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Focal Loss for Dense Rotation Object Detection

Performance（deprecated）

Due to the improvement of the code, the performance of this repo is gradually improving, so the experimental results in this file are for reference only.

DOTA1.0

Model Backbone Training data Val data mAP Model Link Anchor Reg. Loss Angle Range lr schd Data Augmentation GPU Image/GPU Configs
RetinaNet ResNet50_v1d 600->800 DOTA1.0 trainval DOTA1.0 test 53.17 - H smooth L1 90 1x No 8X GeForce RTX 2080 Ti 1 cfgs_res50_dota_v3.py
RetinaNet ResNet50_v1d 600->800 DOTA1.0 trainval DOTA1.0 test 63.18 model H smooth L1 90 1x No 1X GeForce RTX 2080 Ti 1 cfgs_res50_dota_v4.py
RetinaNet ResNet50_v1d 600->800 DOTA1.0 trainval DOTA1.0 test 62.79 - H smooth L1 90 2x No 8X GeForce RTX 2080 Ti 1 cfgs_res50_dota_v8.py
RetinaNet ResNet101_v1d 600->800 DOTA1.0 trainval DOTA1.0 test 64.73 - H smooth L1 90 1x No 1X GeForce RTX 2080 Ti 1 cfgs_res101_dota_v9.py
RetinaNet ResNet152_v1d 600->800 DOTA1.0 trainval DOTA1.0 test 66.97 - H smooth L1 90 1x No 2X GeForce RTX 2080 Ti 1 cfgs_res152_dota_v12.py
RetinaNet ResNet50_v1d 600->800 DOTA1.0 trainval DOTA1.0 test 65.11 - H smooth L1 + atan(theta) 90 1x No 1X GeForce RTX 2080 Ti 1 cfgs_res50_dota_v16.py
RetinaNet ResNet50_v1d 600->800 DOTA1.0 trainval DOTA1.0 test 64.10 - H smooth L1 180 1x No 1X GeForce RTX 2080 Ti 1 cfgs_res50_dota_v15.py
RetinaNet ResNet50_v1d 600->800 DOTA1.0 trainval DOTA1.0 test 62.76 model R smooth L1 90 1x No 1X GeForce RTX 2080 Ti 1 cfgs_res50_dota_v1.py
RetinaNet ResNet50_v1d 600->800 DOTA1.0 trainval DOTA1.0 test 62.25 - R smooth L1 90 2x No 8X GeForce RTX 2080 Ti 1 cfgs_res50_dota_v10.py
RetinaNet ResNet50_v1d 600->800 DOTA1.0 trainval DOTA1.0 test 68.65 - R iou-smooth L1 90 1x No 1X GeForce RTX 2080 Ti 1 cfgs_res50_dota_v5.py

Some model results are slightly higher than in the paper due to retraining.