DOTA-DOAI

  • 语言Jupyter Notebook
  • 默认分支master
  • 大小13.5 MB
  • 更新2023年3月8日
在 GitHub 打开
avatarSJTU-Thinklab-Det
798
13914
MIT License

项目介绍

SJTU-Thinklab-Det/DOTA-DOAI 是一个托管在 GitHub 上的开源项目，主要使用 Jupyter Notebook 编写，采用 MIT License 许可证，目前获得 798 个 Star、139 次 Fork。

仓库全名
SJTU-Thinklab-Det/DOTA-DOAI
开源许可证
MIT License
Star 数
798
Fork 数
139
未关闭 Issue
14

README 摘要

DOTA-DOAI Abstract This repo is the codebase for our team to participate in DOTA related competitions, including rotation and horizontal detection. We mainly use FPN-based two-stage detector, and it is completed by Xue Yang and Jirui Yang. We also recommend two rotated/oriented object detection benchmarks, which are led by Xue Yang and Yue Zhou. AlpahaRotate-TF MMRotate-Pytorch Performance DOTA1.0 (Task1) Model |…

完整内容见 GitHub 原始 README 或本页 。

常见问题

DOTA-DOAI 用什么语言开发？

主要使用 Jupyter Notebook 编写，采用 MIT License 许可证，目前有 798 个 Star。

DOTA-DOAI 可以免费商用吗？

仓库采用 MIT License 许可证，可免费使用与修改，商业使用请遵守许可证条款（例如保留版权声明与许可声明）。

如何获取 DOTA-DOAI 的源码？

可以直接克隆仓库：git clone https://github.com/SJTU-Thinklab-Det/DOTA-DOAI.git，默认分支为 master；安装与运行步骤见仓库 README。

DOTA-DOAI 最近还在维护吗？

仓库最近一次更新为 2023年3月8日，当前有 14 个未关闭 Issue；Star 数为 798，可作为社区活跃度的参考。

本页面展示项目元数据与原始 README，GitRead 中文社区 编辑团队负责选题收录。

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关于

This repo is the codebase for our team to participate in DOTA related competitions, including rotation and horizontal detection.

描述取自 GitHub 官方 API，中文内容由 GitRead 中文社区 编辑团队整理。

简要分析

热度分
1101
价值分
522
活跃状态
活跃
aerial-imagerydotaobject-detectionremote-sensingrotation
GitHub

README

DOTA-DOAI

Abstract

This repo is the codebase for our team to participate in DOTA related competitions, including rotation and horizontal detection. We mainly use FPN-based two-stage detector, and it is completed by Xue Yang and Jirui Yang.

We also recommend two rotated/oriented object detection benchmarks, which are led by Xue Yang and Yue Zhou.

Performance

DOTA1.0 (Task1)

Model Backbone Training data Val data mAP Model Link Tricks lr schd Data Augmentation GPU Image/GPU Configs
FPN ResNet152_v1d (600,800,1024)->MS DOTA1.0 trainval DOTA1.0 test 78.99 model ALL 2x Yes 2X GeForce RTX 2080 Ti 1 cfgs_dota1.0_res152_v1.py

DOTA1.0 (Task2)

Model Backbone Training data Val data mAP Model Link Tricks lr schd Data Augmentation GPU Image/GPU Configs
FPN (memory consumption) ResNet152_v1d (600,800,1024)->MS DOTA1.0 trainval DOTA1.0 test 81.23 model ALL 2x Yes 2X Quadro RTX 8000 1 cfgs_dota1.0_res152_v1.py

Visualization

1

Performance of published papers on DOTA datasets

DOTA1.0 (Task1)

HBox-supervised Model Backbone mAP Paper Link Code Link Remark Recommend
H2RBox ResNet50 74.40 ICLR2023 Pytorch, Jittor weak- and self-supervised :white_check_mark:
RBox-supervised Model Backbone mAP Paper Link Code Link Remark Recommend
FR-O (DOTA) ResNet101 52.93 CVPR2018 MXNet DOTA dataset, baseline :white_check_mark:
IENet ResNet101 57.14 arXiv:1912.00969 - anchor free
TOSO ResNet101 57.52 ICASSP2020 - geometric transformation
Wang et al. HRNet 58.29 Comput. Electr. Eng. - anchor free
PIoU Loss DLA-34 60.5 ECCV2020 PyTorch IoU loss, anchor free :white_check_mark:
R2CNN ResNet101 60.67 arXiv:1706.09579 TF scene text, multi-task, different pooled sizes, baseline :white_check_mark:
RRPN ResNet101 61.01 TMM arXiv:1703.01086 TF scene text, rotation proposals, baseline :white_check_mark:
Axis Learning ResNet101 65.98 Remote Sensing Pytorch single stage, anchor free :white_check_mark:
Li et al. ResNet50 66.01 IGARSS2021 - refine, feature alignment
MARNet ResNet101 67.19 IJRS - based on scrdet
ICN ResNet101 68.16 ACCV2018 - image cascade, multi-scale :white_check_mark:
GSDet ResNet101 68.28 TIP - scale reasoning
RADet ResNeXt101 69.09 Remote Sensing - enhanced FPN, mask rcnn
KARNET ResNet50 68.87 CISNRC 2020 - attention denoising, anchor refining
RoI Transformer ResNet101 69.56 CVPR2019 MXNet, PyTorch, MMRotate roi transformer :white_check_mark:
CAD-Net ResNet101 69.90 TGRS arXiv:1903.00857 - attention
ProbIoU ResNet50 70.04 arXiv:2106.06072 TF gaussian bounding boxes, hellinger distance :white_check_mark:
ROTP ResNet101 70.29 arXiv:2202.06565 - solar corona heatmap, key points, head of object
A2S-Det ResNet101 70.64 Remote Sensing - label assign
AOOD ResNet101 71.18 Neural Computing and Applications - attention + R-DFPN
CGP Box ResNet18 71.35 IJRS - center-guide points
Zhou et al. CSPDarknet53 71.5 ijgi - enhanced fpn
ACE DLA34 71.7 TIP - corner point :white_check_mark:
Pei et al. ResNet101 71.76 IGRASS2021 - enhanced FPN
Cascade-FF ResNet152 71.80 ICME2020 - refined retinanet + feature fusion
SCPNet Hourglass104 72.20 GRSL - corner points
P-RSDet ResNet101 72.30 Access - anchor free, polar coordinates :white_check_mark:
Zhang et al. ResNet101 72.37 GSIS - refine-stage
ROPDet ResNet101-DCN 72.42 J REAL-TIME IMAGE PR - point set representation
SCRDet ResNet101 72.61 ICCV2019 TF: R2CNN++, IoU-Smooth L1: RetinaNet-based, R3Det-based attention, angular boundary problem :white_check_mark:
O2-DNet Hourglass104 72.8 ISPRS, arXiv:1912.10694 - centernet, anchor free :white_check_mark:
HRPNet HRNet-W48 72.83 GRSL - polar
SARD ResNet101 72.95 Access - IoU-based weighted loss
GLS-Net ResNet101 72.96 Remote Sensing - attention, saliency pyramid
ProjBB ResNet101 73.03 Access code, codebase new definition of bounding box
DRN Hourglass104 73.23 CVPR2020 code centernet, feature selection module, dynamic refinement head, new dataset (SKU110K-R) :white_check_mark:
FADet ResNet101 73.28 ICIP2019 - attention
RBA-CenterNet ResNet101 73.41 IJCNN - centernet, refine feature
MFIAR-Net ResNet152 73.49 Sensors - feature attention, enhanced FPN
CFC-NET ResNet101 73.50 TGRS PyTorch critical feature, label assign, refine :white_check_mark:
Dual-Det ResNet18 73.62 IJRS code keypoint-based
Li et al. CSP-Hourglass 73.70 GRSL - CSP-Hourglass Net
R3Det ResNet101 73.79 AAAI2021 TF, r3det-on-mmdetection, r3det-pytorch, MMRotate refined single stage, feature alignment :white_check_mark:
SDCDet ResNet101 73.89 PRAI2021 - instance segmentation direction correction
Geng et al. ResNet101 73.92 GRSL - anchor free, angle encoding
Free3Net ResNet101 74.04 TMM - anchor free, gliding vertex :white_check_mark:
SegmRDet ResNet50 74.14 Neurocomputing - segmentation-baed, new training and inference
Hou et al. ResNet101 74.44 TIP - enhanced FPN, feature alignment
Wu et al. ResNet50 74.45 J Electron Imaging - enhanced FPN, feature alignment
CenterRot ResNet152 74.75 Remote Sensing - anchor free, deformable-fpn, csl
MEAD ResNet101 74.80 Applied Intelligence - mechanism anchor free, mask guided, refine feature
TS4Net ResNet101 74.82 Neurocomputing - label assign
FEDet ResNet50 74.89 ICCSE 2021 - refine feature, angle constraint
SRep-RDet RepVGG-B1g2 74.89 IJRS - refine feature, attention, repvgg
Yuan et al. HRNet32 74.97 ICCTIT2021 - centernet-r
Gliding Vertex ResNet101 75.02 TPAMI arXiv:1911.09358 PyTorch, MMRotate quadrilateral bbox :white_check_mark:
OSSDet ResNeXt101 75.08 JSTARS - feature enhancement and alignment
LO-Det Darknet53 75.24 TGRS, arXiv:2209.07709 PyTorch lightweight :white_check_mark:
EFN U-Net 75.27 Preprints - Field-based :white_check_mark:
SAR ResNet152 75.26 Access - boundary problem :white_check_mark:
TricubeNet Hourglass104 75.26 WACV2022 code 2D tricube kernel :white_check_mark:
Mask OBB ResNeXt101 75.33 Remote Sensing - attention, multi-task :white_check_mark:
BBAVectors ResNet101 75.36 WACV2021 PyTorch keypoint based :white_check_mark:
SAOA ResNet101 75.41 ICIG2021 - anchor free, spatial self-attention
Yuan et al. ResNet101 75.43 TGRS - feature alignment, continuous boundary
Zand et al. DarkNet53 75.5 TGRS - angle classification
MSFF - 75.60 ICCECE 2022 - multi-scale feature fusion
RODFormer ViT-B4 75.60 Sensors - vision transformer
FFA ResNet101 75.7 ISPRS - enhanced FPN, rotation proposals
CBDA-Net DLA-34-DCN 75.74 TGRS - dual attention
APE ResNeXt101(32x4) 75.75 TGRS arXiv:1906.09447 - adaptive period embedding, length independent IoU (LIIoU) :white_check_mark:
R4Det ResNet152 75.54 Image Vis Comput - feature recursion and refinement
SurroundNet ResNet152 75.88 Remote Sensing - attention, anchor free, quad
RIE HRGANet-W48 75.94 Remote Sensing - center-based rotated inscribed ellipse
F3-Net ResNet152 76.02 Remote Sensing - feature fusion and filtration
CenterMap OBB ResNet101 76.03 TGRS - center-probability-map
DA-Net ResNet101 76.11 GRSL - feature alignment
MDL-p ResNet101 76.16 arXiv:2204.00840 - gaussian modeling
CSL ResNet152 76.17 ECCV2020 TF, MMRotate, Pytorch: YOLOv5_DOTA_OBB (CSL) angular boundary problem :white_check_mark:
MRDet ResNet101 76.24 TGRS - arbitrary-oriented rpn, multiple subtasks
AFC-Net ResNet101 76.27 Neurocomputing - adaptive feature concatenate
RSDet/RSDet++ ResNet152 76.30 AAAI2021/TCSVT TF quadrilateral bbox, angular boundary problem :white_check_mark:
OWSR Ensemble 76.36 CVPR2019 WorkShop - enhanced FPN
SLA ResNet50 76.36 Remote Sensing PyTorch sparse label assignment :white_check_mark:
SE2-Det ResNet101 76.42 Remote Sensing - enhanced fpn
OPLD ResNet101 76.43 J-STARS PyTorch boundary problem, point-guided :white_check_mark:
Polar Ray ResNet101 76.50 ACM MM2021 - polar rays representation :white_check_mark:
SIoU ResNet50 76.54 JSTARS - splicing intersection over union :white_check_mark:
R3Det++ ResNet152 76.56 arXiv:2004.13316 TF refined single stage, feature alignment, denoising :white_check_mark:
PolarDet ResNet101 76.64 [IJRS](https://www.t

README 内容过长，已截取前 64 KB。完整内容请前往 GitHub 仓库查看。

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