Repository files navigation Implementing Mixed-Depthwise-Convolutional-Kernels using Pytorch (22 Jul 2019)
Author:
Mingxing Tan (Google Brain)
Quoc V. Le (Google Brain)
Paper Link
By using a multi scale kernel size, performance improvements and efficiency were obtained.
Each kernel size has a different receptive field, so we can get different feature maps for each kernel size.
Datasets
Model
Acc1
Acc5
Parameters (My Model, Paper Model)
CIFAR-10
MixNet-s (WORK IN PROCESS)
92.82%
99.79%
2.6M, -
CIFAR-10
MixNet-m (WORK IN PROCESS)
92.52%
99.78%
3.5M, -
CIFAR-10
MixNet-l (WORK IN PROCESS)
92.72%
99.79%
5.8M, -
IMAGENET
MixNet-s (WORK IN PROCESS)
4.1M, 4.1M
IMAGENET
MixNet-m (WORK IN PROCESS)
5.0M, 5.0M
IMAGENET
MixNet-l (WORK IN PROCESS)
7.3M, 7.3M
--data (str): the ImageNet dataset path
--dataset (str): dataset name, (example: CIFAR10, CIFAR100, MNIST, IMAGENET)
--batch-size (int)
--num-workers (int)
--epochs (int)
--lr (float): learning rate
--momentum (float): momentum
--weight-decay (float): weight dacay
--print-interval (int): training log print cycle
--cuda (bool)
--pretrained-model (bool): hether to use the pretrained model
Distributed SGD
ImageNet experiment
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Implementing MixNet: Mixed Depthwise Convolutional Kernels using Pytorch
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