diff --git a/torch_em/trainer/wandb_logger.py b/torch_em/trainer/wandb_logger.py
index 06e5fba3..b312d710 100644
--- a/torch_em/trainer/wandb_logger.py
+++ b/torch_em/trainer/wandb_logger.py
@@ -26,7 +26,7 @@ def __init__(self, trainer):
 
         wandb.watch(trainer.model)
 
-    def _log_images(self, x, y, prediction, name, gradients=None):
+    def _log_images(self, step, x, y, prediction, name, gradients=None):
 
         selection = np.s_[0] if x.ndim == 4 else np.s_[0, :, x.shape[2] // 2]
 
@@ -35,7 +35,7 @@ def _log_images(self, x, y, prediction, name, gradients=None):
 
         # to numpy and channel last
         image = image.numpy().transpose((1, 2, 0))
-        wandb.log({f"images_{name}/input": [wandb.Image(image, caption='Input Data')]})
+        wandb.log({f"images_{name}/input": [wandb.Image(image, caption='Input Data')]}, step=step)
 
         grid_image = grid_image.numpy().transpose((1, 2, 0))
 
@@ -43,18 +43,18 @@ def _log_images(self, x, y, prediction, name, gradients=None):
         if gradients is not None:
             grid_name += '_gradients'
 
-        wandb.log({f"images_{name}/{grid_name}": [wandb.Image(grid_image, caption=grid_name)]})
+        wandb.log({f"images_{name}/{grid_name}": [wandb.Image(grid_image, caption=grid_name)]}, step=step)
 
     def log_train(self, step, loss, lr, x, y, prediction, log_gradients=False):
-        wandb.log({"train/loss": loss})
+        wandb.log({"train/loss": loss}, step=step)
         if step % self.log_image_interval == 0:
             gradients = prediction.grad if log_gradients else None
-            self._log_images(x, y, prediction, 'train',
+            self._log_images(step, x, y, prediction, 'train',
                              gradients=gradients)
 
     def log_validation(self, step, metric, loss, x, y, prediction):
-        wandb.log({"validation/loss": loss, "validation/metric": metric})
-        self._log_images(x, y, prediction, 'validation')
+        wandb.log({"validation/loss": loss, "validation/metric": metric}, step=step)
+        self._log_images(step, x, y, prediction, "validation")
 
     def get_wandb(self):
         return wandb