seed: 99
net:
    arch: inception_v3  # inception_v1, inception_v2, inception_v3
    kwargs:
        num_classes: 1000

dataset:
  train:
    meta_file: data/imagenet/meta/train.txt
    image_dir: data/imagenet/train
    random_resize_crop: 299
    colorjitter: [0.2, 0.2, 0.2, 0.1]
    mean: [0.485, 0.456, 0.406]
    std: [0.229, 0.224, 0.225]
    mirror: True
  test:
    meta_file: data/imagenet/meta/val.txt
    image_dir: data/imagenet/val
    resize: 331
    center_crop: [299, 299]
    colorjitter:
    mean: [0.485, 0.456, 0.406]
    std: [0.229, 0.224, 0.225]
    mirror: False
  batch_size: 32
  workers: 4

trainer:
  max_epoch: 160
  test_freq: 1
  log_freq: 100
  optimizer:
    type: SGD
    kwargs:
      lr: 0.2
      momentum: 0.9
      weight_decay: 0.0001
  lr_scheduler:
    warmup_epochs: 0
    type: MultiStepLR
    kwargs:
      milestones: [30,60,90]
      gamma: 0.1

saver:
  pretrian_model:
  resume_model:
  save_dir: checkpoints/inception_v3   # save checkpoint locally

# monitor:
#   type: pavi
#   _taskid: # continue training
#   kwargs:
#     project: default  # change to your own pavi project
#     task: inception_v3
#     model: inception_v3
