config/segmentation/deeplabv3_mobilevit_small.yaml [1:76]:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
common:
  run_label: "run_1"
  accum_freq: 1
  accum_after_epoch: -1
  log_freq: 200
  auto_resume: true
  mixed_precision: true
dataset:
  root_train: "/mnt/vision_datasets/pascal_voc/VOCdevkit/"
  root_val: "/mnt/vision_datasets/pascal_voc/VOCdevkit/"
  name: "pascal"
  category: "segmentation"
  train_batch_size0: 32
  val_batch_size0: 16
  eval_batch_size0: 1
  workers: 8
  persistent_workers: false
  pin_memory: false
  pascal:
    use_coco_data: true
    coco_root_dir: "/mnt/vision_datasets/coco_preprocess/"
image_augmentation:
  random_resize:
    enable: true
    min_size: 256
    max_size: 1024
  random_crop:
    enable: true
    mask_fill: 255
    resize_if_needed: true
  random_horizontal_flip:
    enable: true
sampler:
  name: "batch_sampler"
  bs:
    crop_size_width: 512
    crop_size_height: 512
loss:
  category: "segmentation"
  ignore_idx: 255
  segmentation:
    name: "cross_entropy"
optim:
  name: "adamw"
  weight_decay: 0.01
  no_decay_bn_filter_bias: false
  adamw:
    beta1: 0.9
    beta2: 0.999
scheduler:
  name: "cosine"
  is_iteration_based: false
  max_epochs: 50
  warmup_iterations: 500
  warmup_init_lr: 0.00009
  cosine:
    max_lr: 0.0009 # [2.7e-3 * N_GPUS^2 x (BATCH_SIZE_GPU0/ 32) * 0.02 ] # 0.02 comes from this fact 0.1 (ResNet SGD LR)/0.002 (MIT ADAMW LR)
    min_lr: 1.e-6
model:
  segmentation:
    name: "encoder_decoder"
    lr_multiplier: 10
    seg_head: "deeplabv3"
    output_stride: 16
    classifier_dropout: 0.1
    activation:
      name: "relu"
    deeplabv3:
      aspp_dropout: 0.1
      aspp_sep_conv: false
      aspp_out_channels: 256
      aspp_rates: [6, 12, 18]
  classification:
    name: "mobilevit"
    classifier_dropout: 0.1
    mit:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -



config/segmentation/deeplabv3_mobilevit_x_small.yaml [1:76]:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
common:
  run_label: "run_1"
  accum_freq: 1
  accum_after_epoch: -1
  log_freq: 200
  auto_resume: true
  mixed_precision: true
dataset:
  root_train: "/mnt/vision_datasets/pascal_voc/VOCdevkit/"
  root_val: "/mnt/vision_datasets/pascal_voc/VOCdevkit/"
  name: "pascal"
  category: "segmentation"
  train_batch_size0: 32
  val_batch_size0: 16
  eval_batch_size0: 1
  workers: 8
  persistent_workers: false
  pin_memory: false
  pascal:
    use_coco_data: true
    coco_root_dir: "/mnt/vision_datasets/coco_preprocess/"
image_augmentation:
  random_resize:
    enable: true
    min_size: 256
    max_size: 1024
  random_crop:
    enable: true
    mask_fill: 255
    resize_if_needed: true
  random_horizontal_flip:
    enable: true
sampler:
  name: "batch_sampler"
  bs:
    crop_size_width: 512
    crop_size_height: 512
loss:
  category: "segmentation"
  ignore_idx: 255
  segmentation:
    name: "cross_entropy"
optim:
  name: "adamw"
  weight_decay: 0.01
  no_decay_bn_filter_bias: false
  adamw:
    beta1: 0.9
    beta2: 0.999
scheduler:
  name: "cosine"
  is_iteration_based: false
  max_epochs: 50
  warmup_iterations: 500
  warmup_init_lr: 0.00009
  cosine:
    max_lr: 0.0009 # [2.7e-3 * N_GPUS^2 x (BATCH_SIZE_GPU0/ 32) * 0.02 ] # 0.02 comes from this fact 0.1 (ResNet SGD LR)/0.002 (MIT ADAMW LR)
    min_lr: 1.e-6
model:
  segmentation:
    name: "encoder_decoder"
    lr_multiplier: 10
    seg_head: "deeplabv3"
    output_stride: 16
    classifier_dropout: 0.1
    activation:
      name: "relu"
    deeplabv3:
      aspp_dropout: 0.1
      aspp_sep_conv: false
      aspp_out_channels: 256
      aspp_rates: [6, 12, 18]
  classification:
    name: "mobilevit"
    classifier_dropout: 0.1
    mit:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -



