text: """
  Faster R-CNN baseline
  Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = -1.000
  Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=3000 ] = 0.597
  Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=3000 ] = 0.372
  Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=3000 ] = 0.220
  Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=3000 ] = 0.391
  Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=3000 ] = 0.432
  Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=300 ] = 0.444
  Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=1000 ] = 0.447
  Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=3000 ] = 0.447
  Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=3000 ] = 0.315
  Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=3000 ] = 0.483
  Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=3000 ] = 0.551
  
  
  
  """
  
  _base_ = [
      '../_base_/models/faster_rcnn_r50_fpn_dota.py',
      '../_base_/datasets/dota_detection.py',
      '../_base_/schedules/schedule_1x.py', 
      '../_base_/default_runtime.py'
  ]
  
  data = dict(
      samples_per_gpu=4,
      workers_per_gpu=2)
  
  fp16 = dict(loss_scale=512.)
  
  optimizer = dict(type='SGD', lr=0.01*2.0/4.0, momentum=0.9, weight_decay=0.0001)
  # learning policy
  checkpoint_config = dict(interval=1)
  evaluation = dict(interval=12, metric='bbox', proposal_nums=(300, 1000, 3000))