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4f69e23 813519c 4f69e23 f88d737 c79a620 f88d737 4f69e23 f88d737 4f69e23 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 | from pathlib import Path
from ultralytics import YOLO
DATASET_YAML = Path(__file__).parent / "dataset" / "dataset.yaml"
RUNS_DIR = Path(__file__).parent / "runs"
def train():
model = YOLO("runs/pothole/weights/best.pt")
results = model.train(
data=str(DATASET_YAML),
epochs=100,
imgsz=1280,
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}")
return results
def main():
train()
if __name__ == "__main__":
main()
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