lsk_arch

PWC PWC

This repository is the official implementation of "Large Selective Kernel Network for Remote Sensing Object Detection" at: https://arxiv.org/pdf/2303.09030.pdf

Abstract

Recent research on remote sensing object detection has largely focused on improving the representation of oriented bounding boxes but has overlooked the unique prior knowledge presented in remote sensing scenarios. Such prior knowledge can be useful because tiny remote sensing objects may be mistakenly detected without referencing a sufficiently long-range context, and the long-range context required by different types of objects can vary. In this paper, we take these priors into account and propose the Large Selective Kernel Network (LSKNet). LSKNet can dynamically adjust its large spatial receptive field to better model the ranging context of various objects in remote sensing scenarios. To the best of our knowledge, this is the first time that large and selective kernel mechanisms have been explored in the field of remote sensing object detection. Without bells and whistles, LSKNet sets new state-of-the-art scores on standard benchmarks, i.e., HRSC2016 (98.46% mAP), DOTA-v1.0 (81.85% mAP) and FAIR1M-v1.0 (47.87% mAP). Based on a similar technique, we rank 2nd place in 2022 the Greater Bay Area International Algorithm Competition

Introduction

This repository is the official implementation of "Large Selective Kernel Network for Remote Sensing Object Detection" at: https://arxiv.org/pdf/2303.09030.pdf

The master branch is built on MMRotate which works with PyTorch 1.6+.

LSKNet backbone code is placed under mmrotate/models/backbones/, and the train/test configure files are placed under configs/lsknet/

Results and models

Imagenet 300-epoch pre-trained LSKNet-T backbone: Download

Imagenet 300-epoch pre-trained LSKNet-S backbone: Download

DOTA1.0

Model mAP Angle lr schd Batch Size Configs Download note
RTMDet-l (1024,1024,-) 81.33 - 3x-ema 8 - - Prev. Best
LSKNet_T (1024,1024,200) 81.37 le90 1x 2*8 lsk_t_fpn_1x_dota_le90 model | log
LSKNet_S (1024,1024,200) 81.64 le90 1x 1*8 lsk_s_fpn_1x_dota_le90 model | log
LSKNet_S* (1024,1024,200) 81.85 le90 1x 1*8 lsk_s_ema_fpn_1x_dota_le90 model | log EMA Finetune

FAIR1M-1.0

Model mAP Angle lr schd Batch Size Configs Download note
O-RCNN (1024,1024,200) 45.60 le90 1x 1*8 oriented_rcnn_r50_fpn_1x_fair_le90 - Prev. Best
LSKNet_S (1024,1024,200) 47.87 le90 1x 1*8 lsk_s_fpn_1x_dota_le90 model | log

HRSC2016

Model mAP(07) mAP(12) Angle lr schd Batch Size Configs Download note
RTMDet-l 90.60 97.10 le90 3x - - - Prev. Best
ReDet 90.46 97.63 le90 3x 2*4 redet_re50_refpn_3x_hrsc_le90 - Prev. Best
LSKNet_S 90.65 98.46 le90 3x 1*8 lsk_s_fpn_3x_hrsc_le90 model | log

Installation

MMRotate depends on PyTorch, MMCV and MMDetection. Below are quick steps for installation. Please refer to Install Guide for more detailed instruction.

conda create --name openmmlab python=3.8 -y
conda activate openmmlab
conda install pytorch==1.8.0 torchvision==0.9.0 cudatoolkit=10.2 -c pytorch
pip install -U openmim
mim install mmcv-full
mim install mmdet
git clone https://github.com/zcablii/Large-Selective-Kernel-Network.git
cd Large-Selective-Kernel-Network
pip install -v -e .

Get Started

Please see get_started.md for the basic usage of MMRotate. We provide colab tutorial, and other tutorials for:

Acknowledgement

MMRotate is an open source project that is contributed by researchers and engineers from various colleges and companies. We appreciate all the contributors who implement their methods or add new features, as well as users who give valuable feedbacks. We wish that the toolbox and benchmark could serve the growing research community by providing a flexible toolkit to reimplement existing methods and develop their own new methods.

Citation

If you use this toolbox or benchmark in your research, please cite this project.

@article{li2023large,
  title   = {Large Selective Kernel Network for Remote Sensing Object Detection},
  author  = {Li, Yuxuan and Hou, Qibin and Zheng, Zhaohui and Cheng, Mingming and Yang, Jian and Li, Xiang},
  journal={ArXiv},
  year={2023}
}

License

This project is released under the Apache 2.0 license.