HorNet
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.
Model Zoo
ImageNet-1K trained models:
Params FLOPs Top-1*indicate the model is finetuned to 384x384 resolution on ImageNet-22k.
ImageNet Classification
Requirements
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}