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Code training used in the process:
loss = multiscaleEPE(output, target, weights=args.multiscale_weights, sparse=args.sparse)
flow2_EPE = args.div_flow * realEPE(output[0], target, sparse=args.sparse)
I can't understand the loss function you designed, could you please explain it?
The text was updated successfully, but these errors were encountered:
Actually the EPE is not designed by author, it is a common index just like RMSE. In original Paper, the author writes"Training loss we use the endpoint error the Euclidean distance between the predicted flow vector and the ground truth, averaged over all pixels" That is.
Code training used in the process:
loss = multiscaleEPE(output, target, weights=args.multiscale_weights, sparse=args.sparse)
flow2_EPE = args.div_flow * realEPE(output[0], target, sparse=args.sparse)
I can't understand the loss function you designed, could you please explain it?
The text was updated successfully, but these errors were encountered: