text: # model settings
  input_size = 512
  image_width, image_height = input_size, input_size
  width_mult = 1.0
  model = dict(
      type='SingleStageDetector',
      backbone=dict(
          type='mobilenetv2_w1',
          out_indices=(4, 5),
          frozen_stages=-1,
          norm_eval=False,
          pretrained=True
      ),
      neck=None,
      bbox_head=dict(
          type='SSDHead',
          num_classes=1,
          in_channels=(int(width_mult * 96), int(width_mult * 320)),
          anchor_generator=dict(
              type='SSDAnchorGeneratorClustered',
              strides=(16, 32),
              widths=[
                  [image_width * x for x in
                   [0.015411783166343854, 0.033018232306549156, 0.04467156688464953,
                    0.0610697815328886]],
                  [image_width * x for x in
                   [0.0789599954420517, 0.10113984043326349, 0.12805187473050397, 0.16198319380154758,
                    0.21636496806213493]],
  
              ],
              heights=[
                  [image_height * x for x in
                   [0.05032631418898226, 0.10070800135152037, 0.15806180366055939,
                    0.22343401646383804]],
                  [image_height * x for x in
                   [0.300881401352503, 0.393181898580379, 0.4998807213337051, 0.6386035764261081,
                    0.8363451552091518]],
  
              ],
          ),
          bbox_coder=dict(
              type='DeltaXYWHBBoxCoder',
              target_means=(.0, .0, .0, .0),
              target_stds=(0.1, 0.1, 0.2, 0.2), ),
          depthwise_heads=True,
          depthwise_heads_activations='relu',
          loss_balancing=True),
      # model training and testing settings
      train_cfg=dict(
          assigner=dict(
              type='MaxIoUAssigner',
              pos_iou_thr=0.4,
              neg_iou_thr=0.4,
              min_pos_iou=0.,
              ignore_iof_thr=-1,
              gt_max_assign_all=False),
          smoothl1_beta=1.,
          use_giou=False,
          use_focal=False,
          allowed_border=-1,
          pos_weight=-1,
          neg_pos_ratio=3,
          debug=False),
      test_cfg=dict(
          nms=dict(type='nms', iou_threshold=0.45),
          min_bbox_size=0,
          score_thr=0.02,
          max_per_img=200,
          nms_pre_classwise=200))
  cudnn_benchmark = True
  # dataset settings
  dataset_type = 'CocoDataset'
  data_root = '../../data/airport/'
  img_norm_cfg = dict(mean=[0, 0, 0], std=[255, 255, 255], to_rgb=True)
  train_pipeline = [
      dict(type='LoadImageFromFile', to_float32=True),
      dict(type='LoadAnnotations', with_bbox=True),
      dict(
          type='PhotoMetricDistortion',
          brightness_delta=32,
          contrast_range=(0.5, 1.5),
          saturation_range=(0.5, 1.5),
          hue_delta=18),
      dict(
          type='MinIoURandomCrop',
          min_ious=(0.1, 0.3, 0.5, 0.7, 0.9),
          min_crop_size=0.1),
      dict(type='Resize', img_scale=(input_size, input_size), keep_ratio=False),
      dict(type='Normalize', **img_norm_cfg),
      dict(type='RandomFlip', flip_ratio=0.5),
      dict(type='DefaultFormatBundle'),
      dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
  ]
  test_pipeline = [
      dict(type='LoadImageFromFile'),
      dict(
          type='MultiScaleFlipAug',
          img_scale=(input_size, input_size),
          flip=False,
          transforms=[
              dict(type='Resize', keep_ratio=False),
              dict(type='Normalize', **img_norm_cfg),
              dict(type='ImageToTensor', keys=['img']),
              dict(type='Collect', keys=['img']),
          ])
  ]
  data = dict(
      samples_per_gpu=30,
      workers_per_gpu=3,
      train=dict(
          type='RepeatDataset',
          times=5,
          dataset=dict(
              type=dataset_type,
              classes=('person',),
              ann_file=data_root + 'annotation_person_train.json',
              min_size=20,
              img_prefix=data_root + 'train',
              pipeline=train_pipeline
          )
      ),
      val=dict(
          type=dataset_type,
          classes=('person',),
          ann_file=data_root + 'annotation_person_val.json',
          img_prefix=data_root + 'val',
          test_mode=True,
          pipeline=test_pipeline),
      test=dict(
          type=dataset_type,
          classes=('person',),
          ann_file=data_root + 'annotation_person_val.json',
          img_prefix=data_root + 'val',
          test_mode=True,
          pipeline=test_pipeline))
  # optimizer
  optimizer = dict(type='SGD', lr=0.05, momentum=0.9, weight_decay=0.0005)
  optimizer_config = dict()
  # learning policy
  lr_config = dict(
      policy='step',
      warmup='linear',
      warmup_iters=1200,
      warmup_ratio=1.0 / 3,
      step=[8, 15, 18])
  checkpoint_config = dict(interval=1)
  # yapf:disable
  log_config = dict(
      interval=1,
      hooks=[
          dict(type='TextLoggerHook'),
          dict(type='TensorboardLoggerHook')
      ])
  # yapf:enable
  # runtime settings
  total_epochs = 20
  dist_params = dict(backend='nccl')
  log_level = 'INFO'
  work_dir = 'outputs/person-detection-0202'
  load_from = None
  resume_from = None
  workflow = [('train', 1)]
  