config.yml

train:

  # Name used to identify the run. Data inside `job_dir` will be stored under

  # `run_name`.

  run_name: vertebra-detection

  # Base directory in which model checkpoints & summaries (for Tensorboard) will

  # be saved.

  job_dir: jobs/

  save_checkpoint_secs: 1000

  save_summaries_secs: 1000
  
  
    
    



  # Number of epochs (complete dataset batches) to run.

  num_epochs: 500



dataset:

  type: object_detection

  # From which directory to read the dataset.

  dir: tfdata/
  
  data_augmentation:
    - flip:
        left_right: True
        up_down: True
        prob: 0.5
        
        
        
    



model:

  type: fasterrcnn

  network:

    # Total number of classes to predict.

    num_classes: 1
    
    
  # Whether to use batch normalization in the model.
  batch_norm: False
  
  base_network:
    # Which type of pretrained network to use.
    architecture: resnet_v1_101
    # Should we train the pretrained network.
    trainable: True
    # From which file to load the weights.
    
    download: True
    # Which endpoint layer to use as feature map for network.
    endpoint:
    # Starting point after which all the variables in the base network will be
    # trainable. If not specified, then all the variables in the network will be
    # trainable.
    fine_tune_from: block2
    
      
  anchors:
    # Base size to use for anchors.
    base_size: 256
    # Scale used for generating anchor sizes.
    scales: [0.25, 0.5]
    
    ratios: [1, 2]
    # Stride depending on feature map size (of pretrained).