We provide installation instructions for ImageNet classification experiments here.
Create an new conda virtual environment
conda create -n moganet python=3.8 -y
conda activate moganet
Install Pytorch>=1.8.0, torchvision>=0.9.0 following official instructions. For example:
pip install torch==1.10.0+cu111 torchvision==0.11.0+cu111 torchaudio==0.10.0 -f https://download.pytorch.org/whl/torch_stable.html
Clone this repo and install required packages. You can install apex-amp if you want to use fp16 with Pytorch<=1.6.0.
git clone https://github.com/Westlake-AI/MogaNet
pip install timm fvcore
The results in this repository are produced with torch==1.10.0+cu111 torchvision==0.11.0+cu111 timm==0.6.12, and we adopt amp fp16 for fast training.
Download the ImageNet-1K classification dataset (train and val) and structure the data as follows. You can extract ImageNet with this script.
│imagenet/
├──train/
│ ├── n01440764
│ │ ├── n01440764_10026.JPEG
│ │ ├── n01440764_10027.JPEG
│ │ ├── ......
│ ├── ......
├──val/
│ ├── n01440764
│ │ ├── ILSVRC2012_val_00000293.JPEG
│ │ ├── ILSVRC2012_val_00002138.JPEG
│ │ ├── ......
│ ├── ......