roads_test
project.roads_annotated
project.roads_annotated
). Thus, we got a training set of 220 images. We named the resulting project train_01
.train_01
project. We took UNetV2 architecture and init weights from VGG. You can choose any another neural network. We chose UNetV2 because it is fast to train and it is pretty accurate. As a result we got the nn_road_01
model.nn_road_01
model to the test images and put neural network predictions into the inf_01_roads_test
project. Road on many images is presegmented really nice, but there are the cases when NN fails.inf_01_roads_test
project (just to save the original predictions) to the project inf_01_annotated
, choose 5 images and correct the NN predictions manually. We tag corrected images with the corrected
tag.train_02
.nn_road_01
model and continue its training on the new project train_02
. As a result we get the nn_road_02
model.nn_road_02
model to our test images and put the neural network predictions into inf_02_roads_test
project. We compare these predictions with the previous ones and see that NN becomes smarter, presegmentation becomes better and that most images have ideal presegmentation.roads_test
project, and the images that we are going to annotate to the roads_annotated
project. Project roads_test
consists of 156 images, project roads_annotated
consists of 10 images.nn_road_01
. Project train_01
is used for training.nn_road_01
to the project roads_test
. The resulting project with neural network predictions will be saved as inf_01_roads_test
.inf_01_roads_test
to inf_01_annotated
just to save the original predictions and then compare them with the future predictions. We choose 5 images and correct them. Also we assign the tag "corrected" to them. Here they are:nn_road_01
model and continue its training on the new project train_02
. As a result we get the nn_road_02
model.nn_road_02
model to the roads_test
project and save the predictions to inf_02_roads_test
. Let's compare the predictions (project inf_01_roads_test
and project inf_02_roads_test
). New model has to be "smarter" then the previous one. Here we will show only the images, where model predictions became better. We revised all images and could not find any examples when it became worse.