中国斜学我术大草 实验结果： axAP AP50 APi 7a AP APso AP7s #sc #ar AP APso AP75 .100.0 0.0 0.0 07531.149.4 33.0 30.349.0 31.8 .25 10.8 16.0 1L.7 1 .75 31.4 49.9 33.1 31.9 50.0 34.0 50 30.2 46.7 32.8 0 .7 31.9 507 33.4 1 318 49.4 33.7 .75 31.1 49.4 33.0 05 50 32.9 51.7 352 324 523 33.9 .90 30.8 497 32.3 1.0 33.7 520 362 2 342 531 36.5 .99 28.7 47.4 29.9 2.0 34.0 525 36.5 34.0 525 36.5 999 25.1 41.7 26.1 5.0 25 32.2 49.6 34.8 3 33.8 52.1 36.2 (a)Varying a for CE loss(=0) (b)Varying for FL (w.optimal a) (c)Varying anchor seales and aspects method batch nms size thr AP APso AP75 depth scale AP APso AP7s APs APM APL time OHEM 128 .7 31.1 47.2 33.2 50 400 30.5 47.8 32.7 112 33.8 46.1 64 OHEM 256 7 318 48.8 339 500 32.5 50.9 34.8 13.9 35.8 46.7 7 OHEM 512 7 30.6 47.0 32.6 西 600 343 53.2 36.9 162 374 47.4 OHEM 128 5 32.8 503 35.1 700 35.1 54.2 37.7 18.0 393 46.4 OHEM 256 5 31.0 47.4 33.0 5 800 35.7 55.0 385 189 389 46.3 153 OHEM 512 27.6 42.0 29.2 101400 319 49.5 34.1 11.6 358 48.5 81 OHEM1:3128 31.147.2 33.2 101 500 34.4 53.1 36.8 147 385 49.1 OHEM 1:3 256 5 283 424 30.3 101 600 36.0 55.2 387 174 39.6 49.7 122 OHEM 1:3 512 5 24.0 35.5 25.8 101 700 37.1 56.6 39.8 19.1 40.6 49.4 154 FL aa36.054938.7 101 8M00 37.8 57.5 40.8 202 41.1 49.2198 (d)FL vs.OHEM baselines (with ResNet-101-FPN) (e)Accuracy/speed trade-off RetinaNet (on test-dev) Table 1.Ablation experiments for RetinaNet and Focal Loss (FL).All models are trained on trainval35k and tested on minival unless noted.If not specified,default values are:y=2;anchors for 3 scales and 3 aspect ratios;ResNet-50-FPN backbone;and a 600 pixel train and test image scale.(a)RetinaNet with a-balanced CE achieves at most 31.I AP.(b)In contrast,using FL with the same exact network gives a 2.9 AP gain and is fairly robust to exactya settings.(c)Using 2-3 scale and 3 aspect ratio anchors yields good results after which point performance saturates.(d)FL outperforms the best variants of online hard example mining (OHEM)[31,22]by over3 points AP.(e)Accuracy/Speed trade-off of RetinaNet on test-dev for various network depths and image scales (see also Figure 2).实验结果：