config.json

{
  "model_type": "densenet",
  "architecture": "densenet121",
  "num_classes": 3,
  "input_size": [224, 224],
  "in_channels": 3,
  "classifier_input_features": 1024,
  "framework": "pytorch",
  "task": "image-classification",
  "domain": "histopathology",
  "modality": "whole-slide-imaging",
  "license": "gpl-3.0",
  "tags": [
    "histopathology",
    "tissue-detection",
    "whole-slide-imaging",
    "pathology",
    "medical-imaging",
    "densenet",
    "image-classification",
    "computational-pathology",
    "cancer-research"
  ],
  "preprocessing": {
    "resize": 224,
    "normalization": {
      "mean": [0.485, 0.456, 0.406],
      "std": [0.229, 0.224, 0.225]
    }
  },
  "class_labels": {
    "0": "background",
    "1": "artifact",
    "2": "tissue"
  },
  "recommended_threshold": {
    "class": 2,
    "probability": 0.8,
    "description": "Accept patches where class 2 (tissue) probability >= 0.8"
  },
  "version": "1.0.0",
  "release_date": "2024",
  "authors": [
    "Lab-Rasool",
    "Markowetz Lab (original training)"
  ],
  "huggingface_repo": "Lab-Rasool/tissue-detector",
  "related_frameworks": [
    "HoneyBee"
  ]
}