@CVPR2018: Efficient unrolling iterative matrix square-root normalized ConvNets, implemented by PyTorch (and code of B-CNN,Compact bilinear pooling etc.) for training from scratch & finetuning.
I tried on pretrained mpncovresnet101 & mpncovresnet50, the performance is impressive. But the performance is poor when I combined mpncov and efficient-net. I fixed the backbone parameters and only update params of reduce-layer and fc. I replace the layer_reduce_relu with Swish, and train it on imagenet2012 for 55 epochs, the top1 acc is only about 0.76.
I wonder why mpncov shows poor performance on a better backbone, any advice would be thankful!
Thanks for the great work.
I tried on pretrained mpncovresnet101 & mpncovresnet50, the performance is impressive. But the performance is poor when I combined mpncov and efficient-net. I fixed the backbone parameters and only update params of reduce-layer and fc. I replace the layer_reduce_relu with Swish, and train it on imagenet2012 for 55 epochs, the top1 acc is only about 0.76.
I wonder why mpncov shows poor performance on a better backbone, any advice would be thankful!