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In your code, you use the global feature with 256d to do classification, and the paper said they use global features before reduction which is 2048d. Is that a mistake? or you did that for a reason?And by the way, in your code , you use adam with lr 0.0002 which is also different from the paper. Can you tell me why,? In my attemption , it seems like your setting is better than what the paper says.
The text was updated successfully, but these errors were encountered:
Hello, I found the same problem. Have you tried to use the 2048-dimensional feature before dimension reduction to classify ? Why use the settings in the code, the cross entropy loss is difficult to converge when training
In your code, you use the global feature with 256d to do classification, and the paper said they use global features before reduction which is 2048d. Is that a mistake? or you did that for a reason?And by the way, in your code , you use adam with lr 0.0002 which is also different from the paper. Can you tell me why,? In my attemption , it seems like your setting is better than what the paper says.
The text was updated successfully, but these errors were encountered: