Update README.md

README.md

@@ -115,9 +115,17 @@ This model is a fine-tuned YOLOv11s object detection model trained to detect and
 
 ### Key Visualizations
 
-**1. Loss & Metrics Curves:** All three training losses (box, classification, DFL) decrease consistently across 15 epochs. The mAP50 curve trends upward throughout training with no sign of plateauing, confirming the model is still actively learning at the point training was stopped. Train and validation losses track closely together, indicating no overfitting at this stage.
+**1. Loss & Metrics Curves**
+![Loss Curves](results.png)
+All three training losses (box, classification, DFL) decrease consistently across 15 epochs. The mAP50 curve trends upward throughout training with no sign of plateauing, confirming the model is still actively learning at the point training was stopped. Train and validation losses track closely together, indicating no overfitting at this stage.
 
-**2. Confusion Matrix:** Alligator Cracks achieve the highest correct detection count (655), reflecting their visual distinctiveness and higher training representation. The most notable pattern across all classes is the background row — a large proportion of true instances are missed entirely and classified as background. Inter-class confusion between the four damage types is low, meaning when the model does fire a detection, it classifies the damage type correctly. The main failure mode is missed detections, not misclassification.
+**2. Confusion Matrix**
+![Confusion Matrix](Picture1.jpg)
+Alligator Cracks achieve the highest correct detection count (655), reflecting their visual distinctiveness and higher training representation. The most notable pattern across all classes is the background row — a large proportion of true instances are missed entirely and classified as background. Inter-class confusion between the four damage types is low, meaning when the model does fire a detection, it classifies the damage type correctly. The main failure mode is missed detections, not misclassification.
+
+**3. Sample Predictions**
+![Sample Predictions](val_batch0_pred.jpg)
+Real validation set predictions showing all four damage classes detected on Japan road imagery.
 
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