yolov8实现知识蒸馏

yolov8是一个支持知识蒸馏的目标检测模型，实现知识蒸馏可以选择logit和feature-based蒸馏方法，并且在进行蒸馏训练时需要设置好相应的蒸馏参数。以下是yolov8实现知识蒸馏的两个例子：

1. yolov8 logit蒸馏[^1]

# 设置蒸馏参数
teacher_model = Yolov8(classes=NUM_CLASSES, weights=TEACHER_WEIGHTS)
student_model = Yolov8Tiny(classes=NUM_CLASSES, weights=None)
logit_config = DistillConfig(student_model=student_model,
                                  teacher_model=teacher_model,
                                  train_ds=train_ds,
                                  val_ds=val_ds,
                                  epochs=20,
                                  steps_per_epoch=2000,
                                  distill_type='logit',
                                  alpha=0.1)

# 开始训练
logit_trainer = DistillTrainer(logit_config)
logit_trainer.train()

# 评估模型
metrics = student_model.evaluate(val_ds)
print(f"student model evaluate metrics: {metrics}")

2. yolov8 feature-based蒸馏

# 设置蒸馏参数
teacher_model = Yolov8(classes=NUM_CLASSES, weights=TEACHER_WEIGHTS)
student_model = Yolov8Tiny(classes=NUM_CLASSES, weights=None)
feature_config = DistillConfig(student_model=student_model,
                                    teacher_model=teacher_model,
                                    train_ds=train_ds,
                                    val_ds=val_ds,
                                    epochs=20,
                                    steps_per_epoch=2000,
                                    distill_type='feature',
                                    alpha=0.1,
                                    distill_layers=[8, 16, 32])

# 开始训练
feature_trainer = DistillTrainer(feature_config)
feature_trainer.train()

# 评估模型
metrics = student_model.evaluate(val_ds)
print(f"student model evaluate metrics: {metrics}")