diff --git a/gaussian_splatting/local_trainer.py b/gaussian_splatting/local_trainer.py
index d3d87bd63..889d4dfb6 100644
--- a/gaussian_splatting/local_trainer.py
+++ b/gaussian_splatting/local_trainer.py
@@ -1,4 +1,5 @@
 import math
+from matplotlib import pyplot as plt
 import numpy as np
 from transformers import pipeline
 from tqdm import tqdm
@@ -23,11 +24,10 @@ def __init__(self, image, sh_degree: int = 3):
         depth_estimation = DPT(image)["predicted_depth"]
 
         image = PILtoTorch(image)
-
         initial_point_cloud = self._get_initial_point_cloud(
             image,
             depth_estimation,
-            step=10
+            step=25
         )
 
         self.gaussian_model = GaussianModel(sh_degree)
@@ -38,10 +38,11 @@ def __init__(self, image, sh_degree: int = 3):
 
         self._camera = self._get_orthogonal_camera(image)
 
-        self._iterations = 1000
+        self._iterations = 10000
         self._lambda_dssim = 0.2
 
-        self._opacity_reset_interval = 1000
+        # Densification and pruning
+        self._opacity_reset_interval = 10001
         self._min_opacity = 0.005
         self._max_screen_size = 20
         self._percent_dense = 0.01
@@ -58,13 +59,20 @@ def run(self):
         progress_bar = tqdm(
             range(self._iterations), desc="Training progress"
         )
+
+        best_loss, best_iteration, losses = None, 0, []
         for iteration in range(self._iterations):
-            # self.optimizer.update_learning_rate(iteration)
+            self.optimizer.update_learning_rate(iteration)
             rendered_image, viewspace_point_tensor, visibility_filter, radii = render(
                 self._camera, self.gaussian_model
             )
 
             if iteration % 100 == 0:
+                plt.cla()
+                plt.plot(losses)
+                plt.yscale('log')
+                plt.savefig('artifacts/losses.png')
+
                 torchvision.utils.save_image(rendered_image, f"artifacts/rendered_{iteration}.png")
 
             gt_image = self._camera.original_image.cuda()
@@ -72,6 +80,10 @@ def run(self):
             loss = (1.0 - self._lambda_dssim) * Ll1 + self._lambda_dssim * (
                 1.0 - ssim(rendered_image, gt_image)
             )
+            if best_loss is None or best_loss > loss:
+                best_loss = loss.cpu().item()
+                best_iteartion = iteration
+            losses.append(loss.cpu().item())
 
             loss.backward()
 
@@ -100,10 +112,13 @@ def run(self):
 
             progress_bar.set_postfix({
                 "Loss": f"{loss:.{5}f}",
-                "Num_visible": f"{visibility_filter.int().sum().item()}"
+                "Num_visible":
+                f"{visibility_filter.int().sum().item()}/{len(visibility_filter)}"
             })
             progress_bar.update(1)
 
+        print(f"Training done. Best loss = {best_loss} at iteration {best_iteration}.")
+
         torchvision.utils.save_image(rendered_image, f"artifacts/rendered_{iteration}.png")
         torchvision.utils.save_image(gt_image, f"artifacts/gt.png")
 
diff --git a/gaussian_splatting/trainer.py b/gaussian_splatting/trainer.py
index 9a4b43f1a..b06de44e6 100644
--- a/gaussian_splatting/trainer.py
+++ b/gaussian_splatting/trainer.py
@@ -220,7 +220,9 @@ def _densify_and_prune(self, prune_big_points):
         # Prune transparent and large gaussians.
         prune_mask = (self.gaussian_model.get_opacity < self._min_opacity).squeeze()
         if prune_big_points:
+            # Viewspace
             big_points_vs = self.gaussian_model.max_radii2D > self._max_screen_size
+            # World space
             big_points_ws = (
                 self.gaussian_model.get_scaling.max(dim=1).values
                 > 0.1 * self.gaussian_model.camera_extent
diff --git a/scripts/train_colmap_free.py b/scripts/train_colmap_free.py
index 251946dea..dd11d9047 100644
--- a/scripts/train_colmap_free.py
+++ b/scripts/train_colmap_free.py
@@ -12,7 +12,5 @@ def main():
     local_trainer.run()
 
 
-    import pdb; pdb.set_trace()
-
 if __name__ == "__main__":
     main()