Rank & Sort Loss for Object Detection and Instance Segmentation

S3. More Experiments on RS Loss 5 S3.1. Effect of δRS , the Single Hyper-parameter, for RS Loss. . . . . . . . . . . . . . . . . 5 S3.2. Training Cascade R-CNN [1] with RS Loss 5 S3.3. Hyper-parameters of R-CNN Variants in Table 1 of the Paper . . . . . . . . . . . . 5 S3.4. Using Different Localisation Qualities as Continuous Labels to Supervise Instance Segmentation Methods . . . . . . . . . . 5 S3.5. Details of the Ablation Analysis on Different Degrees of Imbalance . . . . . . . . . 6 S3.6. Effect of RS Loss on Efficiency . . . . . . . 7 S3.6.1 Effect on Training Efficiency . . . . 7 S3.6.2 Effect on Inference Efficiency . . . 7

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