text: # Basiskonfigurationsfile
  _base_ = '../detectors/detectors_cascade_rcnn_r50_1x_coco.py'
  
  dataset_type = 'CocoDataset'
  classes = ('raccoon', 'squirrel', 'bobcat', 'dog', 'coyote', 'rabbit', 'bird', 'cat', 'deer', 'fox') #10 classes
  
  
  model = dict(
  
  		backbone=dict(
  			frozen_stages=4
  
  		),
  
      		roi_head=dict(
      			
      			bbox_head=[
              dict(
                  type='Shared2FCBBoxHead',
                  in_channels=256,
                  fc_out_channels=1024,
                  roi_feat_size=7,
                  num_classes=10,
                  bbox_coder=dict(
                      type='DeltaXYWHBBoxCoder',
                      target_means=[0.0, 0.0, 0.0, 0.0],
                      target_stds=[0.1, 0.1, 0.2, 0.2]),
                  reg_class_agnostic=True,
                  loss_cls=dict(
                      type='CrossEntropyLoss',
                      use_sigmoid=False,
                      loss_weight=1.0),
                  loss_bbox=dict(type='SmoothL1Loss', beta=1.0,
                                 loss_weight=1.0)),
              dict(
                  type='Shared2FCBBoxHead',
                  in_channels=256,
                  fc_out_channels=1024,
                  roi_feat_size=7,
                  num_classes=10,
                  bbox_coder=dict(
                      type='DeltaXYWHBBoxCoder',
                      target_means=[0.0, 0.0, 0.0, 0.0],
                      target_stds=[0.05, 0.05, 0.1, 0.1]),
                  reg_class_agnostic=True,
                  loss_cls=dict(
                      type='CrossEntropyLoss',
                      use_sigmoid=False,
                      loss_weight=1.0),
                  loss_bbox=dict(type='SmoothL1Loss', beta=1.0,
                                 loss_weight=1.0)),
              dict(
                  type='Shared2FCBBoxHead',
                  in_channels=256,
                  fc_out_channels=1024,
                  roi_feat_size=7,
                  num_classes=10,
                  bbox_coder=dict(
                      type='DeltaXYWHBBoxCoder',
                      target_means=[0.0, 0.0, 0.0, 0.0],
                      target_stds=[0.033, 0.033, 0.067, 0.067]),
                  reg_class_agnostic=True,
                  loss_cls=dict(
                      type='CrossEntropyLoss',
                      use_sigmoid=False,
                      loss_weight=1.0),
                  loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0))
          ]
      			
      			
          	)
         )
  
  
  data = dict(
      train=dict(
      	type='CocoDataset',
          #img_prefix='customDataCaltech/caltech_halfsize_adv',
          img_prefix='/media/Pool/Thesis/Datensets/cct_images',
   	#img_prefix='/home/felice/Datasets/cct_images',
          classes=classes,
          ann_file='customDataCaltech/adv5/adv5_train.json'),
      val=dict(
      	type='CocoDataset',
          img_prefix='/media/Pool/Thesis/Datensets/cct_images',
  	#img_prefix='/home/felice/Datasets/cct_images',
          classes=classes,
          ann_file='customDataCaltech/adv5/adv5_known_val.json'),
      test=dict(
      	type='CocoDataset',
  	#img_prefix='/home/felice/Datasets/cct_images',
          img_prefix='/media/Pool/Thesis/Datensets/cct_images',
          classes=classes,
          ann_file='customDataCaltech/adv5/adv5_known_test.json'))
  
  #optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001) #original (8x2=16)
  optimizer = dict(type='SGD', lr=0.0025, momentum=0.9, weight_decay=0.0001) #(1x2=2)
  
  evaluation = dict(classwise=True, interval=1, metric='bbox')
  
  #work_dir = '/media/SSD2project/WilLiCam/checkpoint_workdir/detectorsR50_cct_adv5_coco'
  
  total_epochs = 16 #default=12
  
  # Pretrained model laden
  load_from = 'checkpoints/detectors_cascade_rcnn_r50_1x_coco-32a10ba0.pth'
  
  #http://download.openmmlab.com/mmdetection/v2.0/detectors/detectors_cascade_rcnn_r50_1x_coco/detectors_cascade_rcnn_r50_1x_coco-32a10ba0.pth
  
  