text: _base_ = '../_base_/default_runtime.py'
  # model settings
  model = dict(
      type='YOLOV3',
      pretrained='open-mmlab://darknet53',
      backbone=dict(type='Darknet', depth=53, out_indices=(3, 4, 5)),
      neck=dict(
          type='YOLOV3Neck',
          num_scales=3,
          in_channels=[1024, 512, 256],
          out_channels=[512, 256, 128]),
      bbox_head=dict(
          type='YOLOV3Head',
          # edited
          num_classes=1,
          in_channels=[512, 256, 128],
          out_channels=[1024, 512, 256],
          anchor_generator=dict(
              type='YOLOAnchorGenerator',
              # edited
              base_sizes=[[(1214,526), (1549,733), (2036,951)],
                          [(739,480), (881,385), (940,519)],
                          [(391,310), (487,264), (586,400)]],
              strides=[32, 16, 8]),
          bbox_coder=dict(type='YOLOBBoxCoder'),
          featmap_strides=[32, 16, 8],
          loss_cls=dict(
              type='CrossEntropyLoss',
              use_sigmoid=True,
              loss_weight=1.0,
              reduction='sum'),
          loss_conf=dict(
              type='CrossEntropyLoss',
              use_sigmoid=True,
              loss_weight=1.0,
              reduction='sum'),
          loss_xy=dict(
              type='CrossEntropyLoss',
              use_sigmoid=True,
              loss_weight=2.0,
              reduction='sum'),
          loss_wh=dict(type='MSELoss', loss_weight=2.0, reduction='sum')))
  # training and testing settings
  train_cfg = dict(
      assigner=dict(
          type='GridAssigner', pos_iou_thr=0.5, neg_iou_thr=0.5, min_pos_iou=0))
  test_cfg = dict(
      nms_pre=1000,
      min_bbox_size=0,
      score_thr=0.05,
      conf_thr=0.005,
      nms=dict(type='nms', iou_threshold=0.45),
      max_per_img=100)
  # dataset settings
  dataset_type = 'CocoDataset'
  # edited
  # classes = ('car')
  data_root = 'data/custom/'
  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'),
      dict(
          type='Expand',
          mean=img_norm_cfg['mean'],
          to_rgb=img_norm_cfg['to_rgb'],
          ratio_range=(1, 2)),
      dict(
          type='MinIoURandomCrop',
          min_ious=(0.4, 0.5, 0.6, 0.7, 0.8, 0.9),
          min_crop_size=0.3),
      dict(type='Resize', img_scale=[(320, 320), (608, 608)], keep_ratio=True),
      dict(type='RandomFlip', flip_ratio=0.5),
      dict(type='Normalize', **img_norm_cfg),
      dict(type='Pad', size_divisor=32),
      dict(type='DefaultFormatBundle'),
      dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
  ]
  test_pipeline = [
      dict(type='LoadImageFromFile'),
      dict(
          type='MultiScaleFlipAug',
          img_scale=(608, 608),
          flip=False,
          transforms=[
              dict(type='Resize', keep_ratio=True),
              dict(type='RandomFlip'),
              dict(type='Normalize', **img_norm_cfg),
              dict(type='Pad', size_divisor=32),
              dict(type='ImageToTensor', keys=['img']),
              dict(type='Collect', keys=['img'])
          ])
  ]
  data = dict(
      samples_per_gpu=8,
      workers_per_gpu=4,
      # edited
      train=dict(
          type=dataset_type,
          # classes = classes,
          ann_file=data_root + 'annotations/train.json',
          img_prefix=data_root + 'train/',
          pipeline=train_pipeline),
      val=dict(
          type=dataset_type,
          # classes = classes,
          ann_file=data_root + 'annotations/val.json',
          img_prefix=data_root + 'val/',
          pipeline=test_pipeline),
      test=dict(
          type=dataset_type,
          # classes = classes,
          ann_file=data_root + 'annotations/val.json',
          img_prefix=data_root + 'val/',
          pipeline=test_pipeline))
  # optimizer
  optimizer = dict(type='SGD', lr=0.001, momentum=0.9, weight_decay=0.0005)
  optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2))
  # learning policy
  lr_config = dict(
      policy='step',
      warmup='linear',
      warmup_iters=2000,  # same as burn-in in darknet
      warmup_ratio=0.1,
      step=[218, 246])
  # runtime settings
  # edited
  total_epochs = 50
  evaluation = dict(interval=1, metric=['bbox'])
  