config.json

{
  "model_type": "resnet",
  "task": "image-classification",
  "framework": "pytorch",
  "pipeline_tag": "image-classification",
  "num_classes": 3,
  "class_labels": ["fa", "p_def", "blb"],
  "input_shape": [224, 224, 3],
  "preprocessing": {
    "resize": 256,
    "center_crop": 224,
    "normalize": [0.485, 0.456, 0.406],
    "normalize_std": [0.229, 0.224, 0.225]
  },
  "metrics": {
    "validation_accuracy": "93.82%",
    "per_class_accuracy": {
      "fa": "100.00%",
      "p_def": "86.21%",
      "blb": "95.00%"
    }
  },
  "license": "apache-2.0",
  "tags": [
    "image-classification",
    "crop-anomaly-detection",
    "agriculture",
    "resnet50",
    "deep-learning"
  ],
  "description": "ResNet50 model for 3-class anomaly detection. The model classifies images as Fall Armyworm (fa), Phosphorus Deficiency (p_def), or Bacterial Leaf Blight (blb)."
}