outputs/comprehensive_evaluation_report.json

{
  "evaluation_summary": {
    "report_generated": "2025-09-08T14:49:18.192053",
    "models_evaluated": [
      "V1_Baseline",
      "V2_Enhanced"
    ],
    "dataset_characteristics": {
      "total_classes": 17,
      "class_distribution": "Balanced (3 samples per class in test set)",
      "data_challenges": [
        "Small dataset size (255 total samples)",
        "Limited samples per class",
        "Potential class imbalance in training"
      ]
    }
  },
  "performance_analysis": {
    "V1": {
      "final_validation_accuracy": 0.11764705882352941,
      "best_validation_accuracy": 0.11764705882352941,
      "final_training_accuracy": 0.1111111111111111,
      "total_epochs": 20,
      "overfitting_indicator": -0.006535947712418305
    },
    "V2": {
      "final_validation_accuracy": 0.11764705882352941,
      "best_validation_accuracy": 0.11764705882352941,
      "test_accuracy": 0.11764705882352941,
      "test_f1_score": 0.03725490196078431,
      "total_epochs": 20,
      "dataset_size": 153,
      "model_improvements": [
        "Enhanced data augmentation pipeline",
        "Improved model architecture with BatchNorm",
        "Label smoothing for better generalization",
        "AdamW optimizer with weight decay",
        "Cosine annealing learning rate schedule",
        "Gradient clipping for training stability",
        "F1-score based model selection"
      ]
    }
  },
  "key_findings": [
    "\u26a0\ufe0f Low test accuracy (11.8%) indicates model struggles with current dataset",
    "\u26a0\ufe0f Very low F1-score (0.037) suggests poor precision/recall balance",
    "\ud83c\udfaf 15 classes have zero F1-score, indicating classification difficulties"
  ],
  "recommendations": [
    "\ud83d\udcca Increase dataset size significantly (target: 1000+ samples per class)",
    "\ud83d\udd04 Implement more aggressive data augmentation techniques",
    "\u2696\ufe0f Address class imbalance with weighted sampling or SMOTE",
    "\ud83e\udde0 Consider ensemble methods or different architectures (EfficientNet, Vision Transformer)",
    "\ud83d\udcc8 Implement progressive resizing and test-time augmentation",
    "\ud83c\udfaf Use focal loss or class-balanced loss functions",
    "\ud83d\udd0d Perform detailed error analysis and confusion matrix review",
    "\ud83d\udcdd Collect more diverse and representative training data"
  ],
  "next_steps": [
    "\ud83d\udd2c Implement Grad-CAM visualization for model interpretability",
    "\ud83c\udf10 Develop REST API for model deployment",
    "\ud83d\udcf1 Create user-friendly frontend interface",
    "\ud83e\uddea Set up continuous model evaluation pipeline",
    "\ud83d\udcda Build knowledge base with disease information and remedies",
    "\ud83d\ude80 Deploy model to cloud platform for scalability"
  ]
}