diff --git a/docs/digits-setup.md b/docs/digits-setup.md
index 7e6afd02f..fe45c33a5 100644
--- a/docs/digits-setup.md
+++ b/docs/digits-setup.md
@@ -88,11 +88,11 @@ Add the Docker Engine Utility (nvidia-docker2) repository, install nvidia-docker
 ``` bash
 $ curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | \
   sudo apt-key add -
-$ ccurl -s -L https://nvidia.github.io/nvidia-docker/ubuntu16.04/amd64/nvidia-docker.list | \
+$ curl -s -L https://nvidia.github.io/nvidia-docker/ubuntu16.04/amd64/nvidia-docker.list | \
   sudo tee /etc/apt/sources.list.d/nvidia-docker.list
-$ csudo apt-get update
-$ csudo apt-get install -y nvidia-docker2
-$ csudo usermod -aG docker $USER
+$ sudo apt-get update
+$ sudo apt-get install -y nvidia-docker2
+$ sudo usermod -aG docker $USER
 $ sudo reboot
 ```
 
@@ -140,7 +140,7 @@ $ mkdir /home/username/digits-jobs
 
 ``` bash
 $ nvidia-docker run --name digits -d -p 8888:5000 \
- -v /home/username/data:/data:ro
+ -v /home/username/data:/data:ro \
  -v /home/username/digits-jobs:/workspace/jobs nvcr.io/nvidia/digits:18.05
 ```
 
diff --git a/docs/segnet-console.md b/docs/segnet-console.md
index 8d20a59d6..a7b11ac84 100644
--- a/docs/segnet-console.md
+++ b/docs/segnet-console.md
@@ -16,8 +16,8 @@ $ ./segnet-console drone_0428.png output_0428.png \
 --prototxt=$NET/deploy.prototxt \
 --model=$NET/snapshot_iter_22610.caffemodel \
 --labels=$NET/fpv-labels.txt \
---colors=$NET/fpv-deploy-colors.txt \
---input_blob=data \ 
+--colors=$NET/fpv-training-colors.txt \
+--input_blob=data \
 --output_blob=score_fr
 ```
 
diff --git a/docs/segnet-patches.md b/docs/segnet-patches.md
index 29f064d26..91331d1c2 100644
--- a/docs/segnet-patches.md
+++ b/docs/segnet-patches.md
@@ -44,7 +44,7 @@ layer {
 
 And on line 24 of `deploy.prototxt`, change `pad: 100` to `pad: 0`.  
 
-Finally, copy the `fpv-labels.txt` and `fpv-deploy-colors.txt` from the aerial dataset to your model snapshot folder on Jetson.  Your FCN-Alexnet model snapshot is now compatible with TensorRT.  Now we can run it on Jetson and perform inference on images.
+Finally, copy the `fpv-labels.txt` and `fpv-training-colors.txt` from the aerial dataset to your model snapshot folder on Jetson.  Your FCN-Alexnet model snapshot is now compatible with TensorRT.  Now we can run it on Jetson and perform inference on images.
 
 ##
 <p align="right">Next | <b><a href="segnet-console.md">Running Segmentation Models on Jetson</a></b>