SJTU-Thinklab-Det / Dota Doai
Licence: mit
This repo is the codebase for our team to participate in DOTA related competitions, including rotation and horizontal detection.
Stars: ✭ 326
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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.
We also recommend a tensorflow-based rotation detection benchmark, which is led by YangXue.
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
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 | |
| TOSO | ResNet101 | 57.52 | ICASSP2020 | - | geometric transformation | |
| 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 | - | single stage, anchor free | ✅ |
| ICN | ResNet101 | 68.16 | ACCV2018 | - | image cascade, multi-scale | ✅ |
| 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 | roi transformer | ✅ |
| CAD-Net | ResNet101 | 69.90 | TGRS arXiv:1903.00857 | - | attention | |
| A2S-Det | ResNet101 | 70.64 | Remote Sensing | - | label assign | |
| AOOD | ResNet101 | 71.18 | Neural Computing and Applications | - | attention + R-DFPN | |
| Cascade-FF | ResNet152 | 71.80 | ICME2020 | - | Refined RetinaNet + feature fusion | |
| P-RSDet | ResNet101 | 72.30 | Access | - | anchor free, polar coordinates | ✅ |
| BBAVectors | ResNet101 | 72.32 | WACV2021 | Pytorch | keypoint based | ✅ |
| 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 | |
| DRN | Hourglass104 | 73.23 | CVPR(oral) | code | centernet, feature selection module, dynamic refinement head, new dataset (SKU110K-R) | ✅ |
| FADet | ResNet101 | 73.28 | ICIP2019 | - | attention | |
| MFIAR-Net | ResNet152 | 73.49 | Sensors | - | feature attention, enhanced FPN | |
| CFC-NET | ResNet101 | 73.50 | arXiv:2101.06849 | Pytorch | critical feature, label assign, refine | ✅ |
| R3Det | ResNet152 | 73.74 | AAAI2021 | TF, Pytorch | refined single stage, feature alignment | ✅ |
| RSDet | ResNet152 | 74.10 | AAAI2021 | TF | quadrilateral bbox, angular boundary problem | ✅ |
| Gliding Vertex | ResNet101 | 75.02 | TPAMI arXiv:1911.09358 | Pytorch | quadrilateral bbox | ✅ |
| EFN | U-Net | 75.27 | Preprints | - | Field-based | ✅ |
| SAR | ResNet152 | 75.26 | Access | - | boundary problem | ✅ |
| 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 | TGRS arXiv:1906.09447 | - | adaptive period embedding, length independent IoU (LIIoU) | ✅ |
| R4Det | ResNet152 | 75.54 | Image Vis Comput | - | feature recursion and refinement | |
| F3-Net | ResNet152 | 76.02 | Remote Sensing | - | feature fusion and filtration | |
| CenterMap OBB | ResNet101 | 76.03 | TGRS | - | center-probability-map | |
| CSL | ResNet152 | 76.17 | ECCV2020 | TF:CSL_RetinaNet | angular boundary problem | ✅ |
| MRDet | ResNet101 | 76.24 | arXiv:2012.13135 | - | arbitrary-oriented rpn, multiple subtasks | |
| OWSR | Ensemble (ResNet101 + ResNeXt101 + mdcn-ResNet101) | 76.36 | CVPR2019 WorkShop TGRS | - | enhanced FPN | |
| OPLD | ResNet101 | 76.43 | J-STARS | Pytorch | boundary problem, point-guided | ✅ |
| R3Det++ | ResNet152 | 76.56 | arXiv:2004.13316 | TF | refined single stage, feature alignment, denoising | ✅ |
| PolarDet | ResNet101 | 76.64 | arXiv:2010.08720 | - | polar, center-semantic | ✅ |
| SCRDet++ | ResNet101 | 76.81 | arXiv:2004.13316 | TF | angular boundary problem, denoising | ✅ |
| DAL+S2A-Net | ResNet50 | 76.95 | AAAI2021 | Pytorch | label assign | ✅ |
| DCL | ResNet152 | 77.37 | CVPR2021 | TF | boundary problem | ✅ |
| RDD | ResNet101 | 77.75 | Remote Sensing | Pytorch | rotation-decoupled | |
| FR-Est | ResNet101-DCN | 78.49 | TGRS | - | point-based estimator | ✅ |
| S2A-Net | ResNet101 | 79.15 | arXiv:2008.09397 | Pytorch | refined single stage, feature alignment | ✅ |
| ReDet | ReR50-ReFPN | 80.10 | CVPR2021 | Pytorch | rotation-equivariant, rotation-invariant RoI Align, | ✅ |
| GWD | ResNet152 | 80.23 | arXiv:2101.11952 | TF | boundary discontinuity, square-like problem, gaussian wasserstein distance loss | ✅ |
DOTA1.0 (Task2)
| 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 | |
| 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 | ResNeXt-101 | 76.98 | Remote Sensing | - | attention, multi-task | ✅ |
| CenterMap OBB | ResNet101 | 77.33 | TGRS | - | center-probability-map | |
| 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 (ResNet101 + ResNeXt101 + mdcn-ResNet101) | 78.79 | CVPR2019 WorkShop TGRS | - | enhanced FPN | |
| Parallel Cascade R-CNN | ResNeXt-101 | 78.96 | Journal of Physics: Conference Series | - | cascade rcnn | |
| DM-FPN | ResNet-Based | 79.27 | Remote Sensing | - | enhanced FPN | |
| SCRDet++ | ResNet101 | 79.35 | arXiv:2004.13316 | TF | denoising | ✅ |
DOTA1.5 (Task1)
| Model | Backbone | mAP | Paper Link | Code Link | Remark | Recommend |
|---|---|---|---|---|---|---|
| APE | ResNeXt-101(32x4) | 78.34 | TGRS arXiv:1906.09447 | - | length independent IoU (LIIoU) | ✅ |
| OWSR | Ensemble (ResNet101 + ResNeXt101 + mdcn-ResNet101) | 76.60 | TGRS CVPR2019 WorkShop | - | enhanced FPN | |
| ReDet | ReR50-ReFPN | 76.80 | CVPR2021 | Pytorch | rotation-equivariant, rotation-invariant RoI Align, | ✅ |
DOTA1.5 (Task2)
| Model | Backbone | mAP | Paper Link | Code Link | Remark | Recommend |
|---|---|---|---|---|---|---|
| CDD-Net | ResNet101 | 61.3 | GRSL | - | attention | |
| ReDet | ReR50-ReFPN | 78.08 | CVPR2021 | Pytorch | rotation-equivariant, rotation-invariant RoI Align, | ✅ |
| OWSR | Ensemble (ResNet101 + ResNeXt101 + mdcn-ResNet101) | 79.50 | TGRS 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 | Caffe2 | object cluster regions | ✅ |
| DMNet | CVPR2020 WorkShop | - | object cluster regions | ✅ |
| 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 | ✅ |
Other Rotation Detection Codes
| Base Method | Code Link |
|---|---|
| RetinaNet | RetinaNet_Tensorflow_Rotation |
| YOLOv3 | rotate-yolov3-Pytorch, YOLOv3-quadrangle-Pytorch, yolov3-polygon-Pytorch |
| YOLOv5 | rotation-yolov5-Pytorch |
| CenterNet | R-CenterNet-Pytorch |
Dataset
Some remote sensing related object detection dataset statistics are in DATASET.md
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