Centerness about atss HOT 2 CLOSED

另外请问你尝试过四卡训练吗？我四卡训练效果还没有FCOS好呢。。。我之前四卡训过FCOS也只有36.5mAP。
DONE (t=9.35s).
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.353
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.523
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.379
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.195
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.383
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.471
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.307
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.516
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.551
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.331
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.600
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.722

from atss.

@haibochina

1. 使用centerness的原因是：一方面是为了公平对比RetinaNet和FCOS，因为FCOS用了centerness；另一方面是centerness能带来一定的提升。
2. 默认设置是8张卡，每张卡2张图，总的batch size是16。如果是4张卡的话，每张卡要4张图，总的batch size才能是16。如果是四张卡，每张卡只有2张图，那batch size只有8，学习率那些都要相应调整应该才行。

from atss.