geti/library/src/getitune/recipe/detection/yolox_x_tile.yaml at develop · open-edge-platform/geti · GitHub

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task: DETECTION
model:
  class_path: getitune.backend.lightning.models.detection.yolox.YOLOX
  init_args:
    model_name: yolox_x
    label_info: 80

    optimizer:
      class_path: torch.optim.SGD
      init_args:
        lr: 0.001
        momentum: 0.9
        weight_decay: 0.0001

    scheduler:
      class_path: getitune.backend.lightning.schedulers.LinearWarmupSchedulerCallable
      init_args:
        num_warmup_steps: 0
        main_scheduler_callable:
          class_path: lightning.pytorch.cli.ReduceLROnPlateau
          init_args:
            mode: max
            factor: 0.1
            patience: 4
            monitor: val/map_50

engine:
  device: auto

callback_monitor: val/map_50

data: ../_base_/data/detection_tile.yaml

callbacks:
  - class_path: getitune.backend.lightning.callbacks.adaptive_early_stopping.EarlyStoppingWithWarmup
    init_args:
      mode: max
      patience: 10
      check_on_train_epoch_end: false
      monitor: val/map_50
      min_delta: 0.001
      warmup_iters: 30
      warmup_epochs: 3
  - class_path: lightning.pytorch.callbacks.ModelCheckpoint
    init_args:
      dirpath: "" # use engine.work_dir
      monitor: val/map_50
      mode: max
      save_top_k: 1
      save_last: true
      auto_insert_metric_name: false
      filename: "checkpoints/epoch_{epoch:03d}"

overrides:
  gradient_clip_val: 35.0
  data:
    input_size:
      - 640
      - 640
    train_subset:
      batch_size: 4
      augmentations_cpu:
        - class_path: getitune.data.augmentation.transforms.Resize
          init_args:
            size: $(input_size)
            keep_aspect_ratio: false
        - class_path: torchvision.transforms.v2.RandomHorizontalFlip
          init_args:
            p: 0.5
        - class_path: torchvision.transforms.v2.ColorJitter
          init_args:
            brightness: 0.125
            contrast: 0.5
            saturation: 0.5
            hue: 0.05
      augmentations_gpu:
        - class_path: kornia.augmentation.Normalize
          init_args:
            mean: [0.0, 0.0, 0.0]
            std: [0.00392156862745098, 0.00392156862745098, 0.00392156862745098]
    val_subset:
      batch_size: 4
      augmentations_cpu:
        - class_path: getitune.data.augmentation.transforms.Resize
          init_args:
            size: $(input_size)
            keep_aspect_ratio: false
            resize_targets: false
      augmentations_gpu:
        - class_path: kornia.augmentation.Normalize
          init_args:
            mean: [0.0, 0.0, 0.0]
            std: [0.00392156862745098, 0.00392156862745098, 0.00392156862745098]
    test_subset:
      batch_size: 4
      augmentations_cpu:
        - class_path: getitune.data.augmentation.transforms.Resize
          init_args:
            size: $(input_size)
            keep_aspect_ratio: false
            resize_targets: false
      augmentations_gpu:
        - class_path: kornia.augmentation.Normalize
          init_args:
            mean: [0.0, 0.0, 0.0]
            std: [0.00392156862745098, 0.00392156862745098, 0.00392156862745098]