HorNet 
Created by Yongming Rao*, Wenliang Zhao*, Yansong Tang, Jie Zhou, Ser-Nam Limโ , Jiwen Luโ
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.
Model Zoo
ImageNet-1K trained models:
| name | arch | Params | FLOPs | Top-1 | url |
|---|---|---|---|---|---|
| HorNet-T (7x7) | hornet_tiny_7x7 |
22M | 4.0G | 82.8 | Tsinghua Cloud |
| HorNet-T (GF) | hornet_tiny_gf |
23M | 3.9G | 83.0 | Tsinghua Cloud |
| HorNet-S (7x7) | hornet_small_7x7 |
50M | 8.8G | 83.8 | Tsinghua Cloud |
| HorNet-S (GF) | hornet_small_gf |
50M | 8.7G | 84.0 | Tsinghua Cloud |
| HorNet-B (7x7) | hornet_base_7x7 |
87M | 15.6G | 84.2 | Tsinghua Cloud |
| HorNet-B (GF) | hornet_base_gf |
88M | 15.5G | 84.3 | Tsinghua Cloud |
ImageNet-22K trained models:
| name | arch | Params | FLOPs | url |
|---|---|---|---|---|
| HorNet-L (7x7) | hornet_large_7x7 |
209M | 34.8G | Tsinghua Cloud |
| HorNet-L (GF) | hornet_large_gf |
211M | 34.7G | Tsinghua Cloud |
| HorNet-L (GF)* | hornet_large_gf_img384 |
216M | 101.8G | Tsinghua Cloud |
*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
โILSVRC2012/
โโโtrain/
โ โโโ n01440764
โ โ โโโ n01440764_10026.JPEG
โ โ โโโ n01440764_10027.JPEG
โ โ โโโ ......
โ โโโ ......
โโโval/
โ โโโ n01440764
โ โ โโโ ILSVRC2012_val_00000293.JPEG
โ โ โโโ ILSVRC2012_val_00002138.JPEG
โ โ โโโ ......
โ โโโ ......
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}
}