configuration_radarscenes_cnn.yml

RANDOM:
  seed: 1234

DIRECTORIES:
  dataset_dir: 'dataset/RadarScenesData/data'
  model_weights_dir: 'model_weights/cnn'
  weights_name: 'grid_based_detector.pt'

DATA_SELECTION_PARAM:
  temporal_window_size: 25       # accumulate measurements from 25 frames (accumulation for approx 500 ms)

DATASET:
  # all of the radar measurement attributes that 'nuscenes' dataset provide (just for reference)
  radarmeas_attributes_all:   
      ['timestamp', 'range_sc', 'azimuth_sc', 'rcs', 'vr', 'vr_compensated',
       'x_cc', 'y_cc', 'x_seq', 'y_seq', 'uuid', 'track_id', 'label_id' ]
  # a subset of radar dataset attributes that are actually utilized (just for reference)
  # some are used to compute node features, and some are used to compute labels
  radarmeas_attributes_selected: 
      [ 'rcs', 'vr_compensated', 'x_cc', 'y_cc', 'track_id', 'label_id' ]

OBJECT_CATEGORIES:
  OBJECT_CLASS: ['CAR', 'PEDESTRIAN', 'PEDESTRIAN_GROUP', 'TWO_WHEELER', 'LARGE_VEHICLE', 'NONE', 'FALSE', 'STATIC']
  OBJECT_CLASS_WEIGHTS: [0.8, 1.0, 1.0, 1.0, 0.8, 0.7, 0.7, 0.5]
  OBJECT_CLASS_DYN: ['CAR', 'PEDESTRIAN', 'PEDESTRIAN_GROUP', 'TWO_WHEELER', 'LARGE_VEHICLE', 'NONE', 'FALSE']
  OBJECT_CLASS_DYN_WEIGHTS: [1.0, 1.0, 1.0, 1.0, 1.0, 0.7, 0.7]

GRID_LIMITS: 
  max_x: 100
  min_x: 0
  max_y: 50
  min_y: -50
  min_sigma_x: 0.5
  max_sigma_x: 2
  min_sigma_y: 0.5
  max_sigma_y: 2
  dx: 0.5
  dy: 0.5

CNN_ARCHITECTURE:
  input_image_dimension: 3
  base_stem_channels: [32, 64]
  base_kernel_sizes: [11, 7]
  bottleneck_number_of_blocks: [2, 2, 2, 2]
  bottleneck_stem_channels: [128, 256, 512, 1024]
  bottleneck_width_channels: 64
  bottleneck_kernel_size: 3
  neck_out_channels: 64
  neck_kernel_size: 3
  head_stem_channels: [64]
  head_ffn_channels: [64]
  head_kernel_size: 3
  augmented_features_for_feed_forward_block: ['vr', 'rcs']
  reg_offset: ['dx', 'dy']
  reg_mu: [0, 0]
  reg_sigma: [8, 4]
  activation: 'leakyrelu'
  conv_type: 'conv2d'

LOSS_WEIGHTS:
  loss_cls: 1.0
  loss_reg: 10.0

OPTIMIZATION:
  optim: sgd
  max_training_iterations: 100000
  learning_rate: 0.001
  weight_decay: 0.0001

DATASET:
  num_training_samples: 200000         # to use all the samples put -1
  num_validation_samples: 1000         # to use all the samples put -1
  shuffle_training_samples: True
  shuffle_validation_samples: True