未验证 提交 9dadaae7 编写于 作者: W wangxinxin08 提交者: GitHub

fix some problem in docs, test=document_fix (#3028)

上级 277bc13f
......@@ -45,6 +45,6 @@ python -u tools/infer.py -c configs/pedestrian/pedestrian_yolov3_darknet.yml \
Some inference results are visualized below:
![](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/static/docs/images/PedestrianDetection_001.png)
![](../../docs/images/PedestrianDetection_001.png)
![](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/static/docs/images/PedestrianDetection_004.png)
![](../../docs/images/PedestrianDetection_004.png)
......@@ -46,6 +46,6 @@ python -u tools/infer.py -c configs/pedestrian/pedestrian_yolov3_darknet.yml \
预测结果示例:
![](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/static/docs/images/PedestrianDetection_001.png)
![](../../docs/images/PedestrianDetection_001.png)
![](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/static/docs/images/PedestrianDetection_004.png)
![](../../docs/images/PedestrianDetection_004.png)
......@@ -11,12 +11,12 @@ English | [简体中文](README_cn.md)
## Introduction
[PP-YOLO](https://arxiv.org/abs/2007.12099) is a optimized model based on YOLOv3 in PaddleDetection,whose performance(mAP on COCO) and inference spped are better than [YOLOv4](https://arxiv.org/abs/2004.10934),PaddlePaddle 2.0.0rc1(available on pip now) or [Daily Version](https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/install/Tables.html#whl-release) is required to run this PP-YOLO。
[PP-YOLO](https://arxiv.org/abs/2007.12099) is a optimized model based on YOLOv3 in PaddleDetection,whose performance(mAP on COCO) and inference spped are better than [YOLOv4](https://arxiv.org/abs/2004.10934),PaddlePaddle 2.0.2(available on pip now) or [Daily Version](https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/install/Tables.html#whl-develop) is required to run this PP-YOLO。
PP-YOLO reached mmAP(IoU=0.5:0.95) as 45.9% on COCO test-dev2017 dataset, and inference speed of FP32 on single V100 is 72.9 FPS, inference speed of FP16 with TensorRT on single V100 is 155.6 FPS.
<div align="center">
<img src="../../../docs/images/ppyolo_map_fps.png" width=500 />
<img src="../../docs/images/ppyolo_map_fps.png" width=500 />
</div>
PP-YOLO improved performance and speed of YOLOv3 with following methods:
......@@ -213,7 +213,7 @@ Optimizing method and ablation experiments of PP-YOLO compared with YOLOv3.
- Performance and inference spedd are measure with input shape as 608
- All models are trained on COCO train2017 datast and evaluated on val2017 & test-dev2017 dataset,`Box AP` is evaluation results as `mAP(IoU=0.5:0.95)`.
- Inference speed is tested on single Tesla V100 with batch size as 1 following test method and environment configuration in benchmark above.
- [YOLOv3-DarkNet53](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/yolov3/yolov3_darknet53_270e_coco.yml) with mAP as 39.0 is optimized YOLOv3 model in PaddleDetection,see [Model Zoo](https://github.com/PaddlePaddle/PaddleDetection/blob/develop/docs/MODEL_ZOO.md) for details.
- [YOLOv3-DarkNet53](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/yolov3/yolov3_darknet53_270e_coco.yml) with mAP as 39.0 is optimized YOLOv3 model in PaddleDetection,see [YOLOv3](https://github.com/PaddlePaddle/PaddleDetection/blob/develop/configs/yolov3/README.md) for details.
## Citation
......
......@@ -11,12 +11,12 @@
## 简介
[PP-YOLO](https://arxiv.org/abs/2007.12099)是PaddleDetection优化和改进的YOLOv3的模型,其精度(COCO数据集mAP)和推理速度均优于[YOLOv4](https://arxiv.org/abs/2004.10934)模型,要求使用PaddlePaddle 2.0.0rc1(可使用pip安装) 或适当的[develop版本](https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/install/Tables.html#whl-release)
[PP-YOLO](https://arxiv.org/abs/2007.12099)是PaddleDetection优化和改进的YOLOv3的模型,其精度(COCO数据集mAP)和推理速度均优于[YOLOv4](https://arxiv.org/abs/2004.10934)模型,要求使用PaddlePaddle 2.0.2(可使用pip安装) 或适当的[develop版本](https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/install/Tables.html#whl-develop)
PP-YOLO在[COCO](http://cocodataset.org) test-dev2017数据集上精度达到45.9%,在单卡V100上FP32推理速度为72.9 FPS, V100上开启TensorRT下FP16推理速度为155.6 FPS。
<div align="center">
<img src="../../../docs/images/ppyolo_map_fps.png" width=500 />
<img src="../../docs/images/ppyolo_map_fps.png" width=500 />
</div>
PP-YOLO从如下方面优化和提升YOLOv3模型的精度和速度:
......@@ -207,7 +207,7 @@ PP-YOLO模型相对于YOLOv3模型优化项消融实验数据如下表所示。
- 精度与推理速度数据均为使用输入图像尺寸为608的测试结果
- Box AP为在COCO train2017数据集训练,val2017和test-dev2017数据集上评估`mAP(IoU=0.5:0.95)`数据
- 推理速度为单卡V100上,batch size=1, 使用上述benchmark测试方法的测试结果,测试环境配置为CUDA 10.2,CUDNN 7.5.1
- [YOLOv3-DarkNet53](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/yolov3/yolov3_darknet53_270e_coco.yml)精度38.9为PaddleDetection优化后的YOLOv3模型,可参见[模型库](https://github.com/PaddlePaddle/PaddleDetection/blob/master/docs/MODEL_ZOO.md)
- [YOLOv3-DarkNet53](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/yolov3/yolov3_darknet53_270e_coco.yml)精度38.9为PaddleDetection优化后的YOLOv3模型,可参见[YOLOv3](https://github.com/PaddlePaddle/PaddleDetection/blob/develop/configs/yolov3/README.md)
## 引用
......
......@@ -48,6 +48,6 @@ python -u tools/infer.py -c configs/vehicle/vehicle_yolov3_darknet.yml \
Some inference results are visualized below:
![](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/static/docs/images/VehicleDetection_001.jpeg)
![](../../docs/images/VehicleDetection_001.jpeg)
![](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/static/docs/images/VehicleDetection_005.png)
![](../../docs/images/VehicleDetection_005.png)
......@@ -49,6 +49,6 @@ python -u tools/infer.py -c configs/vehicle/vehicle_yolov3_darknet.yml \
预测结果示例:
![](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/static/docs/images/VehicleDetection_001.jpeg)
![](../../docs/images/VehicleDetection_001.jpeg)
![](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/static/docs/images/VehicleDetection_005.png)
![](../../docs/images/VehicleDetection_005.png)
......@@ -398,7 +398,7 @@ OptimizerBuilder:
type: L2
```
**几点说明:**
- 可以通过OptimizerBuilder.optimizer指定优化器的类型及参数,目前支持的优化可以参考[PaddlePaddle官方文档](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/optimizer/Overview_cn.html)
- 可以通过OptimizerBuilder.optimizer指定优化器的类型及参数,目前支持的优化可以参考[PaddlePaddle官方文档](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/optimizer/Overview_cn.html)
- 可以设置LearningRate.schedulers设置不同学习率调整策略的组合,PaddlePaddle目前支持多种学习率调整策略,具体也可参考[PaddlePaddle官方文档](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/optimizer/Overview_cn.html)。需要注意的是,你需要对于PaddlePaddle中的学习率调整策略进行简单的封装,具体可参考源码`ppdet/optimizer.py`
##### 2.2.3Reader配置文件
......
......@@ -65,7 +65,7 @@ PaddleDetection的数据处理模块的所有代码逻辑在`ppdet/data/`中,
}
```
xxx_rec中的内容也可以通过`DetDataSet`的data_fields参数来控制,即可以过滤掉一些不需要的字段,但大多数情况下不需要修改,按照`configs/dataset`中的默认配置即可。
xxx_rec中的内容也可以通过`DetDataSet`的data_fields参数来控制,即可以过滤掉一些不需要的字段,但大多数情况下不需要修改,按照`configs/datasets`中的默认配置即可。
此外,在parse_dataset函数中,保存了类别名到id的映射的一个字典`cname2cid`。在coco数据集中,会利用[COCO API](https://github.com/cocodataset/cocoapi)从标注文件中加载数据集的类别名,并设置此字典。在voc数据集中,如果设置`use_default_label=False`,将从`label_list.txt`中读取类别列表,反之将使用voc默认的类别列表。
......@@ -153,7 +153,7 @@ COCO数据集目前分为COCO2014和COCO2017,主要由json文件和image文件
from . import xxx
from .xxx import *
```
完成以上两步就将新的数据源`XXXDataSet`添加好了,你可以参考[配置及运行](#配置及运行)实现自定义数据集的使用。
完成以上两步就将新的数据源`XXXDataSet`添加好了,你可以参考[配置及运行](#5.配置及运行)实现自定义数据集的使用。
### 3.数据预处理
......
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