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Varios Typos / fixes for tutorial #631

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499422d
Typos / fixes digitis-setup.md
aussiemaverick Jul 1, 2020
53c0810
typos to segnet-console to fix segmentation fault
aussiemaverick Jul 1, 2020
152f7d1
Update segnet-patches.md
aussiemaverick Jul 1, 2020
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10 changes: 5 additions & 5 deletions docs/digits-setup.md
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Original file line number Diff line number Diff line change
Expand Up @@ -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
```

Expand Down Expand Up @@ -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
```

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4 changes: 2 additions & 2 deletions docs/segnet-console.md
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Expand Up @@ -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
```

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2 changes: 1 addition & 1 deletion docs/segnet-patches.md
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Expand Up @@ -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>
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