Update README.md

README.md

@@ -140,3 +140,31 @@ The model performs slightly better at detecting damaged packages than normal one
 
 ### Visual Examples of Classes
 
+![Class Examples](classexamples.png)
+
+The examples above illustrate typical images used during training. Damaged packages often contain dents, crushed corners, or torn cardboard, while normal packages appear structurally intact.
+
+### Key Visualizations
+
+#### Confusion Matrix
+![Confusion Matrix](confusion_matrix_normalized.png)
+
+#### F1 Confidence Curve
+![F1 Confidence Curve](BoxF1_curve.png)
+
+#### Training Results
+![Training Results](results.png)
+
+### Performance Analysis
+
+Overall, the YOLOv11 model performs well for detecting packages and classifying them as damaged or normal. 
+The high mAP50 score of 0.959 suggests the model is usually able to both locate packages and correctly classify their condition. 
+The damaged class performs slightly better than the normal class, likely because visible damage such as dents or crushed edges creates stronger visual features that are easier for the model to learn.
+The confusion matrix shows that most predictions fall along the diagonal, meaning the model correctly classifies the majority of packages. 
+However, the matrix also highlights a background issue that is common in object detection models. In some cases the model predicts background where an object exists, meaning it misses a package entirely. 
+This can happen when packages blend into the environment, appear small in the image, or have subtle visual features.
+The F1-confidence curve shows that the model maintains strong performance across a range of confidence thresholds, with the best F1 score around 0.92. 
+The training results plots also show that loss decreases and evaluation metrics improve steadily over the 50 training epochs, suggesting the model learned useful features without significant overfitting.
+Overall, the model works well for clear package images, but performance may decrease when packages are partially occluded, poorly lit, or when damage is subtle.
+
+## 5. Limitations and Biases