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When I test level1, I found that using 1_F only is better than using 1_F + 1_EN + 1_NM. By the way, the loss of 1_EN and 1_NM is also pretty low. If I use 1_F only for the first level, I got reasonable good result from level3... I just can't understand why 1_F only would be better than blending of three networks.
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So, I didn't train model 1_EN and 1_NM. Maybe the processing part of 1_EN or 1_NM are not good or still need some training trick to train these two model well.
When I test level1, I found that using 1_F only is better than using 1_F + 1_EN + 1_NM. By the way, the loss of 1_EN and 1_NM is also pretty low. If I use 1_F only for the first level, I got reasonable good result from level3... I just can't understand why 1_F only would be better than blending of three networks.
The text was updated successfully, but these errors were encountered: