diff --git a/README.md b/README.md
index d5330e9..9fb1946 100644
--- a/README.md
+++ b/README.md
@@ -60,7 +60,7 @@ ai_VVV_NNN
 │       └── metadata_semantic_instance_bounding_box_object_aligned_2d_positions.hdf5    # position (in asset coordinates) of the 3D bounding box for each semantic instance ID
 └── images
     ├── scene_cam_XX_final_hdf5                  # lossless HDR image data that requires accurate shading
-    │   ├── frame.IIII.color.hdf5                # color image before any tonemapping has been applied
+    │   ├── frame.IIII.color.hdf5                # color image before any tone mapping has been applied
     │   ├── frame.IIII.diffuse_illumination.hdf5 # diffuse illumination
     │   ├── frame.IIII.diffuse_reflectance.hdf5  # diffuse reflectance (many authors refer to this modality as "albedo")
     │   ├── frame.IIII.residual.hdf5             # non-diffuse residual
@@ -110,7 +110,7 @@ Our `color`, `diffuse_illumination`, `diffuse_reflectance`, and `residual` image
 color == (diffuse_reflectance * diffuse_illumination) + residual
 ```
 
-Note that our `color`, `diffuse_illumination`, `diffuse_reflectance`, and `residual` images do not have any tonemapping applied to them. In order to use these images for downstream learning tasks, we recommend applying your own tonemapping operator to the images. We implement a simple tonemapping operator in `ml-hypersim/code/python/tools/scene_generate_images_tonemap.py`.
+Note that our `color`, `diffuse_illumination`, `diffuse_reflectance`, and `residual` images do not have any tone mapping applied to them. In order to use these images for downstream learning tasks, we recommend applying your own tone mapping operator to the images. We implement a simple tone mapping operator in `ml-hypersim/code/python/tools/scene_generate_images_tonemap.py`.
 
 ### Lossy preview images
 
diff --git a/examples/01_marketplace_dataset/README.md b/examples/01_marketplace_dataset/README.md
index c16a20b..8ba9f9a 100644
--- a/examples/01_marketplace_dataset/README.md
+++ b/examples/01_marketplace_dataset/README.md
@@ -438,7 +438,7 @@ The `dataset_render_scene.py` tool generates images in `my_dataset/scenes/my_sce
  
 ## Post-processing the rendering output (optional)
 
-After the rendering output has been generated, we can perform several useful post-processing operations. First, we can tonemap the rendered HDR color data into LDR. Second, if we have labeled our scene using the Hypersim Scene Annotation Tool (located at `ml-hypersim/code/cpp/bin/scene_annotation_tool`), we can generate semantic segmentation images. The scene in this tutorial example has already been labeled, and the segmentation data has been checked in, so we can generate segmentation images without needing to label the scene manually. Third, we can generate animations of our rendered output.
+After the rendering output has been generated, we can perform several useful post-processing operations. First, we can tone-map the rendered HDR color data into LDR. Second, if we have labeled our scene using the Hypersim Scene Annotation Tool (located at `ml-hypersim/code/cpp/bin/scene_annotation_tool`), we can generate semantic segmentation images. The scene in this tutorial example has already been labeled, and the segmentation data has been checked in, so we can generate segmentation images without needing to label the scene manually. Third, we can generate animations of our rendered output.
 
 ```
 # generate tone-mapped images for visualization