random_seed: 42
yolov3_config {
  big_anchor_shape: "[(114.94, 60.67), (159.06, 114.59), (297.59, 176.38)]"
  mid_anchor_shape: "[(42.99, 31.91), (79.57, 31.75), (56.80, 56.93)]"
  small_anchor_shape: "[(15.60, 13.88), (30.25, 20.25), (20.67, 49.63)]"
  matching_neutral_box_iou: 0.7
  arch: "resnet"
  nlayers: 18
  arch_conv_blocks: 2
  loss_loc_weight: 0.8
  loss_neg_obj_weights: 100.0
  loss_class_weights: 1.0
  freeze_bn: false
  #freeze_blocks: 0
  force_relu: false
}
training_config {
  batch_size_per_gpu: 100
  num_epochs: 10
  enable_qat: false
  checkpoint_interval: 10
  learning_rate {
  soft_start_annealing_schedule {
    min_learning_rate: 1e-6
    max_learning_rate: 1e-4
    soft_start: 0.1
    annealing: 0.2
    }
  }
  regularizer {
    type: L1
    weight: 3e-5
  }
  optimizer {
    adam {
      epsilon: 1e-7
      beta1: 0.9
      beta2: 0.999
      amsgrad: false
    }
  }
  pretrain_model_path: "/content/drive/MyDrive/results/yolo_v3/pretrained_resnet18/pretrained_object_detection_vresnet18/resnet_18.hdf5"
}
eval_config {
  average_precision_mode: SAMPLE
  batch_size: 8
  matching_iou_threshold: 0.5
}
nms_config {
  confidence_threshold: 0.001
  clustering_iou_threshold: 0.5
  top_k: 200
  force_on_cpu: True
}
augmentation_config {
  hue: 0.1
  saturation: 1.5
  exposure:1.5
  vertical_flip:0
  horizontal_flip: 0.5
  jitter: 0.3
  output_width: 960
  output_height: 544
  output_channel: 3
  randomize_input_shape_period: 0
}
dataset_config {
  data_sources: {
      tfrecords_path: "/content/drive/MyDrive/cable_damage_yolov8_dataset/tfrecords_new/kitti_tarnival_new*"
      image_directory_path: "/content/drive/MyDrive/cable_damage_yolov8_dataset/train/rename_and_save/images"
  }
  include_difficult_in_training: true
  image_extension: "jpg"
  target_class_mapping {
      key: "break"
      value: "break"
  }
  target_class_mapping {
      key: "thunderbolt"
      value: "thunderbolt"
  }
  validation_fold: 0
}