optimized hyperparams: cosine LR, copy_paste, augmentation tuning, 100 epochs

main.py

@@ -12,12 +12,38 @@ def train():
 
     results = model.train(
         data=str(DATASET_YAML),
-        epochs=75,
+        epochs=100,
         imgsz=1280,
-        batch=128,
+        batch=64,
         project=str(RUNS_DIR),
         name="pothole",
         exist_ok=True,
+
+        # LR scheduling — cosine annealing with warmup
+        lr0=0.001,
+        lrf=0.01,
+        warmup_epochs=3,
+        warmup_momentum=0.8,
+        cos_lr=True,
+
+        # Regularization
+        weight_decay=0.0005,
+        dropout=0.0,
+        patience=50,
+
+        # Augmentation — tuned for road/pothole data
+        mosaic=1.0,
+        copy_paste=0.3,       # paste potholes onto new backgrounds
+        degrees=5.0,           # slight rotation (roads tilt a bit)
+        scale=0.5,             # scale jitter
+        fliplr=0.5,            # horizontal flip
+        flipud=0.0,            # roads are always right-side up
+        hsv_h=0.015,           # hue jitter (lighting conditions)
+        hsv_s=0.7,             # saturation jitter (wet/dry roads)
+        hsv_v=0.4,             # brightness jitter (day/night)
+        translate=0.1,
+        perspective=0.0,       # road camera is always flat
+        mixup=0.1,             # blend images for harder examples
     )
 
     print(f"Training complete. Results saved to: {results.save_dir}")