基准和模型库¶
Rotated RetinaNet-OBB/HBB ( ICCV’2017)
Rotated FasterRCNN-OBB ( TPAMI’2017)
Rotated RepPoints-OBB ( ICCV’2019)
Rotated FCOS (ICCV’2019)
RoI Transformer (CVPR’2019)
Gliding Vertex (TPAMI’2020)
Rotated ATSS-OBB (CVPR’2020)
CSL (ECCV’2020)
R3Det (AAAI’2021)
S2A-Net (TGRS’2021)
ReDet (CVPR’2021)
Beyond Bounding-Box (CVPR’2021)
Oriented R-CNN (ICCV’2021)
GWD (ICML’2021)
KLD (NeurIPS’2021)
SASM (AAAI’2022)
KFIoU (arXiv)
G-Rep (stay tuned)
DOTA v1.0 数据集上的结果¶
| 骨干网络 | mAP | 角度编码方式 | 训练策略 | 显存占用 (GB) | 推理时间 (fps) | 增强方法 | 批量大小 | 配置 | 下载 |
|---|---|---|---|---|---|---|---|---|---|
| ResNet50 (1024,1024,200) | 59.44 | oc | 1x | 3.45 | 15.6 | - | 2 | rotated_reppoints_r50_fpn_1x_dota_oc | model | log |
| ResNet50 (1024,1024,200) | 64.55 | oc | 1x | 3.38 | 15.7 | - | 2 | rotated_retinanet_hbb_r50_fpn_1x_dota_oc | model | log |
| ResNet50 (1024,1024,200) | 65.59 | oc | 1x | 3.12 | 18.5 | - | 2 | rotated_atss_hbb_r50_fpn_1x_dota_oc | model | log |
| ResNet50 (1024,1024,200) | 66.45 | oc | 1x | 3.53 | 15.3 | - | 2 | sasm_reppoints_r50_fpn_1x_dota_oc | model | log |
| ResNet50 (1024,1024,200) | 68.42 | le90 | 1x | 3.38 | 16.9 | - | 2 | rotated_retinanet_obb_r50_fpn_1x_dota_le90 | model | log |
| ResNet50 (1024,1024,200) | 68.79 | le90 | 1x | 2.36 | 22.4 | - | 2 | rotated_retinanet_obb_r50_fpn_fp16_1x_dota_le90 | model | log |
| ResNet50 (1024,1024,200) | 69.49 | le135 | 1x | 4.05 | 8.6 | - | 2 | g_reppoints_r50_fpn_1x_dota_le135 | model | log |
| ResNet50 (1024,1024,200) | 69.51 | le90 | 1x | 4.40 | 24.0 | - | 2 | rotated_retinanet_obb_csl_gaussian_r50_fpn_fp16_1x_dota_le90 | model | log |
| ResNet50 (1024,1024,200) | 69.55 | oc | 1x | 3.39 | 15.5 | - | 2 | rotated_retinanet_hbb_gwd_r50_fpn_1x_dota_oc | model | log |
| ResNet50 (1024,1024,200) | 69.60 | le90 | 1x | 3.38 | 15.1 | - | 2 | rotated_retinanet_hbb_kfiou_r50_fpn_1x_dota_le90 | model | log |
| ResNet50 (1024,1024,200) | 69.63 | le135 | 1x | 3.45 | 16.1 | - | 2 | cfa_r50_fpn_1x_dota_le135 | model | log |
| ResNet50 (1024,1024,200) | 69.76 | oc | 1x | 3.39 | 15.6 | - | 2 | rotated_retinanet_hbb_kfiou_r50_fpn_1x_dota_oc | model | log |
| ResNet50 (1024,1024,200) | 69.77 | le135 | 1x | 3.38 | 15.3 | - | 2 | rotated_retinanet_hbb_kfiou_r50_fpn_1x_dota_le135 | model | log |
| ResNet50 (1024,1024,200) | 69.79 | le135 | 1x | 3.38 | 17.2 | - | 2 | rotated_retinanet_obb_r50_fpn_1x_dota_le135 | model | log |
| ResNet50 (1024,1024,200) | 69.80 | oc | 1x | 3.54 | 12.4 | - | 2 | r3det_r50_fpn_1x_dota_oc | model | log |
| ResNet50 (1024,1024,200) | 69.94 | oc | 1x | 3.39 | 15.6 | - | 2 | rotated_retinanet_hbb_kld_r50_fpn_1x_dota_oc | model | log |
| ResNet50 (1024,1024,200) | 70.18 | oc | 1x | 3.23 | 15.6 | - | 2 | r3det_tiny_r50_fpn_1x_dota_oc | model | log |
| ResNet50 (1024,1024,200) | 70.64 | le90 | 1x | 3.12 | 18.2 | - | 2 | rotated_atss_obb_r50_fpn_1x_dota_le90 | model | log |
| ResNet50 (1024,1024,200) | 70.70 | le90 | 1x | 4.18 | - | 2 | rotated_fcos_sep_angle_r50_fpn_1x_dota_le90 | model | log | |
| ResNet50 (1024,1024,200) | 71.28 | le90 | 1x | 4.18 | - | 2 | rotated_fcos_r50_fpn_1x_dota_le90 | model | log | |
| ResNet50 (1024,1024,200) | 71.76 | le90 | 1x | 4.23 | - | 2 | rotated_fcos_csl_gaussian_r50_fpn_1x_dota_le90 | model | log | |
| ResNet50 (1024,1024,200) | 71.83 | oc | 1x | 3.54 | 12.4 | - | 2 | r3det_kld_r50_fpn_1x_dota_oc | model | log |
| ResNet50 (1024,1024,200) | 71.89 | le90 | 1x | 4.18 | - | 2 | rotated_fcos_kld_r50_fpn_1x_dota_le90 | model | log | |
| ResNet50 (1024,1024,200) | 71.94 | le135 | 1x | 3.45 | 16.1 | - | 2 | oriented_reppoints_r50_fpn_1x_dota_le135 | model | log |
| ResNet50 (1024,1024,200) | 72.29 | le135 | 1x | 3.19 | 18.8 | - | 2 | rotated_atss_obb_r50_fpn_1x_dota_le135 | model | log |
| ResNet50 (1024,1024,200) | 72.68 | oc | 1x | 3.62 | 12.2 | - | 2 | r3det_kfiou_ln_r50_fpn_1x_dota_oc | model | log |
| ResNet50 (1024,1024,200) | 72.76 | oc | 1x | 3.44 | 14.0 | - | 2 | r3det_tiny_kld_r50_fpn_1x_dota_oc | model | log |
| ResNet50 (1024,1024,200) | 73.23 | le90 | 1x | 8.45 | 16.4 | - | 2 | gliding_vertex_r50_fpn_1x_dota_le90 | model | log |
| ResNet50 (1024,1024,200) | 73.40 | le90 | 1x | 8.46 | 16.5 | - | 2 | rotated_faster_rcnn_r50_fpn_1x_dota_le90 | model | log |
| ResNet50 (1024,1024,200) | 73.45 | oc | 40e | 3.45 | 16.1 | - | 2 | cfa_r50_fpn_40e_dota_oc | model | log |
| ResNet50 (1024,1024,200) | 73.91 | le135 | 1x | 3.14 | 15.5 | - | 2 | s2anet_r50_fpn_1x_dota_le135 | model | log |
| ResNet50 (1024,1024,200) | 74.19 | le135 | 1x | 2.17 | 17.4 | - | 2 | s2anet_r50_fpn_fp16_1x_dota_le135 | model | log |
| ResNet50 (1024,1024,200) | 75.63 | le90 | 1x | 7.37 | 21.2 | - | 2 | oriented_rcnn_r50_fpn_fp16_1x_dota_le90 | model | log |
| ResNet50 (1024,1024,200) | 75.69 | le90 | 1x | 8.46 | 16.2 | - | 2 | oriented_rcnn_r50_fpn_1x_dota_le90 | model | log |
| ResNet50 (1024,1024,200) | 75.75 | le90 | 1x | 7.56 | 19.3 | - | 2 | roi_trans_r50_fpn_fp16_1x_dota_le90 | model | log |
| ReResNet50 (1024,1024,200) | 75.99 | le90 | 1x | 7.71 | 13.3 | - | 2 | redet_re50_refpn_fp16_1x_dota_le90 | model | log |
| ResNet50 (1024,1024,200) | 76.08 | le90 | 1x | 8.67 | 14.4 | - | 2 | roi_trans_r50_fpn_1x_dota_le90 | model | log |
| ResNet50 (1024,1024,500) | 76.50 | le90 | 1x | 17.5 | MS+RR | 2 | rotated_retinanet_obb_r50_fpn_1x_dota_ms_rr_le90 | model | log | |
| ReResNet50 (1024,1024,200) | 76.68 | le90 | 1x | 9.32 | 10.9 | - | 2 | redet_re50_refpn_1x_dota_le90 | model | log |
| Swin-tiny (1024,1024,200) | 77.51 | le90 | 1x | 10.9 | - | 2 | roi_trans_swin_tiny_fpn_1x_dota_le90 | model | log | |
| ResNet50 (1024,1024,500) | 79.66 | le90 | 1x | 14.4 | MS+RR | 2 | roi_trans_r50_fpn_1x_dota_ms_rr_le90 | model | log | |
| ReResNet50 (1024,1024,500) | 79.87 | le90 | 1x | 10.9 | MS+RR | 2 | redet_re50_refpn_1x_dota_ms_rr_le90 | model | log |
MS表示多尺度图像增强。RR表示随机旋转增强。
上述模型都是使用 1 * 1080ti/2080ti 训练得到的，并且在 1 * 2080ti 上进行推理测试。