124; [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/rtmdet/rtmdet_m_300e_coco_wo_nms.zip) | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/rtmdet/rtmdet_m_300e_coco_w_nms.onnx) &#124; [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/rtmdet/rtmdet_m_300e_coco_wo_nms.onnx) |
| RTMDet-l | 640 | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/rtmdet/rtmdet_l_300e_coco_w_nms.zip) &#124; [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/rtmdet/rtmdet_l_300e_coco_wo_nms.zip) | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/rtmdet/rtmdet_l_300e_coco_w_nms.onnx) &#124; [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/rtmdet/rtmdet_l_300e_coco_wo_nms.onnx) |
| RTMDet-x | 640 | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/rtmdet/rtmdet_x_300e_coco_w_nms.zip) &#124; [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/rtmdet/rtmdet_x_300e_coco_wo_nms.zip) | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/rtmdet/rtmdet_x_300e_coco_w_nms.onnx) &#124; [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/rtmdet/rtmdet_x_300e_coco_wo_nms.onnx) |
</details>
### **Notes：**
- All the models are trained on COCO train2017 dataset and evaluated on val2017 dataset. The * in front of the model indicates that the training is being updated.
- Please check the specific accuracy and speed details in [PP-YOLOE](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/ppyoloe),[YOLOX](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/yolox),[YOLOv5](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/yolov5),[YOLOv6](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/yolov6),[YOLOv7](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/yolov7). **Note that YOLOv5, YOLOv6 and YOLOv7 have not adopted `multi_label` to eval**.
- TRT-FP16-Latency(ms) is the time spent in testing under TensorRT-FP16, **excluding data preprocessing and model output post-processing (NMS)**. The test adopts single card **Tesla T4 GPU, batch size=1**, and the test environment is **paddlepaddle-2.3.2**, **CUDA 11.2**, **CUDNN 8.2**, **GCC-8.2**, **TensorRT 8.0.3.4**. Please refer to the respective model homepage for details.
- For **FLOPs(G) and Params(M)**, you should first install [PaddleSlim](https://github.com/PaddlePaddle/PaddleSlim), `pip install paddleslim`, then set `print_flops: True` and `print_params: True` in [runtime.yml](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/runtime.yml). Make sure **single scale** like 640x640, **MACs are printed，FLOPs=2*MACs**.
- Based on [PaddleSlim](https://github.com/PaddlePaddle/PaddleSlim), quantitative training of YOLO series models can achieve basically lossless accuracy and generally improve the speed by more than 30%. For details, please refer to [auto_compression](https://github.com/PaddlePaddle/PaddleSlim/tree/develop/example/auto_compression).
### [VOC](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/voc)
<details>
<summary> Baseline </summary>
| Model | Input Size | images/GPU | Epoch | TRT-FP16-Latency(ms) | mAP(0.50,11point) | Params(M) | FLOPs(G) | download | config |
| :-----------: | :-------: | :-------: | :------: | :------------: | :---------------: | :------------------: |:-----------------: | :------: | :------: |
| YOLOv5-s | 640 | 16 | 60e | 3.2 | 80.3 | 7.24 | 16.54 | [model](https://paddledet.bj.bcebos.com/models/yolov5_s_60e_voc.pdparams) | [config](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/voc/yolov5_s_60e_voc.yml) |
| YOLOv7-tiny | 640 | 32 | 60e | 2.6 | 80.2 | 6.23 | 6.90 | [model](https://paddledet.bj.bcebos.com/models/yolov7_tiny_60e_voc.pdparams) | [config](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/voc/yolov7_tiny_60e_voc.yml) |
| YOLOX-s | 640 | 8 | 40e | 3.0 | 82.9 | 9.0 | 26.8 | [model](https://paddledet.bj.bcebos.com/models/yolox_s_40e_voc.pdparams) | [config](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/voc/yolox_s_40e_voc.yml) |
| PP-YOLOE+_s | 640 | 8 | 30e | 2.9 | 86.7 | 7.93 | 17.36 | [model](https://paddledet.bj.bcebos.com/models/ppyoloe_plus_crn_s_30e_voc.pdparams) | [config](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/voc/ppyoloe_plus_crn_s_30e_voc.yml) |
</details>
**Note:**
- The VOC mAP is `mAP(IoU=0.5)`, and all the models **have not adopted `multi_label` to eval**.
- All YOLO VOC models are loaded with the COCO weights of their respective models as pre-train weights. Each config file uses 8 GPUs by default, which can be used as a reference for setting custom datasets. The specific mAP will vary depending on the datasets;
- We recommend to use YOLO detection model **with a total `batch_size` at least greater than `64` to train**. If the resources are insufficient, please **use the smaller model** or **reduce the input size of the model**. To ensure high detection accuracy, **you'd better not try to using single GPU or total `batch_size` less than `64` for training**;
- Params (M) and FLOPs (G) are measured during training. YOLOv7 has no s model, so tiny model is selected;
- For TRT-FP16 Latency (ms) speed measurement, please refer to the config homepage of each YOLO model;
## UserGuide
Download MS-COCO dataset, [official website](https://cocodataset.org). The download links are: [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).
The download link provided by PaddleDetection team is: [coco](https://bj.bcebos.com/v1/paddledet/data/coco.tar)(about 22G) and [test2017](https://bj.bcebos.com/v1/paddledet/data/cocotest2017.zip). Note that test2017 is optional, and the evaluation is based on val2017.
### **Pipeline**
Write the following commands in a script file, such as ```run.sh```, and run as：```sh run.sh```. You can also run the command line sentence by sentence.
```bash
model_name=ppyoloe # yolov7
job_name=ppyoloe_plus_crn_l_80e_coco # yolov7_tiny_300e_coco
config=configs/${model_name}/${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.training（single GPU / multi GPU）
# CUDA_VISIBLE_DEVICES=0 python tools/train.py -c ${config} --eval --amp
python -m paddle.distributed.launch --log_dir=${log_dir} --gpus 0,1,2,3,4,5,6,7 tools/train.py -c ${config} --eval --amp
# 2.eval
CUDA_VISIBLE_DEVICES=0 python tools/eval.py -c ${config} -o weights=${weights} --classwise
# 3.infer
CUDA_VISIBLE_DEVICES=0 python tools/infer.py -c ${config} -o weights=${weights} --infer_img=demo/000000014439_640x640.jpg --draw_threshold=0.5
# 4.export
CUDA_VISIBLE_DEVICES=0 python tools/export_model.py -c ${config} -o weights=${weights} # exclude_nms=True trt=True
# 5.deploy infer
CUDA_VISIBLE_DEVICES=0 python deploy/python/infer.py --model_dir=output_inference/${job_name} --image_file=demo/000000014439_640x640.jpg --device=GPU
# 6.deploy speed, add '--run_mode=trt_fp16' to test in TensorRT FP16 mode
CUDA_VISIBLE_DEVICES=0 python deploy/python/infer.py --model_dir=output_inference/${job_name} --image_file=demo/000000014439_640x640.jpg --device=GPU --run_benchmark=True # --run_mode=trt_fp16
# 7.export onnx
paddle2onnx --model_dir output_inference/${job_name} --model_filename model.pdmodel --params_filename model.pdiparams --opset_version 12 --save_file ${job_name}.onnx
# 8.onnx speed
/usr/local/TensorRT-8.0.3.4/bin/trtexec --onnx=${job_name}.onnx --workspace=4096 --avgRuns=10 --shapes=input:1x3x640x640 --fp16
```
**Note：**
- If you want to switch models, just modify the first two lines, such as:
```
model_name=yolov7
job_name=yolov7_tiny_300e_coco
```
- For **exporting onnx**, you should install [Paddle2ONNX](https://github.com/PaddlePaddle/Paddle2ONNX) by `pip install paddle2onnx` at first.
- For **FLOPs(G) and Params(M)**, you should install [PaddleSlim](https://github.com/PaddlePaddle/PaddleSlim) by `pip install paddleslim` at first, then set `print_flops: True` and `print_params: True` in [runtime.yml](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/runtime.yml). Make sure **single scale** like 640x640, **MACs are printed，FLOPs=2*MACs**.
### CustomDataset
#### preparation：
1.For the annotation of custom dataset, please refer to[DetAnnoTools](../tutorials/data/DetAnnoTools.md);
2.For training preparation of custom dataset，please refer to[PrepareDataSet](../tutorials/PrepareDataSet.md).
#### fintune：
In addition to changing the path of the dataset, it is generally recommended to load **the COCO pre training weight of the corresponding model** to fintune, which will converge faster and achieve higher accuracy, such as：
```base
# fintune with single GPU：
# CUDA_VISIBLE_DEVICES=0 python tools/train.py -c ${config} --eval --amp -o pretrain_weights=https://paddledet.bj.bcebos.com/models/ppyoloe_crn_l_300e_coco.pdparams
# fintune with multi GPU：
python -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
```
**Note:**
- The fintune training will show that the channels of the last layer of the head classification branch is not matched, which is a normal situation, because the number of custom dataset is generally inconsistent with that of COCO dataset;
- In general, the number of epochs for fintune training can be set less, and the lr setting is also smaller, such as 1/10. The highest accuracy may occur in one of the middle epochs;
#### Predict and export:
When using custom dataset to predict and export models, if the path of the TestDataset dataset is set incorrectly, COCO 80 categories will be used by default.
In addition to the correct path setting of the TestDataset dataset, you can also modify and add the corresponding `label_list`. Txt file (one category is recorded in one line), and `anno_path` in TestDataset can also be set as an absolute path, such as:
```
TestDataset:
!ImageFolder
anno_path: label_list.txt # if not set dataset_dir, the anno_path will be relative path of PaddleDetection root directory
# dataset_dir: dataset/my_coco # if set dataset_dir, the anno_path will be dataset_dir/anno_path
```
one line in `label_list.txt` records a corresponding category：
```
person
vehicle
```

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简体中文 | [English](SSLD_PRETRAINED_MODEL_en.md)
### Simple semi-supervised label knowledge distillation solution (SSLD)
### R-CNN on COCO
| 骨架网络 | 网络类型 | 每张GPU图片个数 | 学习率策略 |推理时间(fps) | Box AP | Mask AP | 下载 | 配置文件 |
| :------------------- | :------------| :-----: | :-----: | :------------: | :-----: | :-----: | :-----------------------------------------------------: | :-----: |
| ResNet50-vd-SSLDv2-FPN | Faster | 1 | 1x | ---- | 41.4 | - | [下载链接](https://paddledet.bj.bcebos.com/models/faster_rcnn_r50_vd_fpn_ssld_1x_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/faster_rcnn/faster_rcnn_r50_vd_fpn_ssld_1x_coco.yml) |
| ResNet50-vd-SSLDv2-FPN | Faster | 1 | 2x | ---- | 42.3 | - | [下载链接](https://paddledet.bj.bcebos.com/models/faster_rcnn_r50_vd_fpn_ssld_2x_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/faster_rcnn/faster_rcnn_r50_vd_fpn_ssld_2x_coco.yml) |
| ResNet50-vd-SSLDv2-FPN | Mask | 1 | 1x | ---- | 42.0 | 38.2 | [下载链接](https://paddledet.bj.bcebos.com/models/mask_rcnn_r50_vd_fpn_ssld_1x_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/mask_rcnn/mask_rcnn_r50_vd_fpn_ssld_1x_coco.yml) |
| ResNet50-vd-SSLDv2-FPN | Mask | 1 | 2x | ---- | 42.7 | 38.9 | [下载链接](https://paddledet.bj.bcebos.com/models/mask_rcnn_r50_vd_fpn_ssld_2x_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/mask_rcnn/mask_rcnn_r50_vd_fpn_ssld_2x_coco.yml) |
| ResNet50-vd-SSLDv2-FPN | Cascade Faster | 1 | 1x | ---- | 44.4 | - | [下载链接](https://paddledet.bj.bcebos.com/models/cascade_rcnn_r50_vd_fpn_ssld_1x_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/cascade_rcnn/cascade_rcnn_r50_vd_fpn_ssld_1x_coco.yml) |
| ResNet50-vd-SSLDv2-FPN | Cascade Faster | 1 | 2x | ---- | 45.0 | - | [下载链接](https://paddledet.bj.bcebos.com/models/cascade_rcnn_r50_vd_fpn_ssld_2x_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/cascade_rcnn/cascade_rcnn_r50_vd_fpn_ssld_2x_coco.yml) |
| ResNet50-vd-SSLDv2-FPN | Cascade Mask | 1 | 1x | ---- | 44.9 | 39.1 | [下载链接](https://paddledet.bj.bcebos.com/models/cascade_mask_rcnn_r50_vd_fpn_ssld_1x_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/cascade_rcnn/cascade_mask_rcnn_r50_vd_fpn_ssld_1x_coco.yml) |
| ResNet50-vd-SSLDv2-FPN | Cascade Mask | 1 | 2x | ---- | 45.7 | 39.7 | [下载链接](https://paddledet.bj.bcebos.com/models/cascade_mask_rcnn_r50_vd_fpn_ssld_2x_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/cascade_rcnn/cascade_mask_rcnn_r50_vd_fpn_ssld_2x_coco.yml) |
### YOLOv3 on COCO
| 骨架网络 | 输入尺寸 | 每张GPU图片个数 | 学习率策略 |推理时间(fps) | Box AP | 下载 | 配置文件 |
| :----------------- | :-------- | :-----------: | :------: | :---------: | :----: | :----------------------------------------------------: | :-----: |
| MobileNet-V1-SSLD | 608 | 8 | 270e | ---- | 31.0 | [下载链接](https://paddledet.bj.bcebos.com/models/yolov3_mobilenet_v1_ssld_270e_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/yolov3/yolov3_mobilenet_v1_ssld_270e_coco.yml) |
| MobileNet-V1-SSLD | 416 | 8 | 270e | ---- | 30.6 | [下载链接](https://paddledet.bj.bcebos.com/models/yolov3_mobilenet_v1_ssld_270e_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/yolov3/yolov3_mobilenet_v1_ssld_270e_coco.yml) |
| MobileNet-V1-SSLD | 320 | 8 | 270e | ---- | 28.4 | [下载链接](https://paddledet.bj.bcebos.com/models/yolov3_mobilenet_v1_ssld_270e_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/yolov3/yolov3_mobilenet_v1_ssld_270e_coco.yml) |
### YOLOv3 on Pasacl VOC
| 骨架网络 | 输入尺寸 | 每张GPU图片个数 | 学习率策略 |推理时间(fps) | Box AP | 下载 | 配置文件 |
| :----------------- | :-------- | :-----------: | :------: | :---------: | :----: | :----------------------------------------------------: | :-----: |
| MobileNet-V1-SSLD | 608 | 8 | 270e | - | 78.3 | [下载链接](https://paddledet.bj.bcebos.com/models/yolov3_mobilenet_v1_ssld_270e_voc.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/yolov3/yolov3_mobilenet_v1_ssld_270e_voc.yml) |
| MobileNet-V1-SSLD | 416 | 8 | 270e | - | 79.6 | [下载链接](https://paddledet.bj.bcebos.com/models/yolov3_mobilenet_v1_ssld_270e_voc.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/yolov3/yolov3_mobilenet_v1_ssld_270e_voc.yml) |
| MobileNet-V1-SSLD | 320 | 8 | 270e | - | 77.3 | [下载链接](https://paddledet.bj.bcebos.com/models/yolov3_mobilenet_v1_ssld_270e_voc.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/yolov3/yolov3_mobilenet_v1_ssld_270e_voc.yml) |
| MobileNet-V3-SSLD | 608 | 8 | 270e | - | 80.4 | [下载链接](https://paddledet.bj.bcebos.com/models/yolov3_mobilenet_v3_large_ssld_270e_voc.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/yolov3/yolov3_mobilenet_v3_large_ssld_270e_voc.yml) |
| MobileNet-V3-SSLD | 416 | 8 | 270e | - | 79.2 | [下载链接](https://paddledet.bj.bcebos.com/models/yolov3_mobilenet_v3_large_ssld_270e_voc.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/yolov3/yolov3_mobilenet_v3_large_ssld_270e_voc.yml) |
| MobileNet-V3-SSLD | 320 | 8 | 270e | - | 77.3 | [下载链接](https://paddledet.bj.bcebos.com/models/yolov3_mobilenet_v3_large_ssld_270e_voc.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/yolov3/yolov3_mobilenet_v3_large_ssld_270e_voc.yml) |
**注意事项:**
- [SSLD](https://arxiv.org/abs/2103.05959)是一种知识蒸馏方法，我们使用蒸馏后性能更强的backbone预训练模型，进一步提升检测精度，详细方案请参考[知识蒸馏教程](https://github.com/PaddlePaddle/PaddleClas/blob/develop/docs/en/advanced_tutorials/distillation/distillation_en.md)
![demo image](../images/ssld_model.png)
## Citations
```
@misc{cui2021selfsupervision,
title={Beyond Self-Supervision: A Simple Yet Effective Network Distillation Alternative to Improve Backbones},
author={Cheng Cui and Ruoyu Guo and Yuning Du and Dongliang He and Fu Li and Zewu Wu and Qiwen Liu and Shilei Wen and Jizhou Huang and Xiaoguang Hu and Dianhai Yu and Errui Ding and Yanjun Ma},
year={2021},
eprint={2103.05959},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```

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English | [简体中文](SSLD_PRETRAINED_MODEL.md)
### Simple semi-supervised label knowledge distillation solution (SSLD)
### R-CNN on COCO
| Backbone | Model | Images/GPU | Lr schd | FPS | Box AP | Mask AP | Download | Config |
| :------------------- | :------------| :-----: | :-----: | :------------: | :-----: | :-----: | :-----------------------------------------------------: | :-----: |
| ResNet50-vd-SSLDv2-FPN | Faster | 1 | 1x | ---- | 41.4 | - | [model](https://paddledet.bj.bcebos.com/models/faster_rcnn_r50_vd_fpn_ssld_1x_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/faster_rcnn/faster_rcnn_r50_vd_fpn_ssld_1x_coco.yml) |
| ResNet50-vd-SSLDv2-FPN | Faster | 1 | 2x | ---- | 42.3 | - | [model](https://paddledet.bj.bcebos.com/models/faster_rcnn_r50_vd_fpn_ssld_2x_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/faster_rcnn/faster_rcnn_r50_vd_fpn_ssld_2x_coco.yml) |
| ResNet50-vd-SSLDv2-FPN | Mask | 1 | 1x | ---- | 42.0 | 38.2 | [model](https://paddledet.bj.bcebos.com/models/mask_rcnn_r50_vd_fpn_ssld_1x_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/mask_rcnn/mask_rcnn_r50_vd_fpn_ssld_1x_coco.yml) |
| ResNet50-vd-SSLDv2-FPN | Mask | 1 | 2x | ---- | 42.7 | 38.9 | [model](https://paddledet.bj.bcebos.com/models/mask_rcnn_r50_vd_fpn_ssld_2x_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/mask_rcnn/mask_rcnn_r50_vd_fpn_ssld_2x_coco.yml) |
| ResNet50-vd-SSLDv2-FPN | Cascade Faster | 1 | 1x | ---- | 44.4 | - | [model](https://paddledet.bj.bcebos.com/models/cascade_rcnn_r50_vd_fpn_ssld_1x_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/cascade_rcnn/cascade_rcnn_r50_vd_fpn_ssld_1x_coco.yml) |
| ResNet50-vd-SSLDv2-FPN | Cascade Faster | 1 | 2x | ---- | 45.0 | - | [model](https://paddledet.bj.bcebos.com/models/cascade_rcnn_r50_vd_fpn_ssld_2x_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/cascade_rcnn/cascade_rcnn_r50_vd_fpn_ssld_2x_coco.yml) |
| ResNet50-vd-SSLDv2-FPN | Cascade Mask | 1 | 1x | ---- | 44.9 | 39.1 | [model](https://paddledet.bj.bcebos.com/models/cascade_mask_rcnn_r50_vd_fpn_ssld_1x_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/cascade_rcnn/cascade_mask_rcnn_r50_vd_fpn_ssld_1x_coco.yml) |
| ResNet50-vd-SSLDv2-FPN | Cascade Mask | 1 | 2x | ---- | 45.7 | 39.7 | [model](https://paddledet.bj.bcebos.com/models/cascade_mask_rcnn_r50_vd_fpn_ssld_2x_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/cascade_rcnn/cascade_mask_rcnn_r50_vd_fpn_ssld_2x_coco.yml) |
### YOLOv3 on COCO
| Backbone | Input shape | Images/GPU | Lr schd | FPS | Box AP | Download | Config |
| :----------------- | :-------- | :-----------: | :------: | :---------: | :----: | :----------------------------------------------------: | :-----: |
| MobileNet-V1-SSLD | 608 | 8 | 270e | ---- | 31.0 | [model](https://paddledet.bj.bcebos.com/models/yolov3_mobilenet_v1_ssld_270e_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/yolov3/yolov3_mobilenet_v1_ssld_270e_coco.yml) |
| MobileNet-V1-SSLD | 416 | 8 | 270e | ---- | 30.6 | [model](https://paddledet.bj.bcebos.com/models/yolov3_mobilenet_v1_ssld_270e_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/yolov3/yolov3_mobilenet_v1_ssld_270e_coco.yml) |
| MobileNet-V1-SSLD | 320 | 8 | 270e | ---- | 28.4 | [model](https://paddledet.bj.bcebos.com/models/yolov3_mobilenet_v1_ssld_270e_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/yolov3/yolov3_mobilenet_v1_ssld_270e_coco.yml) |
### YOLOv3 on Pasacl VOC
| Backbone | Input shape | Images/GPU | Lr schd | FPS | Box AP | Download | Config |
| :----------------- | :-------- | :-----------: | :------: | :---------: | :----: | :----------------------------------------------------: | :-----: |
| MobileNet-V1-SSLD | 608 | 8 | 270e | - | 78.3 | [model](https://paddledet.bj.bcebos.com/models/yolov3_mobilenet_v1_ssld_270e_voc.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/yolov3/yolov3_mobilenet_v1_ssld_270e_voc.yml) |
| MobileNet-V1-SSLD | 416 | 8 | 270e | - | 79.6 | [model](https://paddledet.bj.bcebos.com/models/yolov3_mobilenet_v1_ssld_270e_voc.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/yolov3/yolov3_mobilenet_v1_ssld_270e_voc.yml) |
| MobileNet-V1-SSLD | 320 | 8 | 270e | - | 77.3 | [model](https://paddledet.bj.bcebos.com/models/yolov3_mobilenet_v1_ssld_270e_voc.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/yolov3/yolov3_mobilenet_v1_ssld_270e_voc.yml) |
| MobileNet-V3-SSLD | 608 | 8 | 270e | - | 80.4 | [model](https://paddledet.bj.bcebos.com/models/yolov3_mobilenet_v3_large_ssld_270e_voc.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/yolov3/yolov3_mobilenet_v3_large_ssld_270e_voc.yml) |
| MobileNet-V3-SSLD | 416 | 8 | 270e | - | 79.2 | [model](https://paddledet.bj.bcebos.com/models/yolov3_mobilenet_v3_large_ssld_270e_voc.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/yolov3/yolov3_mobilenet_v3_large_ssld_270e_voc.yml) |
| MobileNet-V3-SSLD | 320 | 8 | 270e | - | 77.3 | [model](https://paddledet.bj.bcebos.com/models/yolov3_mobilenet_v3_large_ssld_270e_voc.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/yolov3/yolov3_mobilenet_v3_large_ssld_270e_voc.yml) |
**Notes:**
- [SSLD](https://arxiv.org/abs/2103.05959) is a knowledge distillation method. We use the stronger backbone pretrained model after distillation to further improve the detection accuracy. Please refer to the [knowledge distillation tutorial](https://github.com/PaddlePaddle/PaddleClas/blob/develop/docs/en/advanced_tutorials/distillation/distillation_en.md).
![demo image](../images/ssld_model.png)
## Citations
```
@misc{cui2021selfsupervision,
title={Beyond Self-Supervision: A Simple Yet Effective Network Distillation Alternative to Improve Backbones},
author={Cheng Cui and Ruoyu Guo and Yuning Du and Dongliang He and Fu Li and Zewu Wu and Qiwen Liu and Shilei Wen and Jizhou Huang and Xiaoguang Hu and Dianhai Yu and Errui Ding and Yanjun Ma},
year={2021},
eprint={2103.05959},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```