diff --git a/src/deepforest/main.py b/src/deepforest/main.py
index 8e55ed92..a42a6752 100644
--- a/src/deepforest/main.py
+++ b/src/deepforest/main.py
@@ -1,6 +1,8 @@
 # entry point for deepforest model
 import importlib
 import os
+import contextlib
+import io
 import typing
 import warnings
 
@@ -486,7 +488,8 @@ def predict_tile(self,
                      thickness=1,
                      crop_model=None,
                      crop_transform=None,
-                     crop_augment=False):
+                     crop_augment=False,
+                     verbose=True):
         """For images too large to input into the model, predict_tile cuts the
         image into overlapping windows, predicts trees on each window and
         reassambles into a single array.
@@ -507,19 +510,27 @@ def predict_tile(self,
             return_plot: return a plot of the image with predictions overlaid (deprecated)
             color: color of the bounding box as a tuple of BGR color (deprecated)
             thickness: thickness of the rectangle border line in px (deprecated)
-
+            verbose: whether to show progress bar
         Returns:
             pd.DataFrame or tuple: Predictions dataframe or (predictions, crops) tuple
         """
         self.model.eval()
         self.model.nms_thresh = self.config["nms_thresh"]
 
-        # if more than one GPU present, use only a the first available gpu
+        # if more than one GPU present, use only the first available gpu
         if torch.cuda.device_count() > 1:
-            # Get available gpus and regenerate trainer
             warnings.warn(
                 "More than one GPU detected. Using only the first GPU for predict_tile.")
             self.config["devices"] = 1
+
+        # Configure trainer based on verbose setting
+        if not verbose:
+            callbacks = [
+                cb for cb in self.trainer.callbacks
+                if not isinstance(cb, pl.callbacks.ProgressBar)
+            ]
+            self.create_trainer(enable_progress_bar=False, callbacks=callbacks)
+        else:
             self.create_trainer()
 
         if (raster_path is None) and (image is None):
@@ -551,6 +562,7 @@ def predict_tile(self,
                                        patch_overlap=patch_overlap,
                                        patch_size=patch_size)
 
+        # Predict using trainer
         batched_results = self.trainer.predict(self, self.predict_dataloader(ds))
 
         # Flatten list from batched prediction
@@ -560,11 +572,21 @@ def predict_tile(self,
                 results.append(boxes)
 
         if mosaic:
-            results = predict.mosiac(results,
-                                     ds.windows,
-                                     sigma=sigma,
-                                     thresh=thresh,
-                                     iou_threshold=iou_threshold)
+            # Suppress output if not verbose
+            if not verbose:
+                f = io.StringIO()
+                with contextlib.redirect_stdout(f):
+                    results = predict.mosiac(results,
+                                             ds.windows,
+                                             sigma=sigma,
+                                             thresh=thresh,
+                                             iou_threshold=iou_threshold)
+            else:
+                results = predict.mosiac(results,
+                                         ds.windows,
+                                         sigma=sigma,
+                                         thresh=thresh,
+                                         iou_threshold=iou_threshold)
             results["label"] = results.label.apply(
                 lambda x: self.numeric_to_label_dict[x])
             if raster_path:
diff --git a/tests/test_main.py b/tests/test_main.py
index c234aa83..d745bc94 100644
--- a/tests/test_main.py
+++ b/tests/test_main.py
@@ -906,4 +906,24 @@ def test_evaluate_on_epoch_interval(m):
     m.create_trainer()
     m.trainer.fit(m)
     assert m.trainer.logged_metrics["box_precision"]
-    assert m.trainer.logged_metrics["box_recall"]
\ No newline at end of file
+    assert m.trainer.logged_metrics["box_recall"]
+
+@pytest.mark.parametrize("verbose", [True, False])
+def test_predict_tile_verbose(m, raster_path, capsys, verbose):
+    """Test that verbose output can be controlled in predict_tile"""
+    m.config["train"]["fast_dev_run"] = False
+    m.create_trainer()
+
+    m.predict_tile(
+        raster_path=raster_path,
+        patch_size=300,
+        patch_overlap=0,
+        mosaic=True,
+        verbose=verbose
+    )
+
+    captured = capsys.readouterr()
+    if verbose:
+        assert captured.out.strip()
+    else:
+        assert not captured.out.strip()