Implementing MixNet: Mixed Depthwise Convolutional Kernels using Pytorch
61
stars
8
forks
source link
Implementing Mixed-Depthwise-Convolutional-Kernels using Pytorch (22 Jul 2019)
- Author:
- Mingxing Tan (Google Brain)
- Quoc V. Le (Google Brain)
- Paper Link
Method
- 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