model/evaluate.py

#!/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)