Cyclic Loss (distance between classes)

1

Hi there,

I am getting started with PyTorch, and I attempting to fine-tune a network to classify containing orientation data (pictures of pedestrians mainly). In particular, I clustered orientation into 24 bins, each of these belonging to a portion of 15º (from 0 to 360), and I have the true labels for all pedestrians to train the classifier. In particular, the last layer of my model is a FC layer with a 1x24 output.

By using a cross-entropy loss the obtained results are acceptable, but atm I am not taking into account the distance between classes: i.e. the bin number 0 (from 0-15º) is close to the bin number 1 (15-30º), but it is also close to the 23rd bin (345-360º).

My question is: is there any kind of implemented loss function that can take into account this cyclic feature and uses the distance between classes?
Thanks,

2

Hello,

You could use modulo arithmetic to calculate distance between classes

c1 = (x - y)%23
c2 = (y-x)%23
min_dist = min(c1,c2)

Since you’re comparing orientations, you might be able to use cosine similarity directly without binning.

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