/DOTA-DOAI

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

Primary LanguageJupyter NotebookMIT LicenseMIT

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 YangXue and YangJirui.

Performance

DOTA1.0 (Task1)

Model Backbone Training data Val data mAP Model Link Tricks lr schd Data Augmentation GPU Image/GPU Configs
FPN (baseline) ResNet50_v1 (600,800,1024)->800 DOTA1.0 trainval DOTA1.0 test 69.35 model No 1x No 2X GeForce RTX 2080 Ti 1 cfgs_dota1.0_res50_v2.py
FPN ResNet50_v1d (600,800,1024)->800 DOTA1.0 trainval DOTA1.0 test 70.87 model +InLD 1x No 2X GeForce RTX 2080 Ti 1 cfgs_dota1.0_res50_v3.py
FPN ResNet152_v1d (600,800,1024)->MS DOTA1.0 trainval DOTA1.0 test 76.20 (76.54) 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 (baseline) ResNet50_v1 (600,800,1024)->800 DOTA1.0 trainval DOTA1.0 test 76.03 model No 1x No 2X Quadro RTX 8000 1 cfgs_dota1.0_res50_v2.py
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)

Model Backbone mAP Paper Link Code Link Remark Recommend
FR-O (DOTA) ResNet101 52.93 CVPR2018 MXNet DOTA dataset, baseline ✅
IENet ResNet101 57.14 arXiv:1912.00969 - anchor free
R2CNN ResNet101 60.67 arXiv:1706.09579 TF scene text, multi-task, different pooled sizes, baseline ✅
RRPN ResNet101 61.01 TMM arXiv:1703.01086 TF scene text, rotation proposals, baseline ✅
RetinaNet-H ResNet101 64.73 arXiv:1908.05612 TF single stage, baseline ✅
Axis Learning ResNet101 65.98 Remote Sensing - single stage, anchor free ✅
ICN ResNet101 68.16 ACCV2018 - image cascade, multi-scale ✅
RADet ResNeXt101 69.09 Remote Sensing - enhanced FPN, mask rcnn
RoI Transformer ResNet101 69.56 CVPR2019 MXNet, Pytorch roi transformer ✅
P-RSDet ResNet101 69.82 arXiv:2001.02988 - anchor free, polar coordinates ✅
CAD-Net ResNet101 69.90 TGRS arXiv:1903.00857 - attention
O2-DNet Hourglass104 71.04 arXiv:1912.10694 - anchor free ✅
SCRDet ResNet101 72.61 ICCV2019 TF:R2CNN++, IoU-Smooth L1 attention, angular boundary problem ✅
SARD ResNet101 72.95 Access - IoU-based weighted loss
FADet ResNet101 73.28 ICIP2019 - attention
R3Det ResNet152 73.74 arXiv:1908.05612 TF refined single stage, feature alignment ✅
RSDet ResNet152 74.10 arXiv:1911.08299 - quadrilateral bbox, angular boundary problem ✅
Gliding Vertex ResNet101 75.02 TPAMI arXiv:1911.09358 Pytorch quadrilateral bbox ✅
Mask OBB ResNeXt-101 75.33 Remote Sensing - attention, multi-task ✅
FFA ResNet101 75.7 ISPRS - enhanced FPN, rotation proposals
APE ResNeXt-101(32x4) 75.75 arXiv:1906.09447 - length independent IoU (LIIoU) ✅
CSL ResNet152 76.17 / 70.29 arXiv:2003.05597 TF:CSL_RetinaNet angular boundary problem ✅
OWSR Ensemble (ResNet101 + ResNeXt101 + mdcn-ResNet101) 76.36 CVPR2019 WorkShop TGRS - enhanced FPN
FPN-InLD / R3Det-InLD (R3Det++) ResNet101 / ResNet152 76.81 / 76.56 - TF:R3Det++, FPN-InLD

DOTA1.0 (Task2)

Model Backbone mAP Paper Link Code Link Remark Recommend
FR-H (DOTA) ResNet101 60.46 CVPR2018 MXNet DOTA dataset, baseline ✅
SBL ResNet50 64.77 arXiv:1810.08103 - single stage
FMSSD VGG16 72.43 TGRS - IoU-based weighted loss, enhanced FPN
ICN ResNet101 72.45 ACCV2018 - image cascade, multi-scale ✅
IoU-Adaptive R-CNN ResNet101 72.72 Remote Sensing - IoU-based weighted loss, cascade
EFR VGG16 73.49 Remote Sensing Pytorch enhanced FPN
SCRDet ResNet101 75.35 ICCV2019 TF attention, angular boundary problem ✅
FADet ResNet101 75.38 ICIP2019 - attention
Mask OBB ResNeXt-101 76.98 Remote Sensing - attention, multi-task ✅
A2RMNet ResNet101 78.45 Remote Sensing - attention, enhanced FPN, different pooled sizes
OWSR Ensemble (ResNet101 + ResNeXt101 + mdcn-ResNet101) 78.79 CVPR2019 WorkShop TGRS - enhanced FPN
DM-FPN ResNet-Based 79.27 Remote Sensing - enhanced FPN
FPN-InLD ResNet152 81.23 - FPN-InLD-TF

DOTA1.5 (Task1)

Model Backbone mAP Paper Link Code Link Remark Recommend
APE ResNeXt-101(32x4) 78.34 arXiv:1906.09447 - length independent IoU (LIIoU) ✅
OWSR Ensemble (ResNet101 + ResNeXt101 + mdcn-ResNet101) 76.60 CVPR2019 WorkShop - enhanced FPN

DOTA1.5 (Task2)

Model Backbone mAP Paper Link Code Link Remark Recommend
OWSR Ensemble (ResNet101 + ResNeXt101 + mdcn-ResNet101) 79.50 CVPR2019 WorkShop - enhanced FPN

Related Articles

Model Paper Link Code Link Remark Recommend
SSSDET ICIP2019 arXiv:1909.00292 - vehicle detection, lightweight
AVDNet GRSL arXiv:1907.07477 - vehicle detection, small object
ClusDet ICCV2019 - object cluster regions ✅
OIS arXiv:1911.07732 related Pytorch code Oriented Instance Segmentation ✅

Dataset

Some remote sensing related object detection dataset statistics are in DATASET.md