I trained the network on 38 classes. but when i test the model, i tried printing the np.unique(pan) from the output and it has class ids greater than 38 so when i try to feed the sem to color generator, it throws an error since there are only 38 classes in the gt.
how do i fix this
following is how the np.unique(pan) looks like. all the unique pans after 37 do not make any sense as these classes do not exist in the training
[ 2 3 4 10 14 15 18 20 22 24 25 26 27 28 32 33 34 35
36 37 38 39 40 41 42 43 44 45 46 47 48 49 255]
[ 2 3 4 10 14 15 18 20 22 24 25 26 27 28 32 33 34 35
36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 255]
[ 2 3 4 9 10 14 18 20 22 23 24 25 26 27 28 32 33 34
35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 255]
[ 2 3 4 10 14 18 20 22 23 24 25 26 27 28 32 33 34 35
36 37 38 39 40 41 42 43 44 45 46 47 48 255]
I trained the network on 38 classes. but when i test the model, i tried printing the np.unique(pan) from the output and it has class ids greater than 38 so when i try to feed the sem to color generator, it throws an error since there are only 38 classes in the gt. how do i fix this
following is how the np.unique(pan) looks like. all the unique pans after 37 do not make any sense as these classes do not exist in the training [ 2 3 4 10 14 15 18 20 22 24 25 26 27 28 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 255] [ 2 3 4 10 14 15 18 20 22 24 25 26 27 28 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 255] [ 2 3 4 9 10 14 18 20 22 23 24 25 26 27 28 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 255] [ 2 3 4 10 14 18 20 22 23 24 25 26 27 28 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 255]