the value of MarginInnerProduct param about sphereface HOT 2 OPEN

The training accuracy in the training protext is not the true accuracy of the model. Because when you are training the network with A-Softmax (say with m=4), you are actually using a harder classification rule than the standard classification. The network will classify the sample correcly only if the sample are achieving a large margin criterion, but in fact, the sample can be also successfully classified in testing even if it does not fully satisfy the m=4 large margin criterion.

If you want to show the training accuracy, you might need to modify the computation of the training accuracy in the training protext file in order to make it comparable with the computation of the testing accuracy.

Overall, it is unlikely that A-Softmax loss will reduce the classification accuracy (if you train it successfully).

from sphereface.

The training accuracy in the training protext is not the true accuracy of the model. Because when you are training the network with A-Softmax (say with m=4), you are actually using a harder classification rule than the standard classification. The network will classify the sample correcly only if the sample are achieving a large margin criterion, but in fact, the sample can be also successfully classified in testing even if it does not fully satisfy the m=4 large margin criterion.

If you want to show the training accuracy, you might need to modify the computation of the training accuracy in the training protext file in order to make it comparable with the computation of the testing accuracy.

Overall, it is unlikely that A-Softmax loss will reduce the classification accuracy (if you train it successfully).

Thank you for your reply！
I am still confused about how to set the values of MarginInnerProduct parameters(such as base and gamma) base on my data(such as number of categories and number of samples）.

from sphereface.