Hi @zs-zhong ,
Have you tried 90 epochs training with mixup on ImageNet or iNaturalist ?
I have made some improvements based on your work, but due to the lack of computing resources, training a model for 180/200 epochs is too time-consuming for me, especially for iNaturalist.
In my reproduction, under the condition of training 90 epochs with mixup (alpha 0.2) on ImageNet-LT, epochs of stage-2 is 10, the accuracy of methods with ResNet-50 are as follows:
|
Stage-1 |
mixup |
Stage-2 |
cRT |
LWS |
| Reported in Decouple |
90 epochs |
|
10 epochs |
47.3 |
47.7 |
| My Reproduce |
90 epochs |
|
10 epochs |
48.7 |
49.3 |
| My Reproduce |
90 epochs |
✅ |
10 epochs |
47.6 |
47.4 |
| My Reproduce |
180 epochs |
|
10 epochs |
51.0 |
51.8 |
| Reported in MiSLAS |
180 epochs |
|
10 epochs |
50.3 |
51.2 |
| Reported in MiSLAS |
180 epochs |
✅ |
10 epochs |
51.7 |
52.0 |
They look much worse than the model trained for 180 epochs with mixup, and it does not even have improvement compared to normal training.
I guess this is because mixup could be regarded as a regularization method, which requires longer training epochs, 90 epochs cannot make the network converge.
However, I cannot get the result of using mixup to train 90 epochs on the iNaturalist data set, because the iNaturalist data set is too large and I can't put it in the memory, which makes it take about a week for me to train R50 once.
If possible, could you please provide the pre-trained ResNet-50 model for training 90 epochs with mixup on iNaturalist? I believe this will also be beneficial for fair comparison of future work.
Thank you again for your contribution and look forward to your reply.
Hi @zs-zhong ,
Have you tried 90 epochs training with mixup on ImageNet or iNaturalist ?
I have made some improvements based on your work, but due to the lack of computing resources, training a model for 180/200 epochs is too time-consuming for me, especially for iNaturalist.
In my reproduction, under the condition of training 90 epochs with mixup (alpha 0.2) on ImageNet-LT, epochs of stage-2 is 10, the accuracy of methods with ResNet-50 are as follows:
They look much worse than the model trained for 180 epochs with mixup, and it does not even have improvement compared to normal training.
I guess this is because mixup could be regarded as a regularization method, which requires longer training epochs, 90 epochs cannot make the network converge.
However, I cannot get the result of using mixup to train 90 epochs on the iNaturalist data set, because the iNaturalist data set is too large and I can't put it in the memory, which makes it take about a week for me to train R50 once.
If possible, could you please provide the pre-trained ResNet-50 model for training 90 epochs with mixup on iNaturalist? I believe this will also be beneficial for fair comparison of future work.
Thank you again for your contribution and look forward to your reply.