Awesome

HorNet <img width="32" alt="HorNet Icon" src="figs/hornet_icon.png">

Created by Yongming Rao*, Wenliang Zhao*, Yansong Tang, Jie Zhou, Ser-Nam Lim†, Jiwen Lu†

This repository contains PyTorch implementation for HorNet (NeurIPS 2022).

HorNet is a family of generic vision backbones that perform explicit high-order spatial interactions based on Recursive Gated Convolution.

[Project Page] [arXiv]

Model Zoo

ImageNet-1K trained models:

ImageNet-22K trained models:

*indicate the model is finetuned to 384x384 resolution on ImageNet-22k.

ImageNet Classification

Requirements

• torch==1.8.0
• torchvision==0.9.0
• timm==0.4.12
• tensorboardX
• six
• submitit (multi-node training)

Data preparation: download and extract ImageNet images from http://image-net.org/. The directory structure should be

│ILSVRC2012/
├──train/
│  ├── n01440764
│  │   ├── n01440764_10026.JPEG
│  │   ├── n01440764_10027.JPEG
│  │   ├── ......
│  ├── ......
├──val/
│  ├── n01440764
│  │   ├── ILSVRC2012_val_00000293.JPEG
│  │   ├── ILSVRC2012_val_00002138.JPEG
│  │   ├── ......
│  ├── ......

Evaluation

To evaluate a pre-trained HorNet model on the ImageNet validation set with 8 GPUs, run:

python -m torch.distributed.launch --nproc_per_node=8 main.py \
--model hornet_tiny_7x7 --eval true --input_size 224 \
--resume /path/to/checkpoint \ 
--data_path /path/to/imagenet-1k

Training

To train HorNet models on ImageNet from scratch on a single machine, run:

python -m torch.distributed.launch --nproc_per_node=8 main.py \
--model hornet_tiny_7x7 --drop_path 0.2 --clip_grad 5\
--batch_size 128 --lr 4e-3 --update_freq 4 \
--model_ema true --model_ema_eval true \
--data_path /path/to/imagenet-1k \
--output_dir ./logs/hornet_tiny_7x7

We provide detailed training commands for our models in TRAINING.md.

Downstream Tasks

Please check the object_detection.md and semantic_segmentation.md for training and evaluation instructions on dense prediction tasks.

HorNet also achieves state-of-the-art performance on 3D object classification with our new framework (P2P) to leverage pre-trained image models for point cloud understanding.

License

MIT License

Acknowledgements

Our code is based on pytorch-image-models, DeiT and ConvNeXt. We would like to thank High-Flyer AI Research for their generous support of partial computational resources used in this project.

Citation

If you find our work useful in your research, please consider citing:

@article{rao2022hornet,
  title={HorNet: Efficient High-Order Spatial Interactions with Recursive Gated Convolutions},
  author={Rao, Yongming and Zhao, Wenliang and Tang, Yansong and Zhou, Jie and Lim, Ser-Lam and Lu, Jiwen},
  journal={Advances in Neural Information Processing Systems (NeurIPS)},
  year={2022}
}