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Benchmark and Model Zoo

Results on DOTA v1.0

Backbone mAP Angle lr schd Mem (GB) Inf Time (fps) Aug Batch Size Configs Download
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 means multiple scale image split.

  • RR means random rotation.

The above models are trained with 1 * 1080Ti/2080Ti and inferred with 1 * 2080Ti.

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