text: _base_ = './umop_r50_fpn_1.5x_coco.py'
  model = dict(
      backbone=dict(
          # ResNet-101
          depth=101,
          init_cfg=dict(
              type='Pretrained',
              checkpoint='torchvision://resnet101')))
  # Train settings: mstrain 1333x[480:960]
  img_norm_cfg = dict(
      mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
  train_pipeline = [
      dict(type='LoadImageFromFile'),
      dict(type='LoadAnnotations', with_bbox=True),
      dict(
          type='Resize',
          img_scale=[(1333, 480), (1333, 960)],
          multiscale_mode='range',
          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=(1333, 800),
          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='DefaultFormatBundle'),
              dict(type='Collect', keys=['img']),
          ])
  ]
  data = dict(
      train=dict(pipeline=train_pipeline),
      val=dict(pipeline=test_pipeline),
      test=dict(pipeline=test_pipeline))
  
  work_dir = 'work_dirs/coco/umop/umop_r101_fpn_1.5x_mstrain_4x2_coco'