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.
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 |
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 |
HBox-supervised Model | Backbone | mAP | Paper Link | Code Link | Remark | Recommend |
---|---|---|---|---|---|---|
H2RBox | ResNet50 | 74.40 | ICLR2023 | Pytorch, Jittor | weak- and self-supervised | ✅ |
RBox-supervised 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 | |
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 | ✅ |
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 | ✅ |
Axis Learning | ResNet101 | 65.98 | Remote Sensing | Pytorch | single stage, anchor free | ✅ |
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 | ✅ |
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 | ✅ |
CAD-Net | ResNet101 | 69.90 | TGRS arXiv:1903.00857 | - | attention | |
ProbIoU | ResNet50 | 70.04 | arXiv:2106.06072 | TF | gaussian bounding boxes, hellinger distance | ✅ |
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 | ✅ |
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 | ✅ |
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 | ✅ |
O2-DNet | Hourglass104 | 72.8 | ISPRS, arXiv:1912.10694 | - | centernet, anchor free | ✅ |
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) | ✅ |
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 | ✅ |
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 | ✅ |
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 | ✅ |
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 | ✅ |
OSSDet | ResNeXt101 | 75.08 | JSTARS | - | feature enhancement and alignment | |
LO-Det | Darknet53 | 75.24 | TGRS, arXiv:2209.07709 | PyTorch | lightweight | ✅ |
EFN | U-Net | 75.27 | Preprints | - | Field-based | ✅ |
SAR | ResNet152 | 75.26 | Access | - | boundary problem | ✅ |
TricubeNet | Hourglass104 | 75.26 | WACV2022 | code | 2D tricube kernel | ✅ |
Mask OBB | ResNeXt101 | 75.33 | Remote Sensing | - | attention, multi-task | ✅ |
BBAVectors | ResNet101 | 75.36 | WACV2021 | PyTorch | keypoint based | ✅ |
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) | ✅ |
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 | ✅ |
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 | ✅ |
OWSR | Ensemble | 76.36 | CVPR2019 WorkShop | - | enhanced FPN | |
SLA | ResNet50 | 76.36 | Remote Sensing | PyTorch | sparse label assignment | ✅ |
SE2-Det | ResNet101 | 76.42 | Remote Sensing | - | enhanced fpn | |
OPLD | ResNet101 | 76.43 | J-STARS | PyTorch | boundary problem, point-guided | ✅ |
Polar Ray | ResNet101 | 76.50 | ACM MM2021 | - | polar rays representation | ✅ |
SIoU | ResNet50 | 76.54 | JSTARS | - | splicing intersection over union | ✅ |
R3Det++ | ResNet152 | 76.56 | arXiv:2004.13316 | TF | refined single stage, feature alignment, denoising | ✅ |
PolarDet | ResNet101 | 76.64 | IJRS arXiv:2010.08720 | - | polar, center-semantic | ✅ |
Beyond Bounding-Box | ResNet152 | 76.67 | CVPR2021 | PyTorch, MMRotate | point-based, reppoints | ✅ |
OAN | ResNeX50 | 76.73 | arXiv:2212.13136 | PyTorch | objectness activation network, efficient | ✅ |
SCRDet++ | ResNet101 | 76.81 | TPAMI | TF | angular boundary problem, denoising | ✅ |
DAFNe | ResNet101 | 76.95 | arXiv:2109.06148 | PyTorch | single stage, anchor free, center-to-corner regression | |
DAL+S2A-Net | ResNet50 | 76.95 | AAAI2021 | PyTorch | label assign | ✅ |
GGHL | DarkNet53 | 76.95 | TIP | PyTorch | gaussian heatmap labeling | ✅ |
EAutoDet | DarkNet53 | 77.05 | arXiv:2203.10747 | - | nas, yolov5 | ✅ |
Yu et al. | Res2Net50 | 77.18 | JSTARS | - | boundary-aware vectors, centernet | |
CoF-Net | ResNet50 | 77.2 | TGRS | - | coarse-to-fine, geometric constraints, spatial-spectral nonocal features | |
DCL | ResNet152 | 77.37 | CVPR2021 | TF | boundary problem | ✅ |
CSL+DCL | ResNet152 | 77.37 | IJCV | TF | boundary problem | ✅ |
CLT-Det | ResNet101 | 77.45 | TGRS | - | transformer, correlation learning | ✅ |
MSFF | ResNet50 | 77.46 | JSTARS | - | rotation invariance features | |
RIDet | ResNet50 | 77.62 | GRSL | PyTorch, TF | quad., representation ambiguity | ✅ |
Oriented RepPoints | Swin-Tiny | 77.63 | CVPR2022 | PyTorch, MMRotate | point-based, reppoints | ✅ |
RSP | ViTAEv2-S | 77.72 | arXiv:2204.02825 | PyTorch | remote sensing pretrain | ✅ |
RDD | ResNet101 | 77.75 | Remote Sensing | PyTorch | rotation-decoupled | |
CenterOBB | DLA-34 | 77.85 | Remote Sensing | - | angle classification, centernet | ✅ |
FSDet | ResNet50 | 77.85 | TGRS | - | label assign, feature refinemen, anchor free | ✅ |
CG-Net | ResNet101 | 77.89 | arXiv:2103.11399 | PyTorch | attention | |
HSP | ResNet101 | 78.01 | TGRS | - | hierarchical semantic propagation | |
FoRDet | VGG16 | 78.13 | TGRS | - | refinenet | |
AProNet | ResNet101 | 78.16 | ISPRS | Pyrotch | axis projection-based angle learning, feature enhancement | |
MGAR | DarkNet53 | 78.29 | arXiv:2209.02884 | - | angle classification | ✅ |
TransConvNet | Swin Transformer | 78.41 | Remote Sensing | - | enhanced fpn, self attention, transformer | |
FR-Est | ResNet101-DCN | 78.49 | TGRS | - | point-based estimator | ✅ |
TIOE-Det | - | 78.69 | ISPRS | PyTorch | misaligned tasks, orientation estimation | ✅ |
DARDet | ResNet50 | 78.74 | GRSL | PyTorch | varifocalnet, dcn, piou | |
TS-Conv | DarkNet53 | 78.75 | arXiv:2209.02200 | PyTorch | dynamic label assignment, task-wise samping | ✅ |
DHRec | ResNet152 | 78.83 | TPAMI | PyTorch | double horizontal rectangles | |
FCOSR | ResNeXt101 | 78.90 | arXiv:2111.10780 | PyTorch | anchor free | |
P2P | ResNet101 | 79.15 | AAAI2022 | - | poly iou loss | ✅ |
SASM | ResNeXt101 | 79.17 | AAAI2022 | MMRotate, PyTorch | label assign | ✅ |
AO2-DETR | ResNet50 | 79.22 | arXiv:2205.12785 | PyTorch | detr, feature refinement | ✅ |
GSNet | ResNet101 | 79.37 | arXiv:2204.02033 | - | enhanced fpn | |
S2A-Net | ResNet50/ResNet101 | 79.42/79.15 | TGRS | PyTorch, MMRotate | refined single stage, feature alignment | ✅ |
OFA-Net | ResNet101 | 79.52 | PRICAI2021 | - | refined single stage, feature alignment | |
TARDet | ResNet101 | 79.57 | CVPRW2022 | - | anchor free, feature alignment | |
RBox | ResNet50 | 79.59 | CVPR2022 | - | transformer, feature sampling and grouping | ✅ |
O2DETR | ResNet50 | 79.66 | arXiv:2106.03146 | - | deformable detr, transformer | ✅ |
ROSD | ResNet101 | 79.76 | Access | - | refined single stage, feature alignment | |
RAOD | ResNet101 | 79.78 | Applied Intelligence | - | enhanced fpn, feature refine | |
SES-Net | ResNet50 | 79.80 | arXiv:2111.03420 | - | sampling equivariance, self-attention | ✅ |
SARA | ResNet50/ResNet101 | 79.91/79.13 | Remote Sensing | - | self-adaptive aspect ratio anchor, refine | |
ARP+R-EIoU | YOLOv5x6 | 79.93 | arXiv:2109.10187 | - | area ratio of parallelogram, R-EIoU, yolov5 | |
GF-CSL | ResNet101 | 79.94 | TGRS | PyTorch | circular smooth label, gaussian focal loss | ✅ |
ADT-Det | ResNet152 | 79.95 | Remote Sensing | - | feature pyramid transformer, feature refinement | |
ReDet | ReR50-ReFPN | 80.10 | CVPR2021 | PyTorch, MMRotate | rotation-equivariant, rotation-invariant roI align | ✅ |
G-Rep | Swin-Tiny | 80.12 | arXiv:2205.11796 | MMRotate | pointset-based, gaussian modeling | ✅ |
PCI | ReR50-ReFPN | 80.15 | TIP | - | progressive context-dependent inference | ✅ |
GWD | ResNet152 | 80.23 | ICML2021 | TF, MMRotate | boundary discontinuity, square-like problem, gaussian wasserstein distance loss | ✅ |
DEA | ReR50-ReFPN | 80.37 | TGRS | PyTorch | dynamic enhancement anchor | ✅ |
FDOL | ReR50-ReFPN | 80.41 | TGRS | - | frequency analysis, self-attention | ✅ |
O2MER | ResNet50 | 80.43 | arXiv:2112.00504 | - | consistent geometric constraint | |
DODet | ResNet50 | 80.62 | TGRS | PyTorch | oriented proposal network, localization-guided head | |
KLD | ResNet152 | 80.63 | NeurIPS2021 | TF, MMRotate | Kullback-Leibler divergence, high-precision, scale invariance | ✅ |
AOPG | ResNet50/ResNet101 | 80.66/80.19 | arXiv:2110.01931 | PyTorch | anchor free, feature align | |
Li et al. | ResNet101 | 80.68 | Remote Sensing | - | enhance fpn, polar | |
CGCDet | ResNet50 | 80.70 | TNNLS | PyTorch | geometric consistent constraint, label assignment | ✅ |
AFF-Det | ResNet50 | 80.73 | Acm T Multim Comput. | - | enhanced fpn | |
PP-YOLOE-R-x | CRN-x | 80.73 | arXiv:2211.02386 | Paddle | real-time, yolo | ✅ |
Oriented R-CNN | ResNet50/ResNet101 | 80.87/80.52 | ICCV2021 | PyTorch, MMRotate | Rotation FPN, Gliding Vertex | |
OSKDet | ResNet101 | 80.91 | CVPR2022 | - | keypoint localization | ✅ |
KFIoU | Swin-Tiny | 80.93 | ICLR2023 | TF, MMRotate | Gaussian modeling, kalman filter | ✅ |
QPDet | ResNet50 | 81.00 | TGRS | PyTorch | quadrant point regression, rotated box refinement | ✅ |
Point RCNN | ReR50-ReFPN/Swin-Tiny | 80.71/81.32 | Remote Sensing | - | point based, cascade rcnn | ✅ |
SSEDet | ResNet50 | 81.08 | GRSL | - | circle theorem, short-side excursion | ✅ |
RTMDet | - | 81.33 | arXiv:2212.07784 | MMRotate | real-time | ✅ |
Model | Backbone | mAP | Paper Link | Code Link | Remark | Recommend |
---|---|---|---|---|---|---|
FR-H (DOTA) | ResNet101 | 60.46 | CVPR2018 | MXNet | DOTA dataset, baseline | ✅ |
Deep Active Learning | ResNet18 | 64.26 | arXiv:2003.08793 | - | CenterNet, Deep Active Learning | ✅ |
SBL | ResNet50 | 64.77 | arXiv:1810.08103 | - | single stage | |
CenterFPANet | ResNet18 | 65.29 | HPCCT & BDAI 2020 arXiv:2009.03063 | - | light-weight | |
MARNet | ResNet101 | 71.73 | IJRS | - | based on scrdet | |
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 | |
AF-EMS | ResNet101 | 73.97 | Remote Sensing | - | scale-aware feature, anchor free | |
SCRDet | ResNet101 | 75.35 | ICCV2019 | TF | attention, angular boundary problem | ✅ |
FADet | ResNet101 | 75.38 | ICIP2019 | - | attention | |
MFIAR-Net | ResNet152 | 76.07 | Sensors | - | feature attention, enhanced FPN | |
F3-Net | ResNet152 | 76.48 | Remote Sensing | - | feature fusion and filtration | |
Mask OBB | ResNeXt101 | 76.98 | Remote Sensing | - | attention, multi-task | ✅ |
CenterMap OBB | ResNet101 | 77.33 | TGRS | - | center-probability-map | |
ASSD | VGG16 | 77.8 | TGRS | - | feature aligned | |
AFC-Net | ResNet101 | 78.06 | Neurocomputing | - | adaptive feature concatenate | |
CG-Net | ResNet101 | 78.26 | arXiv:2103.11399 | PyTorch | attention | |
OPLD | ResNet101 | 78.35 | J-STARS | PyTorch | boundary problem, point-guided | ✅ |
A2RMNet | ResNet101 | 78.45 | Remote Sensing | - | attention, enhanced FPN, different pooled sizes | |
OWSR | Ensemble | 78.79 | CVPR2019 WorkShop | - | enhanced FPN | |
NPMMR-Det | DarkNet53 | 78.93 | TGRS | PyTorch | non-local, enhanced FPN | |
Parallel Cascade R-CNN | ResNeXt101 | 78.96 | Journal of Physics: Conference Series | - | cascade rcnn | |
DM-FPN | ResNet-Based | 79.27 | Remote Sensing | - | enhanced FPN | |
DDBN | ResNet101 | 79.3 | PR | - | adjacent feature compensation, dual detection branch | |
SCRDet++ | ResNet101 | 79.35 | TPAMI | TF | denoising | ✅ |
HSP | ResNet101 | 80.42 | TGRS | - | hierarchical semantic propagation | |
AFF-Det | ResNet101 | 81.18 | Acm T Multim Comput. | - | enhanced fpn |
RBox-supervised Model | Backbone | mAP | Paper Link | Code Link | Remark | Recommend |
---|---|---|---|---|---|---|
OAN | ResNet50 | 66.95 | arXiv:2212.13136 | PyTorch | objectness activation network, efficient | ✅ |
DAFNe | ResNet101 | 71.99 | arXiv:2109.06148 | PyTorch | r-fcos | |
RAOD | ResNet101 | 74.70 | Applied Intelligence | - | enhanced fpn, feature refine | |
OWSR | Ensemble | 76.60 | CVPR2019 WorkShop | - | enhanced FPN | |
ReDet | ReR50-ReFPN | 76.80 | CVPR2021 | PyTorch | rotation-equivariant, rotation-invariant RoI Align, | ✅ |
FDOL | ReR50-ReFPN | 77.12 | TGRS | - | frequency analysis, self-attention | ✅ |
APE | ResNeXt101(32x4) | 78.34 | TGRS arXiv:1906.09447 | - | length independent IoU (LIIoU) | ✅ |
Point RCNN | ReR50-ReFPN/Swin-Tiny | 79.31/80.14 | Remote Sensing | - | point based, cascade rcnn | ✅ |
Model | Backbone | mAP | Paper Link | Code Link | Remark | Recommend |
---|---|---|---|---|---|---|
CDD-Net | ResNet101 | 61.3 | GRSL | - | attention | |
GCWNet | ResNet101 | 62.7 | GRSL | - | enhance fpn, feature refine | |
ReDet | ReR50-ReFPN | 78.08 | CVPR2021 | PyTorch | rotation-equivariant, rotation-invariant RoI Align, | ✅ |
OWSR | Ensemble | 79.50 | CVPR2019 WorkShop | - | enhanced FPN |
RBox-supervised Model | Backbone | mAP | Paper Link | Code Link | Remark | Recommend |
---|---|---|---|---|---|---|
OAN | ResNet50 | 54.68 | arXiv:2212.13136 | PyTorch | objectness activation network, efficient | ✅ |
Wen et al. | - | 58.22 | ICCECE 2022 | - | yolov5, csl |
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 | Caffe2 | object cluster regions | ✅ |
DMNet | CVPR2020 WorkShop | - | object cluster regions | ✅ |
AdaZoom | arXiv:2106.10409 | - | object cluster regions, reinforcement learning | ✅ |
OIS | arXiv:1911.07732 | related Pytorch code | Oriented Instance Segmentation | ✅ |
ISOP | IGARSS2020 | - | Oriented Instance Segmentation | |
LR-RCNN | arXiv:2005.14264 | - | vehicle detection | - |
GRS-Det | TGRS | - | ship detection, rotation fcos | - |
DRBox | arXiv:1711.09405 | Caffe | sar object detection | ✅ |
DRBox-v2 | TGRS | TF | sar object detection | - |
RAPiD | arXiv:2005.11623 | PyTorch | overhead fisheye images | - |
OcSaFPN | arXiv:2012.09859 | - | denoising | - |
CR2A-Net | TGRS | - | ship detection | - |
- | TGRS | - | knowledge distillation | ✅ |
CHPDet | arXiv:2101.11189 | - | new ship dataset | ✅ |
EDRdet | PRL | - | adaptive dynamic networks, coarse-to-fine | ✅ |
Base Method | Code Link |
---|---|
RetinaNet | RetinaNet_Tensorflow_Rotation |
YOLOv3 | rotate-yolov3-Pytorch, YOLOv3-quadrangle-Pytorch, yolov3-polygon-Pytorch |
YOLOv4 | rotate-yolov4-Pytorch |
YOLOv5 | rotation-yolov5-Pytorch, YOLOv5_DOTA_OBB (CSL) |
YOLOX | YOLOX-Pytorch |
YOLOv7 | yolov7-obb (KLD) |
CenterNet | R-CenterNet-Pytorch |
JDet | Jittor |
OBBDet_Swin | OBBDet_Swin |
OrientedRepPoints | OrientedRepPoints_DOTA_Swin |
Localization Distillation | Rotated-LD |
Some remote sensing related object detection dataset statistics are in DATASET.md