text: _base_ = '../faster_rcnn/faster_rcnn_x101_64x4d_fpn_1x_coco.py'
  
  
  model = dict(
  
  backbone=dict(
         
          	num_stages=4,
          	frozen_stages=4
          	),
  
  	roi_head=dict(
  		
  		bbox_head=dict(
  
  			num_classes=3
  				
  		)
  
  
  
  	)
  )
  
  
  dataset_type = 'COCODataset'
  classes = ('luchs', 'rotfuchs', 'wolf')
  data = dict(
      train=dict(
          img_prefix='customData/train/',
          classes=classes,
          ann_file='customData/train/_annotations.coco.json'),
      val=dict(
          img_prefix='customData/valid/',
          classes=classes,
          ann_file='customData/valid/_annotations.coco.json'),
      test=dict(
          img_prefix='customData/test/',
          classes=classes,
          ann_file='customData/test/_annotations.coco.json'))
          
  #optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001) #original (8x2=16)
  #optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001) #(4x2=8) 4 GPUs
  optimizer = dict(type='SGD', lr=0.0025, momentum=0.9, weight_decay=0.0001) #(1x2=2)
  
  total_epochs=14
  
  evaluation = dict(classwise=True, interval=1, metric='bbox')
  
  #http://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_x101_64x4d_fpn_1x_coco/faster_rcnn_x101_64x4d_fpn_1x_coco_20200204-833ee192.pth
  
  
  load_from = 'checkpoints/faster_rcnn_x101_64x4d_fpn_1x_coco_20200204-833ee192.pth'
  