random_seed: 42
yolov4_config {
  big_anchor_shape: "[(257.00, 274.00), (312.00, 305.00), (385.5, 387.00)]"
  mid_anchor_shape: "[(176.00, 195.00), (218.00, 225.00), (266.00, 201.00)]"
  small_anchor_shape: "[(84.00, 84.00), (121.00, 125.00), (162.00, 151.00)]"
  box_matching_iou: 0.5
  arch: "cspdarknet"
  nlayers: 19
  arch_conv_blocks: 2
  loss_loc_weight: 0.8
  loss_neg_obj_weights: 100.0
  loss_class_weights: 0.5
  label_smoothing: 0.0
  big_grid_xy_extend: 0.05
  mid_grid_xy_extend: 0.1
  small_grid_xy_extend: 0.2
  freeze_bn: false
  freeze_blocks: 0
  force_relu: false
}
training_config {
  batch_size_per_gpu: 8
  num_epochs: 160
  enable_qat: false
  checkpoint_interval: 10
  learning_rate {
    soft_start_cosine_annealing_schedule {
      min_learning_rate: 1e-7
      max_learning_rate: 1e-4
      soft_start: 0.3
    }
  }
  regularizer {
    type: NO_REG
    weight: 3e-9
  }
  optimizer {
    adam {
      epsilon: 1e-7
      beta1: 0.9
      beta2: 0.999
      amsgrad: false
    }
  }
  pruned_model_path: "/workspace/tlt-experiments/yolo_v4/experiment_dir_pruned/yolov4_cspdarknet19_pruned.tlt"
}
eval_config {
  average_precision_mode: SAMPLE
  batch_size: 1
  matching_iou_threshold: 0.5
}
nms_config {
  confidence_threshold: 0.01
  clustering_iou_threshold: 0.6
  top_k: 200
}
augmentation_config {
  hue: 0.1
  saturation: 1.5
  exposure:1.5
  vertical_flip:0
  horizontal_flip: 0.5
  jitter: 0.3
  output_width: 416
  output_height: 416
  randomize_input_shape_period: 0
  mosaic_prob: 0.5
  mosaic_min_ratio:0.2
}
dataset_config {
  data_sources: {
      label_directory_path: "/workspace/tlt-experiments/data/training/label_2"
      image_directory_path: "/workspace/tlt-experiments/data/training/image_2"
  }
  include_difficult_in_training: true
  target_class_mapping {
      key: "weed"
      value: "weed"
  }
  validation_data_sources: {
      label_directory_path: "/workspace/tlt-experiments/data/val/label"
      image_directory_path: "/workspace/tlt-experiments/data/val/image"
  }
}