Back | Next | Contents
Semantic Segmentation
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 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
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:
At this point, we can try testing our new model's inference on some example images 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:
Next, download and extract the trained model snapshot to Jetson and proceed to the next step.
Next | FCN-Alexnet Patches for TensorRT
Back | Generating Pretrained FCN-Alexnet
© 2016-2019 NVIDIA | Table of Contents