Why is everything -1 in the evaluation and 0 in the training

[blue-q]

Loading and preparing results...
DONE (t=0.14s)
creating index...
index created!
Running per image evaluation...
Evaluate annotation type *bbox*
DONE (t=0.67s).
Accumulating evaluation results...
DONE (t=0.19s).
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = -1.000
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=1000 ] = -1.000
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=1000 ] = -1.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = -1.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = -1.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = -1.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = -1.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=300 ] = -1.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=1000 ] = -1.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = -1.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = -1.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = -1.000
04/29 05:02:47 - mmengine - INFO - bbox_mAP_copypaste: -1.000 -1.000 -1.000 -1.000 -1.000 -1.000
04/29 05:02:47 - mmengine - INFO - Epoch(val) [1][5505/5505]    coco/bbox_mAP: -1.0000  coco/bbox_mAP_50: -1.0000  coco/bbox_mAP_75: -1.0000  coco/bbox_mAP_s: -1.0000  coco/bbox_mAP_m: -1.0000  coco/bbox_mAP_l: -1.0000  data_time: 0.0011  time: 0.1596```


```04/29 05:04:09 - mmengine - INFO - Epoch(train)  [2][  100/81702]  base_lr: 2.0000e-04 lr: 2.0000e-05  eta: 8 days, 10:41:36  time: 0.8367  data_time: 0.0099  memory: 15443  grad_norm: 0.0000  loss: 0.0000  loss_cls: 0.0000  loss_bbox: 0.0000  loss_iou: 0.0000  d0.loss_cls: 0.0000  d0.loss_bbox: 0.0000  d0.loss_iou: 0.0000  d1.loss_cls: 0.0000  d1.loss_bbox: 0.0000  d1.loss_iou: 0.0000  d2.loss_cls: 0.0000  d2.loss_bbox: 0.0000  d2.loss_iou: 0.0000  d3.loss_cls: 0.0000  d3.loss_bbox: 0.0000  d3.loss_iou: 0.0000  d4.loss_cls: 0.0000  d4.loss_bbox: 0.0000  d4.loss_iou: 0.0000  enc_loss_cls: 0.0000  enc_loss_bbox: 0.0000  enc_loss_iou: 0.0000  dn_loss_cls: 0.0000  dn_loss_bbox: 0.0000  dn_loss_iou: 0.0000  d0.dn_loss_cls: 0.0000  d0.dn_loss_bbox: 0.0000  d0.dn_loss_iou: 0.0000  d1.dn_loss_cls: 0.0000  d1.dn_loss_bbox: 0.0000  d1.dn_loss_iou: 0.0000  d2.dn_loss_cls: 0.0000  d2.dn_loss_bbox: 0.0000  d2.dn_loss_iou: 0.0000  d3.dn_loss_cls: 0.0000  d3.dn_loss_bbox: 0.0000  d3.dn_loss_iou: 0.0000  d4.dn_loss_cls: 0.0000  d4.dn_loss_bbox: 0.0000  d4.dn_loss_iou: 0.0000  loss_rpn_cls: 0.0000  loss_rpn_bbox: 0.0000  loss_cls0: 0.0000  acc0: 100.0000  loss_bbox0: 0.0000  loss_cls1: 0.0000  loss_bbox1: 0.0000  loss_centerness1: 0.0000  loss_cls_aux0: 0.0000  loss_bbox_aux0: 0.0000  loss_iou_aux0: 0.0000  d0.loss_cls_aux0: 0.0000  d0.loss_bbox_aux0: 0.0000  d0.loss_iou_aux0: 0.0000  d1.loss_cls_aux0: 0.0000  d1.loss_bbox_aux0: 0.0000  d1.loss_iou_aux0: 0.0000  d2.loss_cls_aux0: 0.0000  d2.loss_bbox_aux0: 0.0000  d2.loss_iou_aux0: 0.0000  d3.loss_cls_aux0: 0.0000  d3.loss_bbox_aux0: 0.0000  d3.loss_iou_aux0: 0.0000  d4.loss_cls_aux0: 0.0000  d4.loss_bbox_aux0: 0.0000  d4.loss_iou_aux0: 0.0000  loss_cls_aux1: 0.0000  loss_bbox_aux1: 0.0000  loss_iou_aux1: 0.0000  d0.loss_cls_aux1: 0.0000  d0.loss_bbox_aux1: 0.0000  d0.loss_iou_aux1: 0.0000  d1.loss_cls_aux1: 0.0000  d1.loss_bbox_aux1: 0.0000  d1.loss_iou_aux1: 0.0000  d2.loss_cls_aux1: 0.0000  d2.loss_bbox_aux1: 0.0000  d2.loss_iou_aux1: 0.0000  d3.loss_cls_aux1: 0.0000  d3.loss_bbox_aux1: 0.0000  d3.loss_iou_aux1: 0.0000  d4.loss_cls_aux1: 0.0000  d4.loss_bbox_aux1: 0.0000  d4.loss_iou_aux1: 0.0000```