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# Training FCN-Alexnet with DIGITS When the previous data import job is complete, return to the DIGITS home screen. Select the `Models` tab and choose to create a new `Segmentation Model` from the drop-down:

In the model creation form, select the dataset you previously created. Set `Subtract Mean` to None and the `Base Learning Rate` to `0.0001`. To set the network topology in DIGITS, select the `Custom Network` tab and make sure the `Caffe` sub-tab is selected. Copy/paste the **[FCN-Alexnet prototxt](https://raw.githubusercontent.com/NVIDIA/DIGITS/master/examples/semantic-segmentation/fcn_alexnet.prototxt)** into the text box. Finally, set the `Pretrained Model` to the output that the `net_surgery` generated above: `DIGITS/examples/semantic-segmentation/fcn_alexnet.caffemodel` ![Alt text](https://github.com/dusty-nv/jetson-inference/raw/master/docs/images/segmentation-digits-aerial-model-options.png) Give your aerial model a name and click the `Create` button at the bottom of the page to start the training job. After about 5 epochs, the `Accuracy` plot (in orange) should ramp up and the model becomes usable: ![Alt text](https://github.com/dusty-nv/jetson-inference/raw/master/docs/images/segmentation-digits-aerial-model-converge.png) At this point, we can try testing our new model's inference on some example images in DIGITS. ### Testing Inference Model in DIGITS Before transfering the trained model to Jetson, let's test it first in DIGITS. On the same page as previous plot, scroll down under the `Trained Models` section. Set the `Visualization Model` to *Image Segmentation* and under `Test a Single Image`, select an image to try (for example `/NVIDIA-Aerial-Drone-Dataset/FPV/SFWA/720p/images/0428.png`):

Press `Test One` and you should see a display similar to: ![Alt text](https://github.com/dusty-nv/jetson-inference/raw/master/docs/images/segmentation-digits-aerial-infer.png) Next, download and extract the trained model snapshot to Jetson and proceed to the next step. ##

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