diff --git a/dacapo/experiments/trainers/gunpowder_trainer.py b/dacapo/experiments/trainers/gunpowder_trainer.py
index 72427951e..f5d8fcd52 100644
--- a/dacapo/experiments/trainers/gunpowder_trainer.py
+++ b/dacapo/experiments/trainers/gunpowder_trainer.py
@@ -37,8 +37,6 @@ def __init__(self, trainer_config):
         self.print_profiling = 100
         self.snapshot_iteration = trainer_config.snapshot_interval
         self.min_masked = trainer_config.min_masked
-        self.reject_probability = trainer_config.reject_probability
-        self.weighted_reject = trainer_config.weighted_reject
 
         self.augments = trainer_config.augments
         self.mask_integral_downsample_factor = 4
@@ -53,14 +51,12 @@ def __init__(self, trainer_config):
 
     def create_optimizer(self, model):
         optimizer = torch.optim.RAdam(lr=self.learning_rate, params=model.parameters())
-        self.scheduler = (
-            torch.optim.lr_scheduler.LinearLR(  # TODO: add scheduler to config
-                optimizer,
-                start_factor=0.01,
-                end_factor=1.0,
-                total_iters=1000,
-                last_epoch=-1,
-            )
+        self.scheduler = torch.optim.lr_scheduler.LinearLR(
+            optimizer,
+            start_factor=0.01,
+            end_factor=1.0,
+            total_iters=1000,
+            last_epoch=-1,
         )
         return optimizer
 
@@ -69,9 +65,7 @@ def build_batch_provider(self, datasets, model, task, snapshot_container=None):
         output_shape = Coordinate(model.output_shape)
 
         # get voxel sizes
-        raw_voxel_size = datasets[
-            0
-        ].raw.voxel_size  # TODO: make dataset specific / resample
+        raw_voxel_size = datasets[0].raw.voxel_size
         prediction_voxel_size = model.scale(raw_voxel_size)
 
         # define input and output size:
@@ -91,7 +85,7 @@ def build_batch_provider(self, datasets, model, task, snapshot_container=None):
         mask_placeholder = gp.ArrayKey("MASK_PLACEHOLDER")
 
         target_key = gp.ArrayKey("TARGET")
-        dataset_weight_key = gp.ArrayKey("DATASET_WEIGHT")  # TODO: put these back in
+        dataset_weight_key = gp.ArrayKey("DATASET_WEIGHT")
         datasets_weight_key = gp.ArrayKey("DATASETS_WEIGHT")
         weight_key = gp.ArrayKey("WEIGHT")
         sample_points_key = gp.GraphKey("SAMPLE_POINTS")
@@ -99,7 +93,7 @@ def build_batch_provider(self, datasets, model, task, snapshot_container=None):
         # Get source nodes
         dataset_sources = []
         weights = []
-        for dataset in datasets:  # TODO: add automatic resampling?
+        for dataset in datasets:
             weights.append(dataset.weight)
             assert isinstance(dataset.weight, int), dataset
 
@@ -152,30 +146,10 @@ def build_batch_provider(self, datasets, model, task, snapshot_container=None):
                 )
             )
 
-            if self.weighted_reject:
-                # Add predictor nodes to dataset_source
-                for augment in self.augments:
-                    dataset_source += augment.node(raw_key, gt_key, mask_key)
+            dataset_source += gp.Reject(mask_placeholder, 1e-6)
 
-                dataset_source += DaCapoTargetFilter(
-                    task.predictor,
-                    gt_key=gt_key,
-                    weights_key=weight_key,
-                    target_key=target_key,
-                    mask_key=mask_key,
-                )
-
-                dataset_source += gp.Reject(
-                    mask=weight_key,
-                    min_masked=self.min_masked,
-                    reject_probability=self.reject_probability,
-                )
-            else:
-                dataset_source += gp.Reject(
-                    mask=mask_placeholder,
-                    min_masked=self.min_masked,
-                    reject_probability=self.reject_probability,
-                )
+            for augment in self.augments:
+                dataset_source += augment.node(raw_key, gt_key, mask_key)
 
             if self.add_predictor_nodes_to_dataset:
                 # Add predictor nodes to dataset_source
@@ -290,7 +264,7 @@ def iterate(self, num_iterations, model, optimizer, device):
                 }
                 if mask is not None:
                     snapshot_arrays["volumes/mask"] = mask
-                logger.info(
+                logger.warning(
                     f"Saving Snapshot. Iteration: {iteration}, "
                     f"Loss: {loss.detach().cpu().numpy().item()}!"
                 )
diff --git a/dacapo/experiments/trainers/gunpowder_trainer_config.py b/dacapo/experiments/trainers/gunpowder_trainer_config.py
index 255c73ad6..539e3c5e1 100644
--- a/dacapo/experiments/trainers/gunpowder_trainer_config.py
+++ b/dacapo/experiments/trainers/gunpowder_trainer_config.py
@@ -28,7 +28,7 @@ class GunpowderTrainerConfig(TrainerConfig):
         metadata={"help_text": "Number of iterations before saving a new snapshot."},
     )
     min_masked: Optional[float] = attr.ib(default=0.15)
-    clip_raw: bool = attr.ib(default=False)
+    clip_raw: bool = attr.ib(default=True)
 
     add_predictor_nodes_to_dataset: Optional[bool] = attr.ib(
         default=True,