I am putting the code in a public repo… in an attempt to let others check my work… cuz to me it is something I must be doing wrong, not the object detection examples

  
      
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JRGEMCP/bootstrap-pytorch-torchvision-fasterrcnn 13

PyTorch + Torch Vision to simplify object detection in Pytorch - JRGEMCP/bootstrap-pytorch-torchvision-fasterrcnn


  
  
    
    
  
  


When I run the low res model and play around with the RPN Non-Max-Supression… the loss is exploding on one metric in particular
LOSS_RPN_BOX_REG
Epoch: [2] [155/156] eta: 0:00:00 lr: 0.050000 loss: 38.1844 (43.6607) loss_classifier: 0.2736 (0.3118) loss_box_reg: 0.0004 (0.0181) loss_objectness: 0.0002 (0.0008) loss_rpn_box_reg: 37.8964 (43.3301) time: 0.4153 data: 0.0037 max mem: 4502
Epoch: [2] Total time: 0:01:05 (0.4176 s / it)
Epoch: [3] [  0/156] eta: 0:01:13 lr: 0.050000 loss: 24.2694 (24.2694) loss_classifier: 0.2735 (0.2735) loss_box_reg: 0.0005 (0.0005) loss_objectness: 0.0006 (0.0006) loss_rpn_box_reg: 23.9949 (23.9949) time: 0.4712 data: 0.0709 max mem: 4502
Epoch: [3] [100/156] eta: 0:00:23 lr: 0.050000 loss: 38.8969 (40.1606) loss_classifier: 0.2732 (0.3010) loss_box_reg: 0.0001 (0.0084) loss_objectness: 0.0002 (0.0007) loss_rpn_box_reg: 38.6098 (39.8506) time: 0.4059 data: 0.0035 max mem: 4502
Epoch: [3] [155/156] eta: 0:00:00 lr: 0.050000 loss: 39.8755 (42.1183) loss_classifier: 0.2743 (0.3043) loss_box_reg: 0.0001 (0.0111) loss_objectness: 0.0010 (0.0008) loss_rpn_box_reg: 39.5986 (41.8022) time: 0.4031 data: 0.0035 max mem: 4502
Epoch: [3] Total time: 0:01:05 (0.4174 s / it)
Epoch: [4] [  0/156] eta: 0:01:13 lr: 0.050000 loss: 14.2214 (14.2214) loss_classifier: 0.4443 (0.4443) loss_box_reg: 0.2627 (0.2627) loss_objectness: 0.0004 (0.0004) loss_rpn_box_reg: 13.5139 (13.5139) time: 0.4684 data: 0.0689 max mem: 4502
Epoch: [4] [100/156] eta: 0:00:23 lr: 0.050000 loss: 40.8120 (43.2169) loss_classifier: 0.2728 (0.3103) loss_box_reg: 0.0001 (0.0207) loss_objectness: 0.0001 (0.0007) loss_rpn_box_reg: 40.5402 (42.8852) time: 0.4003 data: 0.0035 max mem: 4502
Epoch: [4] [155/156] eta: 0:00:00 lr: 0.050000 loss: 42.3280 (43.5196) loss_classifier: 0.2765 (0.3094) loss_box_reg: 0.0002 (0.0168) loss_objectness: 0.0002 (0.0010) loss_rpn_box_reg: 42.0376 (43.1924) time: 0.4200 data: 0.0034 max mem: 4502
Epoch: [4] Total time: 0:01:05 (0.4173 s / it)
Epoch: [5] [  0/156] eta: 0:01:28 lr: 0.050000 loss: 53.0796 (53.0796) loss_classifier: 0.2744 (0.2744) loss_box_reg: 0.0003 (0.0003) loss_objectness: 0.0001 (0.0001) loss_rpn_box_reg: 52.8048 (52.8048) time: 0.5694 data: 0.0829 max mem: 4502
Epoch: [5] [100/156] eta: 0:00:23 lr: 0.050000 loss: 24.7626 (35.3056) loss_classifier: 0.2733 (0.3193) loss_box_reg: 0.0004 (0.0287) loss_objectness: 0.0003 (0.0003) loss_rpn_box_reg: 24.4884 (34.9572) time: 0.4204 data: 0.0034 max mem: 4502