text: 
  coco_params = {
  
      'model_dir': '/home/dan/work/light-head-rcnn/models/run00/',
      'train_dataset_path': '/mnt/datasets/COCO/train_shards/',
      'val_dataset_path': '/mnt/datasets/COCO/val_shards/',
      'pretrained_checkpoint': 'pretrained/resnet_v1_50.ckpt',
      'num_classes': 80,
  
      # IMAGE SIZES:
  
      # an integer or None
      'evaluation_min_dimension': 800,
      # an integer or None
      'evaluation_max_dimension': 1200,
  
      # a list of integers
      'training_min_dimensions': [600, 700, 800, 900, 1000],
      # an integer or None
      'training_max_dimension': 1400,
  
      'num_steps': 2000000,
      'lr_boundaries': [1500000],
      'lr_values': [5e-3, 5e-4],
  
      # PROPOSAL GENERATION:
  
      # an integer
      'min_proposal_area': 36,
      # a float number
      'before_nms_score_threshold': 1e-7,
      # an integer
      'nms_max_output_size': 2000,
      # a float number
      'proposal_iou_threshold': 0.7,
      # an integer
      'rpn_num_channels': 512,
  
      # LOSS:
  
      # float numbers, weights for losses
      'alpha': 1.0,  # rpn localization
      'beta': 1.0,  # rpn classification
      'gamma': 2.0,  # roi localization
      'theta': 1.0,  # roi classification
  
      'first_stage_batch_size': 256,
      'positive_fraction': 0.5,
      'num_hard_examples': 256,
  
      # BOX MATCHING:
  
      'positives_threshold': 0.7,
      'negatives_threshold': 0.3,
      'second_stage_threshold': 0.5,
  
      # FINAL POSTPROCESSING:
  
      'score_threshold': 0.01,
      'iou_threshold': 0.5,
      'max_boxes_per_class': 20,
  
      # FEATURE EXTRACTOR:
  
      'backbone': 'resnet',  # 'resnet' or 'mobilenet' or 'shufflenet'
      'depth_multiplier': 1.0,  # a float number, relevant only for mobilenet and shufflenet
      'weight_decay': 1e-4,
  
      # THE HEAD:
  
      # an integer, ps roi align pooling
      'p': 7,
      # an integer
      'k': 15,
      # an integer
      'channels_middle': 256,
      # an integer
      'fc_layer_size': 2048,
  
      # ANCHOR GENERATION:
  
      # a list of integers
      'scales': [32, 64, 128, 256, 512],
      # a list of float numbers
      'aspect_ratios': [1.0, 2.0, 0.5],
      # a tuple of integers
      'anchor_stride': (16, 16),
      # a tuple of integers
      'anchor_offset': (0, 0),
  }
  
  coco_light_params = coco_params.copy()
  coco_light_params.update({
      'model_dir': '/home/dan/work/light-head-rcnn/models/run01/',
      'train_dataset_path': '/mnt/datasets/COCO/train_shards/',
      'val_dataset_path': '/mnt/datasets/COCO/val_shards/',
      'pretrained_checkpoint': 'pretrained/shufflenet_v2_1.0x/model.ckpt-1661328',
      'backbone': 'shufflenet',
      'depth_multiplier': 1.0,
      'rpn_num_channels': 256,
      'channels_middle': 64,
  })
  