github.com/YXB-NKU/Strip-R-CNN @main
Chat with this repoBrowse by type
What it actually does AI analysis from the code graph — generated when you open this
README
Strip R-CNN: Large Strip Convolution for Remote Sensing Object Detection
Xinbin Yuan, ZhaoHui Zheng, Yuxuan Li, Xialei Liu, Li Liu, Xiang Li, Qibin Hou*, Ming-Ming Cheng
博客If you find our work helpful, please consider giving us a ⭐!
Offical implementation of "Strip R-CNN: Large Strip Convolution for Remote Sensing Object Detection"
we also add our config in https://github.com/zcablii/LSKNet
Abstract
While witnessed with rapid development, remote sensing object detection remains challenging for detecting high aspect ratio objects. This paper shows that large strip convolutions are good feature representation learners for remote sensing object detection and can detect objects of various aspect ratios well. Based on large strip convolutions, we build a new network architecture called Strip R-CNN, which is simple, efficient, and powerful. Unlike recent remote sensing object detectors that leverage large-kernel convolutions with square shapes, our Strip R-CNN takes advantage of sequential orthogonal large strip convolutions to capture spatial information. In addition, we enhance the localization capability of remote-sensing object detectors by decoupling the detection heads and equipping the localization head with strip convolutions to better localize the target objects. Extensive experiments on several benchmarks, for example DOTA, FAIR1M, HRSC2016, and DIOR, show that our Strip R-CNN can greatly improve previous work. In particular, our 30M model achieves 82.75\% mAP on DOTA-v1.0, setting a new state-of-the-art record.
Introduction
This repository is the official implementation of "Strip R-CNN: Large Strip Convolution for Remote Sensing Object Detection" at: arxiv
The master branch is built on MMRotate which works with PyTorch 1.6+.
StripNet backbone code is placed under mmrotate/models/backbones/, and the train/test configure files are placed under configs/strip_rcnn/
Results and models
Imagenet 300-epoch pre-trained Strip R-CNN-T backbone: Download
Imagenet 300-epoch pre-trained Strip R-CNN-S backbone: Download
Please note that the Exponential Moving Average (EMA) strategy was not utilized during the ImageNet pretraining stage.
DOTA-v1.0
| Model | mAP | Angle | lr schd | Batch Size | Configs | Download | note |
|---|---|---|---|---|---|---|---|
| RTMDet-l | 81.33 | - | 3x-ema | 8 | - | - | Prev. Best |
| Strip R-CNN-T | 81.40 | le90 | 1x | 1*8 | strip_rcnn_t_fpn_1x_dota_le90 | model | |
| Strip R-CNN-S | 82.28 | le90 | 1x | 1*8 | strip_rcnn_s_fpn_1x_dota_le90 | model | |
| Strip R-CNN-S* | 82.75 | le90 | 1x | 1*8 | strip_rcnn_s_fpn_1x_dota_le90 | model | MoCAE |
| StripNet-S + Roi_Trans | 81.72 | le90 | 1x | 1*8 | strip_rcnn_s_roitrans_fpn_1x_dota | model |
DOTA-v1.5
| 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 | 70.26 | le90 | 1x | 1*8 | lsk_s_fpn_1x_dota15_le90 | model | log | |
| Strip R-CNN-S | 72.27 | le90 | 1x | 1*8 | strip_rcnn_s_fpn_1x_dota15_le90 | model |
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_fair_le90 | model | log | |
| Strip R-CNN-S | 48.26 | le90 | 1x | 1*8 | strip_rcnn_s_fpn_1x_fair_le90 | model |
DIOR-R
| Model | mAP | Angle | lr schd | Batch Size | Configs | Download | note |
|---|---|---|---|---|---|---|---|
| O-RCNN | 64.30 | le90 | 1x | 1*8 | oriented_rcnn_r50_fpn_1x_dior_le90 | - | |
| LSKNet_S | 65.90 | le90 | 1x | 1*8 | lsk_s_fpn_1x_dior_le90 | model | log | |
| Strip R-CNN-S | 68.70 | le90 | 1x | 1*8 | strip_rcnn_s_fpn_1x_dior_le90 | model |
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 |
| Strip R-CNN-S | 90.60 | 98.70 | le90 | 3x | 1*8 | strip_rcnn_s_fpn_3x_hrsc_le90 | model |
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/YXB-NKU/Strip-R-CNN.git
cd Strip-R-CNN
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:
Core symbols most depended-on inside this repo
Shape
Languages
Modules by API surface
For agents
$ claude mcp add Strip-R-CNN \
-- python -m otcore.mcp_server <graph>