How do I design models for multiple labels?

1

train data’ shape is (3, 224, 224)

label is total 5

label 1 = (0~5)
label 2 = (0~7)
label 3 = (0~3)
label 4 = (0~2)
label 5 = (0~8)

so i mapped

example))
image <-> [1, 7, 3, 1, 4]

and

model = models.resnet50(pretrained=False)
in_features = model.fc.in_features
model.fc = nn.Linear(in_features=in_features, out_features=5)
model.to(device)
print(model)

Is it right to design this?

I have no idea how to set the loss and model if there are multiple such labels using Resnet50.

2

One idea is to work with hot vectors instead. So basically, your last fc layer will be connected to 5 different outputs such that :

  • output 1 is a 6 element vector that represents the first label. If your label is 3 for example then you vector would be [0, 0, 0, 1, 0, 0]
  • output 2 is 8 element vector.
    .
    . and so on

Once you have these representations, you apply CrossEntropyLoss on each one of your outputs. And your final loss is the sum of all these 5 losses.
You can also weight your loss labels if you think one label is more important than the other.