最初のファインチューニングと反復ループ

from ultralytics import YOLO
from ultralytics.data.utils import check_det_dataset
from ultralytics.utils import YAML

names = check_det_dataset("coco128.yaml")["names"]  # # 練習用セットではCOCOのクラス名をそのまま使用します
YAML.save("practice.yaml", {"path": "datasets/practice", "train": "train.txt", "val": "val.txt", "test": "test.txt", "names": names})

model = YOLO("yolo26n.pt")  # # COCOで事前学習済みです。タスクに合ったチェックポイントを使用してください
model.train(data="practice.yaml", epochs=3, imgsz=320)  # # 実際の実行例：epochs=100、imgsz=640

                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)
                   all         20        143      0.614        0.6      0.664      0.486

from ultralytics import YOLO

model = YOLO("runs/detect/train/weights/best.pt")  # your fine-tuned checkpoint
metrics = model.val(data="practice.yaml", imgsz=320, verbose=False)  # same data YAML and imgsz as training

matrix = metrics.confusion_matrix.matrix  # rows: predicted class, columns: true class; last row = background
for ap, cls in sorted(zip(metrics.box.ap, metrics.box.ap_class_index))[:5]:  # weakest five classes first
    total, missed, correct = matrix[:, cls].sum(), matrix[-1, cls], matrix[cls, cls]
    print(f"{model.names[cls]:>8}  AP50-95={ap:.3f}  {total:2.0f} labeled: {correct:2.0f} found, "
          f"{missed:2.0f} missed, {total - correct - missed:2.0f} called another class")

     car  AP50-95=0.000   4 labeled:  0 found,  4 missed,  0 called another class
    fork  AP50-95=0.000   1 labeled:  0 found,  0 missed,  1 called another class
 handbag  AP50-95=0.025   1 labeled:  1 found,  0 missed,  0 called another class
     cup  AP50-95=0.039  14 labeled:  0 found, 10 missed,  4 called another class
    sink  AP50-95=0.075   1 labeled:  1 found,  0 missed,  0 called another class