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gradeeye
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four-ch-soft

Image Classification
PyTorch
DRGradingModel
medical-imaging
diabetic-retinopathy
domain-generalization
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four-ch-soft
File size: 1,273 Bytes
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{
  "model_type": "DRGradingModel",
  "task": "diabetic-retinopathy-grading",
  "architecture": "convnext_tiny",
  "backbone_timm_name": "convnext_tiny",
  "in_channels": 4,
  "image_size": 384,
  "num_classes": 5,
  "ordinal_head": "CORN",
  "num_thresholds": 4,
  "use_cbam": true,
  "cbam_num_stages": 2,
  "head_hidden_dim": 512,
  "dropout": 0.4,
  "channels_last": true,
  "ema_decay": 0.999,
  "loss_type": "corn",
  "use_class_weighting": true,
  "use_mixup": true,
  "primary_weights": "*_best_ema.safetensors (EMA; reproduces paper metrics)",
  "secondary_weights": "*_best.safetensors (raw model_state_dict)",
  "normalization": {
    "rgb": {
      "mean": [
        0.485,
        0.456,
        0.406
      ],
      "std": [
        0.229,
        0.224,
        0.225
      ]
    },
    "extra_channel": "Already in [0,1]; appended after RGB normalization; not ImageNet-normalized."
  },
  "class_names": [
    "No DR",
    "Mild",
    "Moderate",
    "Severe",
    "Proliferative DR"
  ],
  "extra_channel": {
    "source": "segmentation_pooled_soft",
    "producer": "gradeeye/seg-unet-bcedice",
    "description": "Raw sigmoid probability map from the BCE+Dice-trained U-Net; uint16 PNG divided by 65535 to [0,1]. No morphological post-processing."
  }
}