config/detection/ssd_mobilevit_small_320.yaml [1:60]:
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common:
  run_label: "run_1"
  accum_freq: 1
  accum_after_epoch: -1
  log_freq: 500
  auto_resume: true
  mixed_precision: true
dataset:
  root_train: "/mnt/vision_datasets/coco"
  root_val: "/mnt/vision_datasets/coco"
  name: "coco_ssd"
  category: "detection"
  train_batch_size0: 32
  val_batch_size0: 32
  eval_batch_size0: 1
  workers: 8
  persistent_workers: false
  pin_memory: true
sampler:
  name: "batch_sampler"
  bs:
    crop_size_width: 320
    crop_size_height: 320
loss:
  category: "detection"
  detection:
    name: "ssd_multibox_loss"
    ssd_multibox_loss:
      neg_pos_ratio: 3
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: 200
  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:
  detection:
    name: "ssd"
    ssd:
      anchors_aspect_ratio: [ [ 2, 3 ], [ 2, 3 ], [ 2, 3 ], [ 2, 3 ], [ 2, 3 ], [2] ]
      output_strides: [ 16, 32, 64, 128, 256, -1 ]
      proj_channels: [512, 256, 256, 128, 128, 64]
      center_variance: 0.1
      size_variance: 0.2
      iou_threshold: 0.5
      nms_iou_threshold: 0.5
  classification:
    name: "mobilevit"
    classifier_dropout: 0.1
    mit:
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config/detection/ssd_mobilevit_x_small_320.yaml [1:60]:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
common:
  run_label: "run_1"
  accum_freq: 1
  accum_after_epoch: -1
  log_freq: 500
  auto_resume: true
  mixed_precision: true
dataset:
  root_train: "/mnt/vision_datasets/coco"
  root_val: "/mnt/vision_datasets/coco"
  name: "coco_ssd"
  category: "detection"
  train_batch_size0: 32
  val_batch_size0: 32
  eval_batch_size0: 1
  workers: 8
  persistent_workers: false
  pin_memory: true
sampler:
  name: "batch_sampler"
  bs:
    crop_size_width: 320
    crop_size_height: 320
loss:
  category: "detection"
  detection:
    name: "ssd_multibox_loss"
    ssd_multibox_loss:
      neg_pos_ratio: 3
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: 200
  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:
  detection:
    name: "ssd"
    ssd:
      anchors_aspect_ratio: [ [ 2, 3 ], [ 2, 3 ], [ 2, 3 ], [ 2, 3 ], [ 2, 3 ], [2] ]
      output_strides: [ 16, 32, 64, 128, 256, -1 ]
      proj_channels: [512, 256, 256, 128, 128, 64]
      center_variance: 0.1
      size_variance: 0.2
      iou_threshold: 0.5
      nms_iou_threshold: 0.5
  classification:
    name: "mobilevit"
    classifier_dropout: 0.1
    mit:
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