MODEL_ZOO_en.md

[简体中文](MODEL_ZOO_cn.md) | English

# [**PaddleYOLO**](https://github.com/PaddlePaddle/PaddleYOLO)

<div  align="center">
  <img src="https://user-images.githubusercontent.com/13104100/213197403-c8257486-9ac4-486f-a0d5-4e3fe27ca852.jpg" width="480"/>
  <img src="https://user-images.githubusercontent.com/13104100/213197635-eeb55433-bb2d-44f6-b374-73c616cfab24.jpg" width="480"/>
</div>

## Introduction
- [Introduction](#Introduction)
- [ModelZoo](#ModelZoo)
    - [PP-YOLOE](#PP-YOLOE)
    - [YOLOX](#YOLOX)
    - [YOLOv5](#YOLOv5)
    - [YOLOv6](#YOLOv6)
    - [YOLOv7](#YOLOv7)
    - [YOLOv8](#YOLOv8)
    - [RTMDet](#RTMDet)
    - [VOC](#VOC)
- [UserGuide](#UserGuide)
    - [Pipeline](#Pipeline)
    - [CustomDataset](#CustomDataset)

## 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

* 【2023/03/13】Support [YOLOv5u](configs/yolov5/yolov5u) and [YOLOv7u](configs/yolov7/yolov7u) inference and deploy;
* 【2023/01/10】Support [YOLOv8](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.6/configs/yolov8) inference and deploy;
* 【2022/09/29】Support [RTMDet](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.6/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/2.6/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/2.6/configs/yolov5),[YOLOv6](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.6/configs/yolov6),[YOLOv7](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.6/configs/yolov7) and [YOLOv8](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.6/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/2.6/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/2.6/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/2.6/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/2.6/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/2.6/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/2.6/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/2.6/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/2.6/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/2.6/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/2.6/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/2.6/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/2.6/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/2.6/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/2.6/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/2.6/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/2.6/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/2.6/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/2.6/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/2.6/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/2.6/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/2.6/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    |     1.5    |  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/2.6/configs/yolov5/yolov5_n_300e_coco.yml) |
| YOLOv5-s        |  640     |    16      |   300e    |     2.6    |  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/2.6/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/2.6/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/2.6/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/2.6/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/2.6/configs/yolov5/yolov5_convnext_s_36e_coco.yml) |
| *YOLOv5u-n        |  640     |    16      |   300e   |     1.61    |  34.5  | 49.7 |  2.65  | 7.79 | [model](https://paddledet.bj.bcebos.com/models/yolov5u_n_300e_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.6/configs/yolov5/yolov5u/yolov5u_n_300e_coco.yml) |
| *YOLOv5u-s        |  640     |    16      |   300e   |     2.66    |  43.0  | 59.7 |  9.15   | 24.12 | [model](https://paddledet.bj.bcebos.com/models/yolov5u_s_300e_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.6/configs/yolov5/yolov5u/yolov5u_s_300e_coco.yml) |
| *YOLOv5u-m        |  640     |    16      |   300e   |     5.50    |  49.0  | 65.7 |  25.11  | 64.42 | [model](https://paddledet.bj.bcebos.com/models/yolov5u_m_300e_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.6/configs/yolov5/yolov5u/yolov5u_m_300e_coco.yml) |
| *YOLOv5u-l        |  640     |    16      |   300e   |     8.73    |  52.2  | 69.0 |  53.23  | 135.34 | [model](https://paddledet.bj.bcebos.com/models/yolov5u_l_300e_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.6/configs/yolov5/yolov5u/yolov5u_l_300e_coco.yml) |
| *YOLOv5u-x        |  640     |    16      |   300e   |     15.49   |  53.1  | 69.9 |  97.28  | 246.89 | [model](https://paddledet.bj.bcebos.com/models/yolov5u_x_300e_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.6/configs/yolov5/yolov5u/yolov5u_x_300e_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/2.6/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/2.6/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/2.6/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/2.6/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/2.6/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/2.6/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) |  1.3  |  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/2.6/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/2.6/configs/yolov6/yolov6_s_300e_coco.yml) |
| *YOLOv6-m       |  640     |    32      |   300e(+300e) |  5.3  |  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/2.6/configs/yolov6/yolov6_m_300e_coco.yml) |
| *YOLOv6-l(silu) |  640     |    32      |   300e(+300e) |  9.5  |  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/2.6/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/2.6/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/2.6/configs/yolov7/yolov7_l_300e_coco.yml) |
| *YOLOv7u-L       |  640     |    32      |   300e    |      9.0    |  52.1 | 68.8 |  43.59  | 130.10 |[model](https://paddledet.bj.bcebos.com/models/yolov7u_l_300e_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.6/configs/yolov7/yolov7u/yolov7u_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/2.6/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/2.6/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/2.6/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/2.6/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/2.6/configs/yolov7/yolov7p6_e6e_300e_coco.yml) |
| YOLOv7-tiny     |  640     |    32      |   300e    |     2.4   |  37.3 | 54.5 |  6.23  | 13.80 |[model](https://paddledet.bj.bcebos.com/models/yolov7_tiny_300e_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.6/configs/yolov7/yolov7_tiny_300e_coco.yml) |
| YOLOv7-tiny     |  416     |    32      |   300e    |     1.3    | 33.3 | 49.5 |  6.23  | 5.82 |[model](https://paddledet.bj.bcebos.com/models/yolov7_tiny_416_300e_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.6/configs/yolov7/yolov7_tiny_416_300e_coco.yml) |
| YOLOv7-tiny     |  320     |    32      |   300e    |     -    | 29.1 | 43.8 |  6.23  | 3.46 |[model](https://paddledet.bj.bcebos.com/models/yolov7_tiny_320_300e_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.6/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/2.6/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   |    1.8   |  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/2.6/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/2.6/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/2.6/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/2.6/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/2.6/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/2.6/configs/yolov8/yolov8p6_x_500e_coco.yml) |

</details>

<details>
<summary> Deploy Models  </summary>

| Model   | Input Size | Exported weights(with nms) | Exported weights(exclude_nms)| ONNX(exclude_post_process)  |
| :------ | :--------: | :------------------------: | :--------------------------: | :-------------------------: |
| YOLOv8-n |  640   | [(w_nms)](https://paddledet.bj.bcebos.com/deploy/paddleyolo/yolov8/yolov8_n_500e_coco_w_nms.zip) | [(wo_nms)](https://paddledet.bj.bcebos.com/deploy/paddleyolo/yolov8/yolov8_n_500e_coco_wo_nms.zip) | [(onnx)](https://paddledet.bj.bcebos.com/deploy/paddleyolo/yolov8/yolov8_n_500e_coco.onnx) |
| YOLOv8-s |  640   | [(w_nms)](https://paddledet.bj.bcebos.com/deploy/paddleyolo/yolov8/yolov8_s_500e_coco_w_nms.zip) | [(wo_nms)](https://paddledet.bj.bcebos.com/deploy/paddleyolo/yolov8/yolov8_s_500e_coco_wo_nms.zip) | [(onnx)](https://paddledet.bj.bcebos.com/deploy/paddleyolo/yolov8/yolov8_s_500e_coco.onnx) |
| YOLOv8-m |  640   | [(w_nms)](https://paddledet.bj.bcebos.com/deploy/paddleyolo/yolov8/yolov8_m_500e_coco_w_nms.zip) | [(wo_nms)](https://paddledet.bj.bcebos.com/deploy/paddleyolo/yolov8/yolov8_m_500e_coco_wo_nms.zip) | [(onnx)](https://paddledet.bj.bcebos.com/deploy/paddleyolo/yolov8/yolov8_m_500e_coco.onnx) |
| YOLOv8-l |  640   | [(w_nms)](https://paddledet.bj.bcebos.com/deploy/paddleyolo/yolov8/yolov8_l_500e_coco_w_nms.zip) | [(wo_nms)](https://paddledet.bj.bcebos.com/deploy/paddleyolo/yolov8/yolov8_l_500e_coco_wo_nms.zip) | [(onnx)](https://paddledet.bj.bcebos.com/deploy/paddleyolo/yolov8/yolov8_l_500e_coco.onnx) |
| YOLOv8-x |  640   | [(w_nms)](https://paddledet.bj.bcebos.com/deploy/paddleyolo/yolov8/yolov8_x_500e_coco_w_nms.zip) | [(wo_nms)](https://paddledet.bj.bcebos.com/deploy/paddleyolo/yolov8/yolov8_x_500e_coco_wo_nms.zip) | [(onnx)](https://paddledet.bj.bcebos.com/deploy/paddleyolo/yolov8/yolov8_x_500e_coco.onnx) |

</details>


### [RTMDet](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.6/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/2.6/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/2.6/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/2.6/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/2.6/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/2.6/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/2.6/configs/ppyoloe),[YOLOX](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.6/configs/yolox),[YOLOv5](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.6/configs/yolov5),[YOLOv6](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.6/configs/yolov6),[YOLOv7](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.6/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/2.6/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/2.6/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/2.6/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/2.6/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/2.6/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/2.6/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_s_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
# CUDA_VISIBLE_DEVICES=0 python tools/infer.py -c ${config} -o weights=${weights} --infer_dir=demo/ --draw_threshold=0.5

# 4.export
CUDA_VISIBLE_DEVICES=0 python tools/export_model.py -c ${config} -o weights=${weights} # trt=True

# CUDA_VISIBLE_DEVICES=0 python tools/export_model.py -c ${config} -o weights=${weights} exclude_post_process=True # trt=True

# 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=${job_name}.onnx --workspace=4096 --avgRuns=10 --shapes=input:1x3x640x640 --fp16
/usr/local/TensorRT-8.0.3.4/bin/trtexec --onnx=${job_name}.onnx --workspace=4096 --avgRuns=10 --shapes=input:1x3x640x640 --fp32
```

**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/2.6/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
```