diff --git a/viscy/translation/engine.py b/viscy/translation/engine.py
index 66e18f8b..5c49aa10 100644
--- a/viscy/translation/engine.py
+++ b/viscy/translation/engine.py
@@ -1,5 +1,6 @@
 import logging
 import os
+import random
 from typing import Literal, Sequence, Union
 
 import numpy as np
@@ -9,7 +10,7 @@
 from lightning.pytorch import LightningModule
 from monai.data.utils import collate_meta_tensor
 from monai.optimizers import WarmupCosineSchedule
-from monai.transforms import Compose, DivisiblePad, Rotate90
+from monai.transforms import DivisiblePad, Rotate90
 from torch import Tensor, nn
 from torch.optim.lr_scheduler import ConstantLR
 from torchmetrics.functional import (
@@ -472,14 +473,10 @@ class FcmaeUNet(VSUNet):
     def __init__(
         self,
         fit_mask_ratio: float = 0.0,
-        train_transforms=[],
-        validation_transforms=[],
         **kwargs,
     ):
         super().__init__(architecture="fcmae", **kwargs)
         self.fit_mask_ratio = fit_mask_ratio
-        self.train_transforms = Compose(train_transforms)
-        self.validation_transforms = Compose(validation_transforms)
         self.save_hyperparameters()
 
     def on_fit_start(self):
@@ -512,6 +509,8 @@ def train_transform_and_collate(self, batch: list[dict[Sample]]) -> Tensor:
         for dataset_batch, dm in zip(batch, self.datamodules):
             dataset_batch = dm.train_gpu_transforms(dataset_batch)
             transformed.extend(dataset_batch)
+        # shuffle references in place for better logging
+        random.shuffle(transformed)
         return collate_meta_tensor(transformed)["source"]
 
     @torch.no_grad()
@@ -525,10 +524,9 @@ def training_step(self, batch: list[list[Sample]], batch_idx: int) -> Tensor:
         x = self.train_transform_and_collate(batch)
         pred, mask, loss = self.forward_fit(x)
         if batch_idx < self.log_batches_per_epoch:
+            target = x * mask.unsqueeze(2)
             self.training_step_outputs.extend(
-                detach_sample(
-                    (x, x * mask.unsqueeze(2), pred), self.log_samples_per_batch
-                )
+                detach_sample((x, target, pred), self.log_samples_per_batch)
             )
         self.log(
             "loss/train",