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efficientnet_arch_params.yaml 598 B

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  1. defaults:
  2. - default_arch_params
  3. backbone_mode: False # cuts off classification head
  4. batch_norm_momentum: 0.99 # value used for the running_mean and running_var computation
  5. batch_norm_epsilon: 1e-3 # value added to the denominator for numerical stability
  6. image_size: # net's input size
  7. # see doc in super_gradients/training/models/efficientnet.py round_filters
  8. width_coefficient:
  9. depth_divisor: 8
  10. min_depth:
  11. depth_coefficient:
  12. dropout_rate: # dropout probability in final layer
  13. num_classes: # number of outputs of the classification head
  14. drop_connect_rate: 0.2 # connection dropout probability
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