text: #_base_ = [
  #    '../_base_/models/faster_rcnn_r50_fpn.py',
  #    '../_base_/datasets/person_detection.py',
  #    '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
  #]
  
  _base_ = './faster_rcnn_r50_caffe_fpn_mstrain_1x_coco.py'
  model = dict(roi_head=dict(bbox_head=dict(num_classes=1)))
  #classes = ('person', )
  #data = dict(
  #    train=dict(classes=classes),
  #    val=dict(classes=classes),
  #    test=dict(classes=classes))
  
  
  _base_ = [
      '../faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_1x_coco.py',
      '../_base_/datasets/person_detection.py'
  ]
  
  #checkpoint_file = 'checkpoints/faster_rcnn_r50_caffe_fpn_mstrain_3x_coco_bbox_mAP-0.398_20200504_163323-30042637.pth'
  checkpoint_file = 'checkpoints/faster_rcnn_r50_fpn_1x_coco-person_20201216_175929-d022e227.pth'
  model = dict(roi_head=dict(bbox_head=dict(num_classes=1)))
  
  # optimizer
  # lr is set for a batch size of 8
  optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0005)
  optimizer_config = dict(grad_clip=None)
  # learning policy
  lr_config = dict(
      policy='step',
      warmup='linear',
      warmup_iters=500,
      warmup_ratio=0.001,
      step=[7])
  # the max_epochs and step in lr_config need specifically tuned for the customized dataset
  runner = dict(max_epochs=10)
  log_config = dict(interval=100)
  