基准和模型库

• Rotated RetinaNet-OBB/HBB (ICCV’2017)

• Rotated FasterRCNN-OBB (TPAMI’2017)

• Rotated RepPoints-OBB (ICCV’2019)

• RoI Transformer (CVPR’2019)

• Gliding Vertex (TPAMI’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)	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)	69.49	le135	1x	4.05	8.6	-	2	g_reppoints_r50_fpn_1x_dota_le135	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)	69.51	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)	71.83	oc	1x	3.54	12.4	-	2	r3det_kld_r50_fpn_1x_dota_oc	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 (1024,1024,200)	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 (1024,1024,200)	79.66	le90	1x		14.4	MS+RR	2	roi_trans_r50_fpn_1x_dota_ms_le90	model | log
ReResNet50 (1024,1024,200)	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。