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ExamInk-Seg

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ExamInk-Seg
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{
  "architectures": [
    "InkEraserModel"
  ],
  "model_type": "unet_plus_plus",
  "encoder_name": "resnet50",
  "encoder_weights": "imagenet",
  "in_channels": 3,
  "classes": 1,
  "decoder_attention_type": "scse",
  "activation": "sigmoid",
  "framework": "pytorch-lightning",
  "task": "binary-segmentation",
  "input_normalization": {
    "mean": [
      0.485,
      0.456,
      0.406
    ],
    "std": [
      0.229,
      0.224,
      0.225
    ],
    "max_pixel_value": 255.0
  },
  "training_config": {
    "optimizer": "AdamW",
    "lr": 3e-05,
    "weight_decay": 1e-05,
    "loss": {
      "dice_weight": 0.8,
      "bce_weight": 0.2,
      "bce_pos_weight": 2.0,
      "dice_from_logits": true
    },
    "metrics": [
      "iou_03",
      "iou_05",
      "f1_03",
      "f1_05"
    ],
    "scheduler": {
      "name": "ReduceLROnPlateau",
      "mode": "max",
      "monitor": "val_iou_05",
      "factor": 0.5,
      "patience": 2,
      "min_lr": 1e-07
    },
    "train": {
      "batch_size": 6,
      "crop_size": 1024,
      "accumulate_grad_batches": 4,
      "precision": "bf16-mixed",
      "max_epochs": 200,
      "min_epochs": 5
    }
  },
  "inference": {
    "sliding_window": {
      "tile": 1024,
      "stride": 768,
      "tta_hflip": true
    }
  },
  "description": "ExamInk-Seg 二值墨迹分割模型（U-Net++ + ResNet50 编码器）。",
  "license": "MIT"
}