On the exercise of segmentation with camvid dataset, some of the pixels on the solution have category of ‘void’, which don’t count while evaluating the accuracy.
If they mean nothing … Shouldn’t we avoid training the network on them?
The network will try to identify that ‘void’ category (after all, void is a category as any other) but that effort is meaningless and could potentially create a struggle for the network as it will try to resolve something that has no solution.
Would that avoided work help the network?
If so, how can we do it?
I tried to “see” where are those void pixels, and can understand why they are there (unimportant o difficult to categorize areas due to the distance or mix of materials).
# Extract the void pixels nvoid=mask.data==30 nvoid.size() # Because # mask.data.size() #-> torch.Size([1, 720, 960]) nvoid2=nvoid[0,:,:] nvoid2.size() # -> torch.Size([720, 960])
Here is a visual example
import matplotlib.pyplot as plt plt.imshow(nvoid2.numpy(), cmap='gray') img.show(figsize=(5,5)) mask.show(figsize=(5,5), alpha=1)