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5b86813 | 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 | #!/usr/bin/env python3
"""Quick model quality report."""
if __name__ == "__main__":
from ultralytics import YOLO
model = YOLO("best.pt")
epoch = model.ckpt.get("epoch", "?") if hasattr(model, "ckpt") else "?"
print(f"Epochs trained: {epoch}")
metrics = model.val(data="dataset/data.yaml", imgsz=416, device=0, conf=0.001)
print()
print("=" * 50)
print(" MODEL QUALITY REPORT")
print("=" * 50)
print(f" mAP@0.5: {metrics.box.map50:.4f} ({metrics.box.map50*100:.1f}%)")
print(f" mAP@0.5:0.95: {metrics.box.map:.4f} ({metrics.box.map*100:.1f}%)")
print(f" Precision: {metrics.box.mp:.4f} ({metrics.box.mp*100:.1f}%)")
print(f" Recall: {metrics.box.mr:.4f} ({metrics.box.mr*100:.1f}%)")
p, r = metrics.box.mp, metrics.box.mr
f1 = 2 * p * r / (p + r) if (p + r) > 0 else 0.0
print(f" F1-score: {f1:.4f} ({f1*100:.1f}%)")
print()
print(" Per-class mAP@0.5:")
for i, ap in enumerate(metrics.box.ap50):
print(f" {model.names[i]:>20s}: {ap:.4f} ({ap*100:.1f}%)")
print()
print(f" Inference speed: {metrics.speed['inference']:.1f} ms/image")
print("=" * 50)
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