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mixed-depthwise-convolutional-kernels's Introduction

Implementing Mixed-Depthwise-Convolutional-Kernels using Pytorch (22 Jul 2019)

  • Author:
    • Mingxing Tan (Google Brain)
    • Quoc V. Le (Google Brain)
  • Paper Link

Method

캑처

  • 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.

Experiment

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

Usage

python main.py
  • --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

Todo

  • Distributed SGD
  • ImageNet experiment

Reference

mixed-depthwise-convolutional-kernels's People

Contributors

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mixed-depthwise-convolutional-kernels's Issues

Error

Hi @leaderj1001 ,
I have an error when i tested your model with input_size is an even multiple of 32 such as (512 x 512, 448 x 448) but don't have this error with input_size is an odd multiple of 32 like 224x 224, 416 x 416.
How can I fix that ?
image

Reproduce Paper Results

Hello, thanks for the repo! Are you able to train with this repo to similar results as the published paper?

Training MixNet models for Cifar 10

Hello,

Have you trained MixNet models for Cifar-10 from scratch (not transfer learning from ImageNet models)?

With default configuration, I obtain about 70% accuracy for MixNet small, and the accuracy decreases as the model size increases. Do I need to change some particular parameters in the configuration while training Cifar-10?

Thank you.

BUG

I tried your code but i have an error:
image
and when i summary your model with summaryX i get an error
image
How can I fix it ?

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