diff --git a/SbSLRScheduler.py b/SbSLRScheduler.py
deleted file mode 100644
index ea81038..0000000
--- a/SbSLRScheduler.py
+++ /dev/null
@@ -1,106 +0,0 @@
-import torch
-
-
-class SbSLRScheduler(torch.optim.lr_scheduler.ReduceLROnPlateau):
-    def __init__(
-        self,
-        optimizer,
-        mode: str = "min",
-        factor: float = 0.1,
-        patience: int = 10,
-        threshold: float = 1e-4,
-        threshold_mode: str = "rel",
-        cooldown: int = 0,
-        min_lr: float = 0,
-        eps: float = 1e-8,
-        verbose: bool = False,
-        tau: float = 10,
-    ) -> None:
-
-        super().__init__(
-            optimizer=optimizer,
-            mode=mode,
-            factor=factor,
-            patience=patience,
-            threshold=threshold,
-            threshold_mode=threshold_mode,
-            cooldown=cooldown,
-            min_lr=min_lr,
-            eps=eps,
-            verbose=verbose,
-        )
-        self.lowpass_tau: float = tau
-        self.lowpass_decay_value: float = 1.0 - (1.0 / self.lowpass_tau)
-
-        self.lowpass_number_of_steps: int = 0
-        self.loss_maximum_over_time: float | None = None
-        self.lowpass_memory: float = 0.0
-        self.lowpass_learning_rate_minimum_over_time: float | None = None
-        self.lowpass_learning_rate_minimum_over_time_past_step: float | None = None
-
-        self.previous_learning_rate: float | None = None
-        self.loss_normalized_past_step: float | None = None
-
-    def step(self, metrics, epoch=None) -> None:
-
-        loss = float(metrics)
-
-        if self.loss_maximum_over_time is None:
-            self.loss_maximum_over_time = loss
-
-        if self.loss_normalized_past_step is None:
-            self.loss_normalized_past_step = loss / self.loss_maximum_over_time
-
-        if self.previous_learning_rate is None:
-            self.previous_learning_rate = self.optimizer.param_groups[-1]["lr"]  # type: ignore
-
-        # The parent lr scheduler controlls the basic learn rate
-        self.previous_learning_rate = self.optimizer.param_groups[-1]["lr"]  # type: ignore
-        super().step(metrics=self.loss_normalized_past_step, epoch=epoch)
-
-        # If the parent changes the base learning rate,
-        # then we reset the adaptive part
-        if self.optimizer.param_groups[-1]["lr"] != self.previous_learning_rate:  # type: ignore
-            self.previous_learning_rate = self.optimizer.param_groups[-1]["lr"]  # type: ignore
-
-            self.lowpass_number_of_steps = 0
-            self.loss_maximum_over_time = None
-            self.lowpass_memory = 0.0
-            self.lowpass_learning_rate_minimum_over_time = None
-            self.lowpass_learning_rate_minimum_over_time_past_step = None
-
-        if self.loss_maximum_over_time is None:
-            self.loss_maximum_over_time = loss
-        else:
-            self.loss_maximum_over_time = max(self.loss_maximum_over_time, loss)
-
-        self.lowpass_number_of_steps += 1
-
-        self.lowpass_memory = self.lowpass_memory * self.lowpass_decay_value + (
-            loss / self.loss_maximum_over_time
-        ) * (1.0 / self.lowpass_tau)
-
-        loss_normalized: float = self.lowpass_memory / (
-            1.0 - self.lowpass_decay_value ** float(self.lowpass_number_of_steps)
-        )
-
-        if self.lowpass_learning_rate_minimum_over_time is None:
-            self.lowpass_learning_rate_minimum_over_time = loss_normalized
-        else:
-            self.lowpass_learning_rate_minimum_over_time = min(
-                self.lowpass_learning_rate_minimum_over_time, loss_normalized
-            )
-
-        if self.lowpass_learning_rate_minimum_over_time_past_step is None:
-            self.lowpass_learning_rate_minimum_over_time_past_step = (
-                self.lowpass_learning_rate_minimum_over_time
-            )
-
-        self.optimizer.param_groups[-1]["lr"] *= (  # type: ignore
-            self.lowpass_learning_rate_minimum_over_time
-            / self.lowpass_learning_rate_minimum_over_time_past_step
-        )
-        self.lowpass_learning_rate_minimum_over_time_past_step = (
-            self.lowpass_learning_rate_minimum_over_time
-        )
-        self.loss_normalized_past_step = loss_normalized