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.
| 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 |
| 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 |
| 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 | - | 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 | ✅ |
| 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 | - | centernet, anchor free | ✅ |
| AOOD | ResNet101 | 71.18 | Neural Computing and Applications | - | attention + R-DFPN | |
| Cascade-FF | ResNet152 | 71.80 | ICME2020 | - | Refined RetinaNet + feature fusion | |
| SCRDet | ResNet101 | 72.61 | ICCV2019 | TF:R2CNN++, IoU-Smooth L1: RetinaNet-based, R3Det-based | attention, angular boundary problem | ✅ |
| 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 | |
| R3Det | ResNet152 | 73.74 | arXiv:1908.05612 | TF, Pytorch | 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 | TGRS arXiv:1906.09447 | - | adaptive period embedding, length independent IoU (LIIoU) | ✅ |
| CSL | ResNet152 | 76.17 | ECCV2020 | TF:CSL_RetinaNet | angular boundary problem | ✅ |
| OWSR | Ensemble (ResNet101 + ResNeXt101 + mdcn-ResNet101) | 76.36 | CVPR2019 WorkShop TGRS | - | enhanced FPN | |
| R3Det++ | ResNet152 | 76.56 | arXiv:2004.13316 | TF | refined single stage, feature alignment, denoising | ✅ |
| SCRDet++ | ResNet101 | 76.81 | arXiv:2004.13316 | TF | angular boundary problem, denoising | ✅ |
| 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 | |
| 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 | |
| MFIAR-Net | ResNet152 | 76.07 | Sensors | - | feature attention, enhanced FPN | |
| 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 | |
| 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 | ✅ |
| 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 |
| Model | Backbone | mAP | Paper Link | Code Link | Remark | Recommend |
|---|---|---|---|---|---|---|
| OWSR | Ensemble (ResNet101 + ResNeXt101 + mdcn-ResNet101) | 79.50 | TGRS CVPR2019 WorkShop | - | enhanced FPN |
| 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 | ✅ |
| LR-RCNN | arXiv:2005.14264 | - | vehicle detection | - |
Some remote sensing related object detection dataset statistics are in DATASET.md