GmNet: Revisiting Gating Mechanisms From A Frequency View

ICLR 2026

📄 arxiv | 💻 Code

Install requirements

Run the following command to install the dependences:

pip install -r requirements.txt

Data preparation

We need to prepare ImageNet-1k dataset from http://www.image-net.org/.

ImageNet-1k

ImageNet-1k contains 1.28 M images for training and 50 K images for validation. The images shall be stored as individual files:

ImageNet/
├── train
│   ├── n01440764
│   │   ├── n01440764_10026.JPEG
│   │   ├── n01440764_10027.JPEG
...
├── val
│   ├── n01440764
│   │   ├── ILSVRC2012_val_00000293.JPEG
...

Our code also supports storing the train set and validation set as the *.tar archives:

ImageNet/
├── train.tar
│   ├── n01440764
│   │   ├── n01440764_10026.JPEG
...
└── val.tar
│   ├── n01440764
│   │   ├── ILSVRC2012_val_00000293.JPEG
...

Training

To train the model on a single node with 8 GPUs for 300 epochs and distributed evaluation, run:

python3 -m torch.distributed.launch --nproc_per_node=8 train_imagenet.py --data {path to dataset} --model gmnet_s3 -b 256 --lr 3e-3 --weight-decay 0.05 --aa rand-m1-mstd0.5-inc1 --cutmix 0.2 --color-jitter 0. --drop-path 0. --log-wandb

You can find the checkpoints in Google Drive (Keep Updating ...)

Speed test

Run the following command to compare the throughputs on GPU/CPU:

python benchmark_onnx.py.py

BibTeX

@article{wang2025gmnet,
    title={GmNet: Revisiting Gating Mechanisms From A Frequency View},
    author={Wang, Yifan and Ma, Xu and Zhang, Yitian and Wang, Zhongruo and Kim, Sung-Cheol and Mirjalili, Vahid and Renganathan, Vidya and Fu, Yun},
    journal={arXiv preprint arXiv:2503.22841},
    year={2025}
    }

License

The majority of GmNet is licensed under an Apache License 2.0