eligapris/v-mdd-2000-150

Corn Leaf Disease Classification Model Analysis

Dataset Breakdown

The dataset consists of four classes with the following distribution:

Class	Number of Images
Healthy_Leaf	3021
Gray_Leaf_Spot	2478
Common_Rust	2949
Northern_Leaf_Blight	3303

Note: There is a slight class imbalance, with Gray_Leaf_Spot having notably fewer images compared to the other classes.

Model Performance Metrics

The model was trained using AutoTrain for image classification. Here's a breakdown of the validation metrics:

Metric	Value
Loss	0.3697
Accuracy	0.8585
F1 (Macro)	0.6843
F1 (Micro)	0.8585
F1 (Weighted)	0.8303
Precision (Macro)	0.8204
Precision (Micro)	0.8585
Precision (Weighted)	0.8821
Recall (Macro)	0.7170
Recall (Micro)	0.8585
Recall (Weighted)	0.8585

Metric Explanations

1. Loss (0.3697): This relatively low value indicates that the model is learning well.

2. Accuracy (0.8585): The model correctly classifies 85.85% of all instances across all classes.

3. F1 Score:

   • Macro (0.6843): The unweighted mean of F1 scores for each class.
   • Micro (0.8585): Calculated globally by counting the total true positives, false negatives, and false positives.
   • Weighted (0.8303): The weighted average of F1 scores for each class, accounting for class imbalance.

4. Precision:

   • Macro (0.8204): The unweighted mean of precision scores for each class.
   • Micro (0.8585): The global precision across all classes.
   • Weighted (0.8821): The weighted average of precision scores for each class.

5. Recall:

   • Macro (0.7170): The unweighted mean of recall scores for each class.
   • Micro (0.8585): The global recall across all classes.
   • Weighted (0.8585): The weighted average of recall scores for each class.

Analysis

1. Class Imbalance: The difference between macro and micro scores suggests class imbalance, which aligns with our dataset breakdown. The Gray_Leaf_Spot class, having fewer images, likely contributes to this imbalance.

2. Precision vs Recall: Precision scores are generally higher than recall scores, especially for macro metrics. This suggests the model is more cautious in its predictions, preferring to be correct when it does predict a class.

3. Performance on Majority vs Minority Classes: The higher micro and weighted scores compared to macro scores indicate that the model performs better on more frequent classes. This is likely due to the class imbalance, with the model potentially struggling more with the Gray_Leaf_Spot class.

4. Overall Performance: With an accuracy of 85.85%, the model shows good overall performance. However, there's room for improvement, especially in handling the class imbalance.