[cherry-pick] add visdrone ppyoloe configs and doc (#6164)

* add visdrone ppyoloe cfg and doc

* fix doc

* fix configs

configs/datasets/visdrone_detection.yml

+metric: COCO
+num_classes: 10
+
+TrainDataset:
+  !COCODataSet
+    image_dir: VisDrone2019-DET-train
+    anno_path: train.json
+    dataset_dir: dataset/visdrone
+    data_fields: ['image', 'gt_bbox', 'gt_class', 'is_crowd']
+
+EvalDataset:
+  !COCODataSet
+    image_dir: VisDrone2019-DET-val
+    anno_path: val.json
+    # image_dir: test_dev
+    # anno_path: test_dev.json
+    dataset_dir: dataset/visdrone
+
+TestDataset:
+  !ImageFolder
+    anno_path: val.json
+    dataset_dir: dataset/visdrone

configs/visdrone/README.md

+简体中文 | [English](README.md)
+
+# VisDrone-DET 检测模型
+
+PaddleDetection团队提供了针对VisDrone-DET小目标数航拍场景的基于PP-YOLOE的检测模型，用户可以下载模型进行使用。整理后的COCO格式VisDrone-DET数据集[下载链接](https://bj.bcebos.com/v1/paddledet/data/smalldet/visdrone.zip)，检测其中的10类，包括 `pedestrian(1), people(2), bicycle(3), car(4), van(5), truck(6), tricycle(7), awning-tricycle(8), bus(9), motor(10)`，原始数据集[下载链接](https://github.com/VisDrone/VisDrone-Dataset)。
+
+|    模型   | COCOAPI mAP<sup>val<br>0.5:0.95 | COCOAPI mAP<sup>val<br>0.5 | COCOAPI mAP<sup>test_dev<br>0.5:0.95 | COCOAPI mAP<sup>test_dev<br>0.5 | MatlabAPI mAP<sup>test_dev<br>0.5:0.95 | MatlabAPI mAP<sup>test_dev<br>0.5 | 下载  | 配置文件 |
+|:---------|:------:|:------:| :----: | :------:| :------: | :------:| :----: | :------:|
+|PP-YOLOE-s|  23.5  |  39.9  |  19.4  |  33.6   |  23.68   |  40.66  | [下载链接](https://paddledet.bj.bcebos.com/models/ppyoloe_crn_s_80e_visdrone.pdparams) | [配置文件](./ppyoloe_crn_s_80e_visdrone.yml) |
+|PP-YOLOE-l|  29.8  |  48.3  |  23.0  |  38.6   |  27.29   |  45.52  | [下载链接](https://paddledet.bj.bcebos.com/models/ppyoloe_crn_l_80e_visdrone.pdparams) | [配置文件](./ppyoloe_crn_l_80e_visdrone.yml) |
+
+**注意:**
+- 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。
+- 具体使用教程请参考[ppyoloe](../configs/ppyoloe#getting-start)。
+
+
+## 引用
+```
+@ARTICLE{9573394,
+  author={Zhu, Pengfei and Wen, Longyin and Du, Dawei and Bian, Xiao and Fan, Heng and Hu, Qinghua and Ling, Haibin},
+  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
+  title={Detection and Tracking Meet Drones Challenge},
+  year={2021},
+  volume={},
+  number={},
+  pages={1-1},
+  doi={10.1109/TPAMI.2021.3119563}
+}
+```

configs/visdrone/ppyoloe_crn_l_80e_visdrone.yml

+_BASE_: [
+  '../datasets/visdrone_detection.yml',
+  '../runtime.yml',
+  '../ppyoloe/_base_/optimizer_300e.yml',
+  '../ppyoloe/_base_/ppyoloe_crn.yml',
+  '../ppyoloe/_base_/ppyoloe_reader.yml',
+]
+log_iter: 100
+snapshot_epoch: 10
+weights: output/ppyoloe_crn_l_80e_visdrone/model_final
+
+pretrain_weights: https://paddledet.bj.bcebos.com/models/ppyoloe_crn_l_300e_coco.pdparams
+depth_mult: 1.0
+width_mult: 1.0
+
+TrainReader:
+  batch_size: 8
+
+epoch: 80
+LearningRate:
+  base_lr: 0.001
+  schedulers:
+    - !CosineDecay
+      max_epochs: 96
+    - !LinearWarmup
+      start_factor: 0.
+      epochs: 1
+
+PPYOLOEHead:
+  static_assigner_epoch: -1
+  nms:
+    name: MultiClassNMS
+    nms_top_k: 10000
+    keep_top_k: 500
+    score_threshold: 0.01
+    nms_threshold: 0.6

configs/visdrone/ppyoloe_crn_s_80e_visdrone.yml

+_BASE_: [
+  '../datasets/visdrone_detection.yml',
+  '../runtime.yml',
+  '../ppyoloe/_base_/optimizer_300e.yml',
+  '../ppyoloe/_base_/ppyoloe_crn.yml',
+  '../ppyoloe/_base_/ppyoloe_reader.yml',
+]
+log_iter: 100
+snapshot_epoch: 10
+weights: output/ppyoloe_crn_s_80e_visdrone/model_final
+
+pretrain_weights: https://paddledet.bj.bcebos.com/models/ppyoloe_crn_s_300e_coco.pdparams
+depth_mult: 0.33
+width_mult: 0.50
+
+TrainReader:
+  batch_size: 8
+
+epoch: 80
+LearningRate:
+  base_lr: 0.001
+  schedulers:
+    - !CosineDecay
+      max_epochs: 96
+    - !LinearWarmup
+      start_factor: 0.
+      epochs: 1
+
+PPYOLOEHead:
+  static_assigner_epoch: -1
+  nms:
+    name: MultiClassNMS
+    nms_top_k: 10000
+    keep_top_k: 500
+    score_threshold: 0.01
+    nms_threshold: 0.6