<filename>bottom-up-features/config.py<gh_stars>1-10
# effdet_config

from torch.utils.data.sampler import SequentialSampler, RandomSampler

class effdet_config:
  num_workers = 8
  batch_size = hyper_parameters['batch_size']
  n_epochs = 15
  lr = 2e-4
  folder = 'trained_model'
  verbose = True
  verbose_step = 1
  step_scheduler = False
  validation_scheduler = True
  SchedulerClass = torch.optim.lr_scheduler.ReduceLROnPlateau
  scheduler_params = dict(
      mode = 'min',
      factor = 0.5,
      patience = 1,
      verbose = False, 
      threshold = 0.0001,
      threshold_mode = 'abs',
      cooldown = 0, 
      min_lr = 1e-8,
      eps = 1e-08)

<filename>bottom-up-features/config.py<gh_stars>1-10
# effdet_config

from torch.utils.data.sampler import SequentialSampler, RandomSampler

class effdet_config:
  num_workers = 8
  batch_size = hyper_parameters['batch_size']
  n_epochs = 15
  lr = 2e-4
  folder = 'trained_model'
  verbose = True
  verbose_step = 1
  step_scheduler = False
  validation_scheduler = True
  SchedulerClass = torch.optim.lr_scheduler.ReduceLROnPlateau
  scheduler_params = dict(
      mode = 'min',
      factor = 0.5,
      patience = 1,
      verbose = False, 
      threshold = 0.0001,
      threshold_mode = 'abs',
      cooldown = 0, 
      min_lr = 1e-8,
      eps = 1e-08)

# effdet_config