text: import paddlex as pdx
  from paddlex import transforms as T
  
  # 下载和解压昆虫检测数据集
  dataset = 'https://bj.bcebos.com/paddlex/datasets/insect_det.tar.gz'
  pdx.utils.download_and_decompress(dataset, path='./')
  
  # 定义训练和验证时的transforms
  # API说明：https://github.com/PaddlePaddle/PaddleX/blob/release/2.0.0/docs/apis/transforms/transforms.md
  train_transforms = T.Compose([
      T.MixupImage(mixup_epoch=-1), T.RandomDistort(),
      T.RandomExpand(im_padding_value=[123.675, 116.28, 103.53]), T.RandomCrop(),
      T.RandomHorizontalFlip(), T.BatchRandomResize(
          target_sizes=[192, 224, 256, 288, 320, 352, 384, 416, 448, 480, 512],
          interp='RANDOM'), T.Normalize(
              mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
  ])
  
  eval_transforms = T.Compose([
      T.Resize(
          target_size=320, interp='CUBIC'), T.Normalize(
              mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
  ])
  
  # 定义训练和验证所用的数据集
  # API说明：https://github.com/PaddlePaddle/PaddleX/blob/release/2.0.0/docs/apis/datasets.md
  train_dataset = pdx.datasets.VOCDetection(
      data_dir='insect_det',
      file_list='insect_det/train_list.txt',
      label_list='insect_det/labels.txt',
      transforms=train_transforms,
      shuffle=True)
  
  eval_dataset = pdx.datasets.VOCDetection(
      data_dir='insect_det',
      file_list='insect_det/val_list.txt',
      label_list='insect_det/labels.txt',
      transforms=eval_transforms,
      shuffle=False)
  
  # 初始化模型，并进行训练
  # 可使用VisualDL查看训练指标，参考https://github.com/PaddlePaddle/PaddleX/blob/release/2.0.0/docs/visualdl.md
  num_classes = len(train_dataset.labels)
  model = pdx.det.PPYOLOTiny(num_classes=num_classes)
  
  # API说明：https://github.com/PaddlePaddle/PaddleX/blob/release/2.0.0/docs/apis/models/detection.md
  # 各参数介绍与调整说明：https://github.com/PaddlePaddle/PaddleX/blob/release/2.0.0/docs/parameters.md
  model.train(
      num_epochs=550,
      train_dataset=train_dataset,
      train_batch_size=16,
      eval_dataset=eval_dataset,
      pretrain_weights='COCO',
      learning_rate=0.005,
      warmup_steps=1000,
      warmup_start_lr=0.0,
      lr_decay_epochs=[130, 540],
      lr_decay_gamma=.5,
      save_interval_epochs=5,
      save_dir='output/ppyolotiny',
      use_vdl=True)
  