modify ppyoloe docs, test=document_fix (#5784)

configs/ppyoloe/README.md

@@ -34,7 +34,7 @@ PP-YOLOE is composed of following methods:
 **Notes:**
 
 - PP-YOLOE is trained on COCO train2017 dataset and evaluated on val2017 & test-dev2017 dataset，Box AP<sup>test</sup> is evaluation results of `mAP(IoU=0.5:0.95)`.
-- PP-YOLOE used 8 GPUs for mixed precision training, if GPU number and mini-batch size is changed, learning rate and iteration times should be adjusted according [FAQ](https://github.com/PaddlePaddle/PaddleDetection/blob/develop/docs/tutorials/FAQ).
+- 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.
 - Refer to [Speed testing](#Speed-testing) to reproduce the speed testing results of PP-YOLOE.
 - If you set `--run_benchmark=True`，you should install these dependencies at first, `pip install pynvml psutil GPUtil`.
@@ -49,7 +49,7 @@ Training PP-YOLOE with mixed precision on 8 GPUs with following command
 python -m paddle.distributed.launch --gpus 0,1,2,3,4,5,6,7 tools/train.py -c configs/ppyoloe/ppyoloe_crn_l_300e_coco.yml --amp
 ```
 
-** Notes: ** use `--amp` to train with default config to avoid out of memeory.
+**Notes:** use `--amp` to train with default config to avoid out of memeory.
 
 ### Evaluation
 
@@ -158,7 +158,7 @@ CUDA_VISIBLE_DEVICES=0 python deploy/python/infer.py --model_dir=output_inferenc
 
 ```
 
-**Notes: **
+**Notes:**
 - TensorRT will perform optimization for the current hardware platform according to the definition of the network, generate an inference engine and serialize it into a file. This inference engine is only applicable to the current hardware hardware platform. If your hardware and software platform has not changed, you can set `use_static=True` in [enable_tensorrt_engine](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.4/deploy/python/infer.py#L660). In this way, the serialized file generated will be saved in the `output_inference` folder, and the saved serialized file will be loaded the next time when TensorRT is executed.
 - PaddleDetection release/2.4 and later versions will support NMS calling TensorRT, which requires PaddlePaddle release/2.3 and later versions.
 
@@ -170,7 +170,7 @@ Model | AP | AP<sub>50</sub>
 [YOLOv5](https://github.com/ultralytics/yolov5) | 26.0 | 42.7
 **PP-YOLOE** | **30.5** | **46.4**
 
-**Note**
+**Notes**
 - Here, we use [VisDrone](https://github.com/VisDrone/VisDrone-Dataset) dataset, and to detect 9 objects including `person, bicycles, car, van, truck, tricyle, awning-tricyle, bus, motor`.
 - Above models trained using official default config, and load pretrained parameters on COCO dataset.
 - *Due to the limited time, more verification results will be supplemented in the future. You are also welcome to contribute to PP-YOLOE*

configs/ppyoloe/README_cn.md

@@ -34,7 +34,7 @@ PP-YOLOE由以下方法组成
 **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，Box AP<sup>test</sup>为`mAP(IoU=0.5:0.95)`评估结果。
-- PP-YOLOE模型训练过程中使用8 GPUs进行混合精度训练，如果训练GPU数和batch size不使用上述配置，须参考[FAQ](https://github.com/PaddlePaddle/PaddleDetection/blob/develop/docs/tutorials/FAQ)调整学习率和迭代次数。
+- 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**。
 - 参考[速度测试](#速度测试)以复现PP-YOLOE推理速度测试结果。
 - 如果你设置了`--run_benchnark=True`, 你首先需要安装以下依赖`pip install pynvml psutil GPUtil`。
@@ -49,7 +49,7 @@ PP-YOLOE由以下方法组成
 python -m paddle.distributed.launch --gpus 0,1,2,3,4,5,6,7 tools/train.py -c configs/ppyoloe/ppyoloe_crn_l_300e_coco.yml --amp
 ```
 
-** 注意: ** 使用默认配置训练需要设置`--amp`以避免显存溢出.
+**注意:** 使用默认配置训练需要设置`--amp`以避免显存溢出.
 
 ### 评估