EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
Use Grid search to find the best combination of alpha, beta and gamma for EfficientNet-B1, as discussed in Section 3.3 in paper. Search space, tuner, configuration examples are provided here.
:githublink:`Example code <examples/trials/efficientnet>`
- Set your working directory here in the example code directory.
- Run
git clone https://github.com/ultmaster/EfficientNet-PyTorch
to clone the ultmaster modified version of the original EfficientNet-PyTorch. The modifications were done to adhere to the original Tensorflow version as close as possible (including EMA, label smoothing and etc.); also added are the part which gets parameters from tuner and reports intermediate/final results. Clone it intoEfficientNet-PyTorch
; the files likemain.py
,train_imagenet.sh
will appear inside, as specified in the configuration files. - Run
nnictl create --config config_local.yml
(useconfig_pai.yml
for OpenPAI) to find the best EfficientNet-B1. Adjust the training service (PAI/local/remote), batch size in the config files according to the environment.
For training on ImageNet, read EfficientNet-PyTorch/train_imagenet.sh
. Download ImageNet beforehand and extract it adhering to PyTorch format and then replace /mnt/data/imagenet
in with the location of the ImageNet storage. This file should also be a good example to follow for mounting ImageNet into the container on OpenPAI.
The follow image is a screenshot, demonstrating the relationship between acc@1 and alpha, beta, gamma.
![](https://github.com/savan77/nni/raw/master/docs/img/efficientnet_search_result.png)