About the training process about confusion HOT 3 CLOSED

Oh I got the reason. However, which layer should be written in the parameter of --confusion?

from confusion.

emmm... And where do I modify the regularization parameter λ？

from confusion.

Hi @abcdvzz, you should use the confusion typically on the last (logit) layer of the network, but it should provide moderate improvements (with very small λ of around 10e-4 to 10e-5) for intermediate layers as well.

The formula explored in the ECCV paper can be replicated with the command:

./train.py .... --confusion '{"final_layer_name": 0.01}' 

The parameters to the --confusion argument are basically the layer name and the weight (λ), which in this case are final_layer_name and 0.01. The README already covers this usage.

from confusion.