2022-06-06-组会
2022-06-06-组会
训练结果
跑完了ReDet的训练(基于DOTAv1.0)
训练结果日志:
ap为平均精度
2022-05-31 13:41:46,926 - mmrotate - INFO - Saving checkpoint at 12 epochs
2022-05-31 14:22:35,774 - mmrotate - INFO -
+--------------------+-------+--------+--------+-------+
| class | gts | dets | recall | ap |
+--------------------+-------+--------+--------+-------+
| plane | 18788 | 23259 | 0.896 | 0.799 |
| baseball-diamond | 1087 | 2135 | 0.724 | 0.651 |
| bridge | 4181 | 4962 | 0.584 | 0.470 |
| ground-track-field | 733 | 1090 | 0.618 | 0.556 |
| small-vehicle | 58868 | 110625 | 0.841 | 0.748 |
| large-vehicle | 43075 | 74399 | 0.905 | 0.842 |
| ship | 76153 | 88620 | 0.869 | 0.805 |
| tennis-court | 5923 | 9230 | 0.937 | 0.904 |
| basketball-court | 1180 | 2564 | 0.770 | 0.700 |
| storage-tank | 13670 | 15183 | 0.674 | 0.622 |
| soccer-ball-field | 827 | 2764 | 0.625 | 0.472 |
| roundabout | 973 | 1973 | 0.623 | 0.544 |
| harbor | 15468 | 22735 | 0.791 | 0.687 |
| swimming-pool | 3836 | 8011 | 0.794 | 0.644 |
| helicopter | 1189 | 1946 | 0.807 | 0.765 |
+--------------------+-------+--------+--------+-------+
| mAP | | | | 0.681 |
+--------------------+-------+--------+--------+-------+
2022-05-31 14:22:35,861 - mmrotate - INFO - Exp name: redet_re50_refpn_1x_dota_le90.py
2022-05-31 14:22:35,861 - mmrotate - INFO - Epoch(val) [12][12800] mAP: 0.6807
和论文中贴出来的结果表格的相比,总体mAP(平均精度均值)要少8个百分点:
初步分析是因为Batch Size不一样, GPU不一样,:
我租用的云服务器GPU是2080ti,因为第一次训练怕爆显存,我跑的时候batch_size设置成了1。
日志分析
分类损失和回归损失:
最终loss_rpn_cls: 0.0263, loss_rpn_bbox: 0.0228
总Loss函数损失:
最终总loss: 0.5917
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