[cherry-pick] add ppyoloe-s 400epoch model (#6340)

* add ppyoloe-s 400epoch, test=document_fix * fix doc typo, test=document_fix

[cherry-pick] add ppyoloe-s 400epoch model (#6340)
* add ppyoloe-s 400epoch, test=document_fix * fix doc typo, test=document_fix
5f4d92de · Feng Ni · GitHub · a38f26e9 · 5f4d92de · 5f4d92de
3 changed file
--- a/configs/ppyoloe/README.md
+++ b/configs/ppyoloe/README.md
@@ -9,7 +9,7 @@ English | [简体中文](README_cn.md)
 - [Appendix](#Appendix)

 ## Introduction
-PP-YOLOE is an excellent single-stage anchor-free model based on PP-YOLOv2, surpassing a variety of popular yolo models. PP-YOLOE has a series of models, named s/m/l/x, which are configured through width multiplier and depth multiplier. PP-YOLOE avoids using special operators, such as deformable convolution or matrix nms, to be deployed friendly on various hardware. For more details, please refer to our [report](https://arxiv.org/abs/2203.16250).
+PP-YOLOE is an excellent single-stage anchor-free model based on PP-YOLOv2, surpassing a variety of popular YOLO models. PP-YOLOE has a series of models, named s/m/l/x, which are configured through width multiplier and depth multiplier. PP-YOLOE avoids using special operators, such as Deformable Convolution or Matrix NMS, to be deployed friendly on various hardware. For more details, please refer to our [report](https://arxiv.org/abs/2203.16250).

 <div align="center">
  <img src="../../docs/images/ppyoloe_map_fps.png" width=500 />
@@ -21,28 +21,31 @@ PP-YOLOE is composed of following methods:
 - Scalable backbone and neck
 - [Task Alignment Learning](https://arxiv.org/abs/2108.07755)
 - Efficient Task-aligned head with [DFL](https://arxiv.org/abs/2006.04388) and [VFL](https://arxiv.org/abs/2008.13367)
- [SiLU activation function](https://arxiv.org/abs/1710.05941)
+- [SiLU(Swish) activation function](https://arxiv.org/abs/1710.05941)

 ## Model Zoo
-|          Model           | GPU number | images/GPU |  backbone  | input shape | Box AP<sup>val<br>0.5:0.95 | Box AP<sup>test<br>0.5:0.95 | Params(M) | FLOPs(G) | V100 FP32(FPS) | V100 TensorRT FP16(FPS) | download | config  |
-|:------------------------:|:-------:|:----------:|:----------:| :-------:| :------------------: | :-------------------: |:---------:|:--------:| :------------: | :---------------------: | :------: | :------: |
-| PP-YOLOE-s                  |     8      |     32     | cspresnet-s |     640     |       43.0        |        43.2         |   7.93    |  17.36   |      208.3      |          333.3          | [model](https://paddledet.bj.bcebos.com/models/ppyoloe_crn_s_300e_coco.pdparams) | [config](./ppyoloe_crn_s_300e_coco.yml)                   |
-| PP-YOLOE-m                  |     8      |     28     | cspresnet-m |     640     |       49.0        |        49.1         |   23.43   |  49.91   |      123.4      |          208.3          | [model](https://paddledet.bj.bcebos.com/models/ppyoloe_crn_m_300e_coco.pdparams) | [config](./ppyoloe_crn_m_300e_coco.yml)                   |
-| PP-YOLOE-l                  |     8      |     20      | cspresnet-l |     640     |       51.4        |        51.6         |   52.20   |  110.07  |      78.1      |          149.2          | [model](https://paddledet.bj.bcebos.com/models/ppyoloe_crn_l_300e_coco.pdparams) | [config](./ppyoloe_crn_l_300e_coco.yml)                   |
-| PP-YOLOE-x                  |     8      |     16     | cspresnet-x |     640     |       52.3        |        52.4         |   98.42   |  206.59  |      45.0      |          95.2          | [model](https://paddledet.bj.bcebos.com/models/ppyoloe_crn_x_300e_coco.pdparams) | [config](./ppyoloe_crn_x_300e_coco.yml)                   |
+|          Model           | Epoch | GPU number | images/GPU |  backbone  | input shape | Box AP<sup>val<br>0.5:0.95 | Box AP<sup>test<br>0.5:0.95 | Params(M) | FLOPs(G) | V100 FP32(FPS) | V100 TensorRT FP16(FPS) | download | config  |
+|:------------------------:|:-------:|:-------:|:--------:|:----------:| :-------:| :------------------: | :-------------------: |:---------:|:--------:|:---------------:| :---------------------: | :------: | :------: |
+| PP-YOLOE-s                  | 400 |     8      |    32    | cspresnet-s |     640     |       43.4        |        43.6         |   7.93    |  17.36   |   208.3   |  333.3   | [model](https://paddledet.bj.bcebos.com/models/ppyoloe_crn_s_400e_coco.pdparams) | [config](./ppyoloe_crn_s_400e_coco.yml)                   |
+| PP-YOLOE-s                  | 300 |     8      |    32    | cspresnet-s |     640     |       43.0        |        43.2         |   7.93    |  17.36   |   208.3   |  333.3   | [model](https://paddledet.bj.bcebos.com/models/ppyoloe_crn_s_300e_coco.pdparams) | [config](./ppyoloe_crn_s_300e_coco.yml)                   |
+| PP-YOLOE-m                  | 300 |     8      |    28    | cspresnet-m |     640     |       49.0        |        49.1         |   23.43   |  49.91   |   123.4   |  208.3   | [model](https://paddledet.bj.bcebos.com/models/ppyoloe_crn_m_300e_coco.pdparams) | [config](./ppyoloe_crn_m_300e_coco.yml)                   |
+| PP-YOLOE-l                  | 300 |     8      |    20    | cspresnet-l |     640     |       51.4        |        51.6         |   52.20   |  110.07  |   78.1    |  149.2   | [model](https://paddledet.bj.bcebos.com/models/ppyoloe_crn_l_300e_coco.pdparams) | [config](./ppyoloe_crn_l_300e_coco.yml)                   |
+| PP-YOLOE-x                  | 300 |     8      |    16    | cspresnet-x |     640     |       52.3        |        52.4         |   98.42   |  206.59  |   45.0    |   95.2   | [model](https://paddledet.bj.bcebos.com/models/ppyoloe_crn_x_300e_coco.pdparams) | [config](./ppyoloe_crn_x_300e_coco.yml)                   |
+

 ### Comprehensive Metrics
-|          Model           | AP<sup>0.5:0.95 | AP<sup>0.5 |  AP<sup>0.75  | AP<sup>small  | AP<sup>medium | AP<sup>large | AR<sup>small | AR<sup>medium | AR<sup>large | download | config  |
-|:----------------------:|:---------------:|:----------:|:-------------:| :------------:| :-----------: | :----------: |:------------:|:-------------:|:------------:| :-----: | :-----: |
-| PP-YOLOE-s             |      43.0      |     59.6    |     47.2      |     26.0      |      47.4     |     58.7     |     45.1     |      70.6     |   81.4         | [model](https://paddledet.bj.bcebos.com/models/ppyoloe_crn_s_300e_coco.pdparams) | [config](./ppyoloe_crn_s_300e_coco.yml)|
-| PP-YOLOE-m             |      49.0      |     65.9    |     53.8      |     30.9      |      53.5     |     65.3     |     50.9     |      74.4     |   84.7         | [model](https://paddledet.bj.bcebos.com/models/ppyoloe_crn_m_300e_coco.pdparams) | [config](./ppyoloe_crn_m_300e_coco.yml)|
-| PP-YOLOE-l             |      51.4      |     68.6    |     56.2      |     34.8      |      56.1     |     68.0     |     53.1     |      76.8     |   85.6         | [model](https://paddledet.bj.bcebos.com/models/ppyoloe_crn_l_300e_coco.pdparams) | [config](./ppyoloe_crn_l_300e_coco.yml)|
-| PP-YOLOE-x             |      52.3      |     69.5    |     56.8      |     35.1      |      57.0     |     68.6     |     55.5     |      76.9     |   85.7         | [model](https://paddledet.bj.bcebos.com/models/ppyoloe_crn_x_300e_coco.pdparams) | [config](./ppyoloe_crn_x_300e_coco.yml)|
+|          Model           | Epoch | AP<sup>0.5:0.95 | AP<sup>0.5 |  AP<sup>0.75  | AP<sup>small  | AP<sup>medium | AP<sup>large | AR<sup>small | AR<sup>medium | AR<sup>large | download | config  |
+|:----------------------:|:-----:|:---------------:|:----------:|:-------------:| :------------:| :-----------: | :----------: |:------------:|:-------------:|:------------:| :-----: | :-----: |
+| PP-YOLOE-s             | 400 |      43.4      |     60.0    |     47.5      |     25.7      |      47.8     |     59.2     |     43.9     |      70.8     |   81.9         | [model](https://paddledet.bj.bcebos.com/models/ppyoloe_crn_s_400e_coco.pdparams) | [config](./ppyoloe_crn_s_400e_coco.yml)|
+| PP-YOLOE-s             | 300 |      43.0      |     59.6    |     47.2      |     26.0      |      47.4     |     58.7     |     45.1     |      70.6     |   81.4         | [model](https://paddledet.bj.bcebos.com/models/ppyoloe_crn_s_300e_coco.pdparams) | [config](./ppyoloe_crn_s_300e_coco.yml)|
+| PP-YOLOE-m             | 300 |      49.0      |     65.9    |     53.8      |     30.9      |      53.5     |     65.3     |     50.9     |      74.4     |   84.7         | [model](https://paddledet.bj.bcebos.com/models/ppyoloe_crn_m_300e_coco.pdparams) | [config](./ppyoloe_crn_m_300e_coco.yml)|
+| PP-YOLOE-l             | 300 |      51.4      |     68.6    |     56.2      |     34.8      |      56.1     |     68.0     |     53.1     |      76.8     |   85.6         | [model](https://paddledet.bj.bcebos.com/models/ppyoloe_crn_l_300e_coco.pdparams) | [config](./ppyoloe_crn_l_300e_coco.yml)|
+| PP-YOLOE-x             | 300 |      52.3      |     69.5    |     56.8      |     35.1      |      57.0     |     68.6     |     55.5     |      76.9     |   85.7         | [model](https://paddledet.bj.bcebos.com/models/ppyoloe_crn_x_300e_coco.pdparams) | [config](./ppyoloe_crn_x_300e_coco.yml)|


 **Notes:**

- PP-YOLOE is trained on COCO train2017 dataset and evaluated on val2017 & test-dev2017 dataset，all the model weights are trained for **300 epoches**.
+- PP-YOLOE is trained on COCO train2017 dataset and evaluated on val2017 & test-dev2017 dataset.
 - The model weights in the table of Comprehensive Metrics are **the same as** that in the original Model Zoo, and evaluated on **val2017**.
 - PP-YOLOE used 8 GPUs for mixed precision training, if **GPU number** or **mini-batch size** is changed, **learning rate** should be adjusted according to the formula **lr<sub>new</sub> = lr<sub>default</sub> * (batch_size<sub>new</sub> * GPU_number<sub>new</sub>) / (batch_size<sub>default</sub> * GPU_number<sub>default</sub>)**.
 - PP-YOLOE inference speed is tesed on single Tesla V100 with batch size as 1, **CUDA 10.2**, **CUDNN 7.6.5**, **TensorRT 6.0.1.8** in TensorRT mode.
@@ -221,17 +224,6 @@ Model | AP | AP<sub>50</sub>
 - *Due to the limited time, more verification results will be supplemented in the future. You are also welcome to contribute to PP-YOLOE*


-### Feature Models
-
-The PaddleDetection team provides configs and weights of various feature detection models based on PP-YOLOE, which users can download for use:
-
-|Scenarios | Related Datasets | Links|
-| :--------: | :---------: | :------: |
-|Pedestrian Detection | CrowdHuman | [pphuman](../pphuman) |
-|Vehicle Detection | BDD100K，UA-DETRAC | [ppvehicle](../ppvehicle) |
-|Small Object Detection | VisDrone | [visdrone](../visdrone) |
-
-
 ## Appendix

 Ablation experiments of PP-YOLOE.

--- a/configs/ppyoloe/README_cn.md
+++ b/configs/ppyoloe/README_cn.md
@@ -9,7 +9,7 @@
 - [附录](#附录)

 ## 简介
-PP-YOLOE是基于PP-YOLOv2的卓越的单阶段Anchor-free模型，超越了多种流行的yolo模型。PP-YOLOE有一系列的模型，即s/m/l/x，可以通过width multiplier和depth multiplier配置。PP-YOLOE避免使用诸如deformable convolution或者matrix nms之类的特殊算子，以使其能轻松地部署在多种多样的硬件上。更多细节可以参考我们的[report](https://arxiv.org/abs/2203.16250)。
+PP-YOLOE是基于PP-YOLOv2的卓越的单阶段Anchor-free模型，超越了多种流行的YOLO模型。PP-YOLOE有一系列的模型，即s/m/l/x，可以通过width multiplier和depth multiplier配置。PP-YOLOE避免了使用诸如Deformable Convolution或者Matrix NMS之类的特殊算子，以使其能轻松地部署在多种多样的硬件上。更多细节可以参考我们的[report](https://arxiv.org/abs/2203.16250)。

 <div align="center">
  <img src="../../docs/images/ppyoloe_map_fps.png" width=500 />
@@ -21,29 +21,31 @@ PP-YOLOE由以下方法组成
 - 可扩展的backbone和neck
 - [Task Alignment Learning](https://arxiv.org/abs/2108.07755)
 - Efficient Task-aligned head with [DFL](https://arxiv.org/abs/2006.04388)和[VFL](https://arxiv.org/abs/2008.13367)
- [SiLU激活函数](https://arxiv.org/abs/1710.05941)
+- [SiLU(Swish)激活函数](https://arxiv.org/abs/1710.05941)

 ## 模型库
-|          模型           | GPU个数 | 每GPU图片个数 |  骨干网络  | 输入尺寸 | Box AP<sup>val<br>0.5:0.95 | Box AP<sup>test<br>0.5:0.95 | Params(M) | FLOPs(G) | V100 FP32(FPS) | V100 TensorRT FP16(FPS) | 模型下载 | 配置文件  |
-|:------------------------:|:-------:|:--------:|:----------:| :-------:| :------------------: | :-------------------: |:---------:|:--------:|:---------------:| :---------------------: | :------: | :------: |
-| PP-YOLOE-s                  |     8      |    32    | cspresnet-s |     640     |       43.0        |        43.2         |   7.93    |  17.36   |       208.3 |          333.3          | [model](https://paddledet.bj.bcebos.com/models/ppyoloe_crn_s_300e_coco.pdparams) | [config](./ppyoloe_crn_s_300e_coco.yml)                   |
-| PP-YOLOE-m                  |     8      |    28    | cspresnet-m |     640     |       49.0        |        49.1         |   23.43   |  49.91   |   123.4   |  208.3   | [model](https://paddledet.bj.bcebos.com/models/ppyoloe_crn_m_300e_coco.pdparams) | [config](./ppyoloe_crn_m_300e_coco.yml)                   |
-| PP-YOLOE-l                  |     8      |    20    | cspresnet-l |     640     |       51.4        |        51.6         |   52.20   |  110.07  |   78.1    |  149.2   | [model](https://paddledet.bj.bcebos.com/models/ppyoloe_crn_l_300e_coco.pdparams) | [config](./ppyoloe_crn_l_300e_coco.yml)                   |
-| PP-YOLOE-x                  |     8      |    16    | cspresnet-x |     640     |       52.3        |        52.4         |   98.42   |  206.59  |   45.0    |   95.2   | [model](https://paddledet.bj.bcebos.com/models/ppyoloe_crn_x_300e_coco.pdparams) | [config](./ppyoloe_crn_x_300e_coco.yml)                   |
+|          模型           | Epoch | GPU个数 | 每GPU图片个数 |  骨干网络  | 输入尺寸 | Box AP<sup>val<br>0.5:0.95 | Box AP<sup>test<br>0.5:0.95 | Params(M) | FLOPs(G) | V100 FP32(FPS) | V100 TensorRT FP16(FPS) | 模型下载 | 配置文件  |
+|:------------------------:|:-------:|:-------:|:--------:|:----------:| :-------:| :------------------: | :-------------------: |:---------:|:--------:|:---------------:| :---------------------: | :------: | :------: |
+| PP-YOLOE-s                  | 400 |     8      |    32    | cspresnet-s |     640     |       43.4        |        43.6         |   7.93    |  17.36   |   208.3   |  333.3   | [model](https://paddledet.bj.bcebos.com/models/ppyoloe_crn_s_400e_coco.pdparams) | [config](./ppyoloe_crn_s_400e_coco.yml)                   |
+| PP-YOLOE-s                  | 300 |     8      |    32    | cspresnet-s |     640     |       43.0        |        43.2         |   7.93    |  17.36   |   208.3   |  333.3   | [model](https://paddledet.bj.bcebos.com/models/ppyoloe_crn_s_300e_coco.pdparams) | [config](./ppyoloe_crn_s_300e_coco.yml)                   |
+| PP-YOLOE-m                  | 300 |     8      |    28    | cspresnet-m |     640     |       49.0        |        49.1         |   23.43   |  49.91   |   123.4   |  208.3   | [model](https://paddledet.bj.bcebos.com/models/ppyoloe_crn_m_300e_coco.pdparams) | [config](./ppyoloe_crn_m_300e_coco.yml)                   |
+| PP-YOLOE-l                  | 300 |     8      |    20    | cspresnet-l |     640     |       51.4        |        51.6         |   52.20   |  110.07  |   78.1    |  149.2   | [model](https://paddledet.bj.bcebos.com/models/ppyoloe_crn_l_300e_coco.pdparams) | [config](./ppyoloe_crn_l_300e_coco.yml)                   |
+| PP-YOLOE-x                  | 300 |     8      |    16    | cspresnet-x |     640     |       52.3        |        52.4         |   98.42   |  206.59  |   45.0    |   95.2   | [model](https://paddledet.bj.bcebos.com/models/ppyoloe_crn_x_300e_coco.pdparams) | [config](./ppyoloe_crn_x_300e_coco.yml)                   |


 ### 综合指标
-|          模型           | AP<sup>0.5:0.95 | AP<sup>0.5 |  AP<sup>0.75  | AP<sup>small  | AP<sup>medium | AP<sup>large | AR<sup>small | AR<sup>medium | AR<sup>large | 模型下载 | 配置文件  |
-|:----------------------:|:---------------:|:----------:|:-------------:| :------------:| :-----------: | :----------: |:------------:|:-------------:|:------------:| :-----: | :-----: |
-| PP-YOLOE-s             |      43.0      |     59.6    |     47.2      |     26.0      |      47.4     |     58.7     |     45.1     |      70.6     |   81.4         | [model](https://paddledet.bj.bcebos.com/models/ppyoloe_crn_s_300e_coco.pdparams) | [config](./ppyoloe_crn_s_300e_coco.yml)|
-| PP-YOLOE-m             |      49.0      |     65.9    |     53.8      |     30.9      |      53.5     |     65.3     |     50.9     |      74.4     |   84.7         | [model](https://paddledet.bj.bcebos.com/models/ppyoloe_crn_m_300e_coco.pdparams) | [config](./ppyoloe_crn_m_300e_coco.yml)|
-| PP-YOLOE-l             |      51.4      |     68.6    |     56.2      |     34.8      |      56.1     |     68.0     |     53.1     |      76.8     |   85.6         | [model](https://paddledet.bj.bcebos.com/models/ppyoloe_crn_l_300e_coco.pdparams) | [config](./ppyoloe_crn_l_300e_coco.yml)|
-| PP-YOLOE-x             |      52.3      |     69.5    |     56.8      |     35.1      |      57.0     |     68.6     |     55.5     |      76.9     |   85.7         | [model](https://paddledet.bj.bcebos.com/models/ppyoloe_crn_x_300e_coco.pdparams) | [config](./ppyoloe_crn_x_300e_coco.yml)|
+|          模型           | Epoch | AP<sup>0.5:0.95 | AP<sup>0.5 |  AP<sup>0.75  | AP<sup>small  | AP<sup>medium | AP<sup>large | AR<sup>small | AR<sup>medium | AR<sup>large | 模型下载 | 配置文件  |
+|:----------------------:|:-----:|:---------------:|:----------:|:-------------:| :------------:| :-----------: | :----------: |:------------:|:-------------:|:------------:| :-----: | :-----: |
+| PP-YOLOE-s             | 400 |      43.4      |     60.0    |     47.5      |     25.7      |      47.8     |     59.2     |     43.9     |      70.8     |   81.9         | [model](https://paddledet.bj.bcebos.com/models/ppyoloe_crn_s_400e_coco.pdparams) | [config](./ppyoloe_crn_s_400e_coco.yml)|
+| PP-YOLOE-s             | 300 |      43.0      |     59.6    |     47.2      |     26.0      |      47.4     |     58.7     |     45.1     |      70.6     |   81.4         | [model](https://paddledet.bj.bcebos.com/models/ppyoloe_crn_s_300e_coco.pdparams) | [config](./ppyoloe_crn_s_300e_coco.yml)|
+| PP-YOLOE-m             | 300 |      49.0      |     65.9    |     53.8      |     30.9      |      53.5     |     65.3     |     50.9     |      74.4     |   84.7         | [model](https://paddledet.bj.bcebos.com/models/ppyoloe_crn_m_300e_coco.pdparams) | [config](./ppyoloe_crn_m_300e_coco.yml)|
+| PP-YOLOE-l             | 300 |      51.4      |     68.6    |     56.2      |     34.8      |      56.1     |     68.0     |     53.1     |      76.8     |   85.6         | [model](https://paddledet.bj.bcebos.com/models/ppyoloe_crn_l_300e_coco.pdparams) | [config](./ppyoloe_crn_l_300e_coco.yml)|
+| PP-YOLOE-x             | 300 |      52.3      |     69.5    |     56.8      |     35.1      |      57.0     |     68.6     |     55.5     |      76.9     |   85.7         | [model](https://paddledet.bj.bcebos.com/models/ppyoloe_crn_x_300e_coco.pdparams) | [config](./ppyoloe_crn_x_300e_coco.yml)|


 **注意:**

- PP-YOLOE模型使用COCO数据集中train2017作为训练集，使用val2017和test-dev2017作为测试集，模型权重均为训练**300 epoches**得到的。
+- PP-YOLOE模型使用COCO数据集中train2017作为训练集，使用val2017和test-dev2017作为测试集。
 - 综合指标的表格与模型库的表格里的模型权重是**同一个权重**，综合指标是使用**val2017**作为验证精度的。
 - PP-YOLOE模型训练过程中使用8 GPUs进行混合精度训练，如果**GPU卡数**或者**batch size**发生了改变，你需要按照公式 **lr<sub>new</sub> = lr<sub>default</sub> * (batch_size<sub>new</sub> * GPU_number<sub>new</sub>) / (batch_size<sub>default</sub> * GPU_number<sub>default</sub>)** 调整学习率。
 - PP-YOLOE模型推理速度测试采用单卡V100，batch size=1进行测试，使用**CUDA 10.2**, **CUDNN 7.6.5**，TensorRT推理速度测试使用**TensorRT 6.0.1.8**。
@@ -224,17 +226,6 @@ CUDA_VISIBLE_DEVICES=0 python deploy/python/infer.py --model_dir=output_inferenc
 - *由于人力/时间有限，后续将会持续补充更多验证结果，也欢迎各位开源用户贡献，共同优化PP-YOLOE*


-### 垂类模型应用
-
-PaddleDetection团队提供了基于PP-YOLOE的各种垂类检测模型的配置文件和权重，用户可以下载进行使用：
-
-|     场景    |    相关数据集    |    链接   |
-| :--------: | :---------: | :------: |
-|  行人检测   | CrowdHuman   |   [pphuman](../pphuman)  |
-|  车辆检测   | BDD100K，UA-DETRAC  |  [ppvehicle](../ppvehicle)   |
-|  小目标检测 | VisDrone     |  [visdrone](../visdrone)   |
-
-
 ## 附录

 PP-YOLOE消融实验

--- a/configs/ppyoloe/ppyoloe_crn_s_400e_coco.yml
+++ b/configs/ppyoloe/ppyoloe_crn_s_400e_coco.yml
+_BASE_: [
+  '../datasets/coco_detection.yml',
+  '../runtime.yml',
+  './_base_/optimizer_300e.yml',
+  './_base_/ppyoloe_crn.yml',
+  './_base_/ppyoloe_reader.yml',
+]
+
+log_iter: 100
+snapshot_epoch: 10
+weights: output/ppyoloe_crn_s_400e_coco/model_final
+
+pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/CSPResNetb_s_pretrained.pdparams
+depth_mult: 0.33
+width_mult: 0.50
+
+TrainReader:
+  batch_size: 32
+
+LearningRate:
+  base_lr: 0.04
+
+
+epoch: 400
+LearningRate:
+  base_lr: 0.025
+  schedulers:
+    - !CosineDecay
+      max_epochs: 480
+    - !LinearWarmup
+      start_factor: 0.
+      epochs: 5
+
+OptimizerBuilder:
+  optimizer:
+    momentum: 0.9
+    type: Momentum
+  regularizer:
+    factor: 0.0005
+    type: L2
+
+
+PPYOLOEHead:
+  static_assigner_epoch: 133
+  nms:
+    name: MultiClassNMS
+    nms_top_k: 10000
+    keep_top_k: 300
+    score_threshold: 0.01
+    nms_threshold: 0.7