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Banconxuan / RTM3D

The official PyTorch Implementation of RTM3D and KM3D for Monocular 3D Object Detection
MIT License
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Loss increases with training #8

Open enteguo opened 3 years ago

enteguo commented 3 years ago

Why the loss increases as the epoch increases？ Did the network not converge? And the model I trained cannot get the same result as the author.

Banconxuan commented 3 years ago

The loss is actually set to the average of the IOU, so it keeps increasing. Can you provide your setup and final results?

enteguo commented 3 years ago

The loss is actually set to the average of the IOU, so it keeps increasing. Can you provide your setup and final results?

The IOU metric is the higher the better, but the best model is selected as the smallest loss if log_dict_val[opt.metric] < best: best = log_dict_val[opt.metric] save_model(os.path.join(opt.save_dir, 'model_best.pth'), epoch, model)

Banconxuan commented 3 years ago

We did not use model_best because loss does not represent the final result on the Kitti. We typically use model_last to verify the results.

Banconxuan commented 3 years ago

Here is the log of the first table in the README. You can check to see if it's matched with your loss. 2020-12-14-13-04: epoch: 1 |loss 0.044084 | hm_loss 2.984045 | hp_loss 7.373300 | hm_hp_loss 3.112973 | hp_offset_loss 0.275482 | wh_loss 13.003339 | off_loss 0.280270 | dim_loss 0.720487 | rot_loss 1.926005 | prob_loss 5.468876 | coor_loss 26.215098 | box_score 0.044084 | time 3.716667 | 2020-12-14-13-08: epoch: 2 |loss 0.128893 | hm_loss 1.912404 | hp_loss 6.088788 | hm_hp_loss 2.217700 | hp_offset_loss 0.251246 | wh_loss 5.940583 | off_loss 0.252135 | dim_loss 0.302496 | rot_loss 1.867203 | prob_loss 6.009809 | coor_loss 5.777905 | box_score 0.128893 | time 3.516667 | 2020-12-14-13-11: epoch: 3 |loss 0.138301 | hm_loss 1.500287 | hp_loss 6.017632 | hm_hp_loss 1.912993 | hp_offset_loss 0.250973 | wh_loss 5.316960 | off_loss 0.248090 | dim_loss 0.234139 | rot_loss 1.861147 | prob_loss 6.104925 | coor_loss 5.714835 | box_score 0.138301 | time 3.516667 | 2020-12-14-13-15: epoch: 4 |loss 0.121327 | hm_loss 1.276340 | hp_loss 5.860701 | hm_hp_loss 1.811430 | hp_offset_loss 0.248182 | wh_loss 4.611679 | off_loss 0.243031 | dim_loss 0.206281 | rot_loss 1.854213 | prob_loss 5.511015 | coor_loss 6.001116 | box_score 0.121327 | time 3.516667 | 2020-12-14-13-18: epoch: 5 |loss 0.106950 | hm_loss 1.142965 | hp_loss 5.510242 | hm_hp_loss 1.759445 | hp_offset_loss 0.247544 | wh_loss 4.356024 | off_loss 0.241491 | dim_loss 0.201071 | rot_loss 1.814175 | prob_loss 5.258084 | coor_loss 6.796084 | box_score 0.106950 | time 3.533333 | loss 0.105778 | hm_loss 1.411631 | hp_loss 4.577029 | hm_hp_loss 2.050297 | hp_offset_loss 0.249297 | wh_loss 3.996953 | off_loss 0.243236 | dim_loss 0.214372 | rot_loss 1.684043 | prob_loss 0.289017 | coor_loss 4.758581 | box_score 0.105778 | time 5.250000 | 2020-12-14-13-27: epoch: 6 |loss 0.130595 | hm_loss 1.127580 | hp_loss 4.450998 | hm_hp_loss 1.730497 | hp_offset_loss 0.247487 | wh_loss 4.113551 | off_loss 0.238403 | dim_loss 0.204593 | rot_loss 1.728893 | prob_loss 5.967966 | coor_loss 5.288455 | box_score 0.130595 | time 3.516667 | 2020-12-14-13-31: epoch: 7 |loss 0.141804 | hm_loss 1.024674 | hp_loss 4.297338 | hm_hp_loss 1.663806 | hp_offset_loss 0.246276 | wh_loss 4.032637 | off_loss 0.235340 | dim_loss 0.194922 | rot_loss 1.675376 | prob_loss 6.240923 | coor_loss 4.622164 | box_score 0.141804 | time 3.650000 | 2020-12-14-13-35: epoch: 8 |loss 0.149742 | hm_loss 0.970030 | hp_loss 3.864174 | hm_hp_loss 1.621934 | hp_offset_loss 0.246064 | wh_loss 4.040950 | off_loss 0.231528 | dim_loss 0.194330 | rot_loss 1.623938 | prob_loss 6.457511 | coor_loss 4.720210 | box_score 0.149742 | time 3.450000 | 2020-12-14-13-38: epoch: 9 |loss 0.162581 | hm_loss 0.950546 | hp_loss 3.289582 | hm_hp_loss 1.583668 | hp_offset_loss 0.244849 | wh_loss 3.577725 | off_loss 0.231074 | dim_loss 0.192668 | rot_loss 1.552094 | prob_loss 6.742824 | coor_loss 4.504929 | box_score 0.162581 | time 3.566667 | 2020-12-14-13-42: epoch: 10 |loss 0.179495 | hm_loss 0.910656 | hp_loss 2.868239 | hm_hp_loss 1.539423 | hp_offset_loss 0.244365 | wh_loss 2.827252 | off_loss 0.229095 | dim_loss 0.187940 | rot_loss 1.499403 | prob_loss 7.127785 | coor_loss 4.100062 | box_score 0.179495 | time 3.500000 | loss 0.139458 | hm_loss 1.347842 | hp_loss 3.242095 | hm_hp_loss 1.864668 | hp_offset_loss 0.245964 | wh_loss 2.805524 | off_loss 0.233277 | dim_loss 0.204717 | rot_loss 1.589614 | prob_loss 0.316808 | coor_loss 3.519975 | box_score 0.139458 | time 4.883333 | 2020-12-14-13-50: epoch: 11 |loss 0.186704 | hm_loss 0.853902 | hp_loss 2.753708 | hm_hp_loss 1.487165 | hp_offset_loss 0.242540 | wh_loss 2.541394 | off_loss 0.226046 | dim_loss 0.183071 | rot_loss 1.464723 | prob_loss 7.201556 | coor_loss 3.887345 | box_score 0.186704 | time 3.783333 | 2020-12-14-13-54: epoch: 12 |loss 0.200134 | hm_loss 0.825686 | hp_loss 2.654255 | hm_hp_loss 1.443880 | hp_offset_loss 0.243814 | wh_loss 2.387757 | off_loss 0.225815 | dim_loss 0.182106 | rot_loss 1.435089 | prob_loss 7.491909 | coor_loss 3.716617 | box_score 0.200134 | time 3.616667 | 2020-12-14-13-58: epoch: 13 |loss 0.210695 | hm_loss 0.763484 | hp_loss 2.441777 | hm_hp_loss 1.394905 | hp_offset_loss 0.243407 | wh_loss 2.149736 | off_loss 0.222854 | dim_loss 0.174530 | rot_loss 1.393514 | prob_loss 7.682131 | coor_loss 3.536792 | box_score 0.210695 | time 3.650000 | 2020-12-14-14-02: epoch: 14 |loss 0.210503 | hm_loss 0.747867 | hp_loss 2.404311 | hm_hp_loss 1.352858 | hp_offset_loss 0.241668 | wh_loss 2.045009 | off_loss 0.221470 | dim_loss 0.172916 | rot_loss 1.387047 | prob_loss 7.609778 | coor_loss 3.541536 | box_score 0.210503 | time 3.666667 | 2020-12-14-14-05: epoch: 15 |loss 0.217532 | hm_loss 0.709659 | hp_loss 2.369144 | hm_hp_loss 1.309137 | hp_offset_loss 0.241419 | wh_loss 1.973580 | off_loss 0.219495 | dim_loss 0.170254 | rot_loss 1.368146 | prob_loss 7.744495 | coor_loss 3.469948 | box_score 0.217532 | time 3.883333 | loss 0.142822 | hm_loss 1.412460 | hp_loss 3.087654 | hm_hp_loss 1.833494 | hp_offset_loss 0.245019 | wh_loss 2.700186 | off_loss 0.233692 | dim_loss 0.209742 | rot_loss 1.596757 | prob_loss 0.316924 | coor_loss 3.648750 | box_score 0.142822 | time 4.483333 | 2020-12-14-14-14: epoch: 16 |loss 0.224624 | hm_loss 0.680445 | hp_loss 2.203977 | hm_hp_loss 1.265828 | hp_offset_loss 0.240634 | wh_loss 1.818737 | off_loss 0.219005 | dim_loss 0.167310 | rot_loss 1.352880 | prob_loss 7.859932 | coor_loss 3.410154 | box_score 0.224624 | time 3.733333 | 2020-12-14-14-18: epoch: 17 |loss 0.225550 | hm_loss 0.673697 | hp_loss 2.229354 | hm_hp_loss 1.234297 | hp_offset_loss 0.241141 | wh_loss 1.825357 | off_loss 0.216857 | dim_loss 0.165664 | rot_loss 1.344739 | prob_loss 7.944559 | coor_loss 3.298520 | box_score 0.225550 | time 3.716667 | 2020-12-14-14-21: epoch: 18 |loss 0.233201 | hm_loss 0.647285 | hp_loss 2.259419 | hm_hp_loss 1.200519 | hp_offset_loss 0.239290 | wh_loss 1.765657 | off_loss 0.217453 | dim_loss 0.160941 | rot_loss 1.341308 | prob_loss 7.947845 | coor_loss 3.346833 | box_score 0.233201 | time 3.466667 | 2020-12-14-14-25: epoch: 19 |loss 0.235999 | hm_loss 0.620227 | hp_loss 2.202577 | hm_hp_loss 1.163884 | hp_offset_loss 0.238825 | wh_loss 1.703584 | off_loss 0.214569 | dim_loss 0.158148 | rot_loss 1.328353 | prob_loss 8.025284 | coor_loss 3.242356 | box_score 0.235999 | time 3.450000 | 2020-12-14-14-28: epoch: 20 |loss 0.241926 | hm_loss 0.607374 | hp_loss 2.104469 | hm_hp_loss 1.135619 | hp_offset_loss 0.237751 | wh_loss 1.688710 | off_loss 0.215229 | dim_loss 0.156151 | rot_loss 1.326543 | prob_loss 8.115642 | coor_loss 3.154946 | box_score 0.241926 | time 3.450000 | loss 0.164422 | hm_loss 1.432034 | hp_loss 2.871780 | hm_hp_loss 1.748205 | hp_offset_loss 0.243438 | wh_loss 2.269549 | off_loss 0.233212 | dim_loss 0.213518 | rot_loss 1.574219 | prob_loss 0.339318 | coor_loss 3.193785 | box_score 0.164422 | time 4.816667 | 2020-12-14-14-36: epoch: 21 |loss 0.238978 | hm_loss 0.594549 | hp_loss 2.112707 | hm_hp_loss 1.106700 | hp_offset_loss 0.238027 | wh_loss 1.642313 | off_loss 0.215271 | dim_loss 0.152835 | rot_loss 1.320809 | prob_loss 8.066637 | coor_loss 3.148218 | box_score 0.238978 | time 3.483333 | 2020-12-14-14-40: epoch: 22 |loss 0.245296 | hm_loss 0.588695 | hp_loss 2.049800 | hm_hp_loss 1.086192 | hp_offset_loss 0.237507 | wh_loss 1.644176 | off_loss 0.213906 | dim_loss 0.150005 | rot_loss 1.315500 | prob_loss 8.209228 | coor_loss 3.028924 | box_score 0.245296 | time 3.516667 | 2020-12-14-14-43: epoch: 23 |loss 0.248355 | hm_loss 0.570865 | hp_loss 1.916510 | hm_hp_loss 1.058679 | hp_offset_loss 0.236655 | wh_loss 1.631849 | off_loss 0.210924 | dim_loss 0.149003 | rot_loss 1.315744 | prob_loss 8.278384 | coor_loss 2.938839 | box_score 0.248355 | time 3.466667 | 2020-12-14-14-47: epoch: 24 |loss 0.259964 | hm_loss 0.570738 | hp_loss 1.863532 | hm_hp_loss 1.047271 | hp_offset_loss 0.236344 | wh_loss 1.614176 | off_loss 0.213314 | dim_loss 0.146024 | rot_loss 1.308850 | prob_loss 8.512192 | coor_loss 2.793516 | box_score 0.259964 | time 3.600000 | 2020-12-14-14-51: epoch: 25 |loss 0.265068 | hm_loss 0.567533 | hp_loss 1.679654 | hm_hp_loss 1.025860 | hp_offset_loss 0.235757 | wh_loss 1.565367 | off_loss 0.210198 | dim_loss 0.143237 | rot_loss 1.303796 | prob_loss 8.570004 | coor_loss 2.697738 | box_score 0.265068 | time 3.616667 | loss 0.147622 | hm_loss 1.451181 | hp_loss 2.585227 | hm_hp_loss 1.740359 | hp_offset_loss 0.243200 | wh_loss 2.240504 | off_loss 0.232177 | dim_loss 0.223633 | rot_loss 1.565979 | prob_loss 0.325702 | coor_loss 3.126695 | box_score 0.147622 | time 4.633333 | 2020-12-14-14-59: epoch: 26 |loss 0.266661 | hm_loss 0.547527 | hp_loss 1.629609 | hm_hp_loss 1.000014 | hp_offset_loss 0.235546 | wh_loss 1.543540 | off_loss 0.209766 | dim_loss 0.139074 | rot_loss 1.301433 | prob_loss 8.615607 | coor_loss 2.680099 | box_score 0.266661 | time 3.666667 | 2020-12-14-15-03: epoch: 27 |loss 0.275325 | hm_loss 0.535328 | hp_loss 1.621457 | hm_hp_loss 0.986487 | hp_offset_loss 0.235004 | wh_loss 1.530051 | off_loss 0.209765 | dim_loss 0.135354 | rot_loss 1.297935 | prob_loss 8.717052 | coor_loss 2.562016 | box_score 0.275325 | time 3.600000 | 2020-12-14-15-07: epoch: 28 |loss 0.277870 | hm_loss 0.531047 | hp_loss 1.560089 | hm_hp_loss 0.970706 | hp_offset_loss 0.234448 | wh_loss 1.525195 | off_loss 0.210103 | dim_loss 0.134994 | rot_loss 1.298668 | prob_loss 8.754414 | coor_loss 2.487761 | box_score 0.277870 | time 3.666667 | 2020-12-14-15-10: epoch: 29 |loss 0.282505 | hm_loss 0.526499 | hp_loss 1.539053 | hm_hp_loss 0.955362 | hp_offset_loss 0.234273 | wh_loss 1.513894 | off_loss 0.208806 | dim_loss 0.131695 | rot_loss 1.296360 | prob_loss 8.787892 | coor_loss 2.439906 | box_score 0.282505 | time 3.616667 | 2020-12-14-15-14: epoch: 30 |loss 0.286422 | hm_loss 0.510063 | hp_loss 1.561617 | hm_hp_loss 0.941019 | hp_offset_loss 0.233100 | wh_loss 1.510498 | off_loss 0.208567 | dim_loss 0.129240 | rot_loss 1.292191 | prob_loss 8.833938 | coor_loss 2.550809 | box_score 0.286422 | time 3.683333 | loss 0.164712 | hm_loss 1.522472 | hp_loss 2.628460 | hm_hp_loss 1.774448 | hp_offset_loss 0.242456 | wh_loss 2.226959 | off_loss 0.230922 | dim_loss 0.221159 | rot_loss 1.605364 | prob_loss 0.337883 | coor_loss 3.152199 | box_score 0.164712 | time 5.083333 | 2020-12-14-15-23: epoch: 31 |loss 0.287343 | hm_loss 0.496780 | hp_loss 1.418605 | hm_hp_loss 0.923076 | hp_offset_loss 0.232693 | wh_loss 1.470316 | off_loss 0.206506 | dim_loss 0.127808 | rot_loss 1.291139 | prob_loss 8.842289 | coor_loss 2.448236 | box_score 0.287343 | time 3.516667 | 2020-12-14-15-27: epoch: 32 |loss 0.287561 | hm_loss 0.484506 | hp_loss 1.413389 | hm_hp_loss 0.905220 | hp_offset_loss 0.232938 | wh_loss 1.469458 | off_loss 0.208075 | dim_loss 0.126495 | rot_loss 1.289572 | prob_loss 8.886903 | coor_loss 2.436062 | box_score 0.287561 | time 3.650000 | 2020-12-14-15-30: epoch: 33 |loss 0.289847 | hm_loss 0.487869 | hp_loss 1.555691 | hm_hp_loss 0.899695 | hp_offset_loss 0.233268 | wh_loss 1.451232 | off_loss 0.207941 | dim_loss 0.124892 | rot_loss 1.286597 | prob_loss 8.885811 | coor_loss 2.440913 | box_score 0.289847 | time 3.583333 | 2020-12-14-15-34: epoch: 34 |loss 0.297230 | hm_loss 0.479121 | hp_loss 1.472896 | hm_hp_loss 0.879027 | hp_offset_loss 0.232135 | wh_loss 1.375735 | off_loss 0.206174 | dim_loss 0.124174 | rot_loss 1.284072 | prob_loss 8.939864 | coor_loss 2.355746 | box_score 0.297230 | time 3.583333 | 2020-12-14-15-37: epoch: 35 |loss 0.302996 | hm_loss 0.470707 | hp_loss 1.339412 | hm_hp_loss 0.874015 | hp_offset_loss 0.232121 | wh_loss 1.422153 | off_loss 0.204341 | dim_loss 0.121812 | rot_loss 1.281789 | prob_loss 9.059833 | coor_loss 2.314291 | box_score 0.302996 | time 3.566667 | loss 0.176223 | hm_loss 1.496088 | hp_loss 2.462655 | hm_hp_loss 1.715955 | hp_offset_loss 0.242669 | wh_loss 2.073548 | off_loss 0.236401 | dim_loss 0.217768 | rot_loss 1.585927 | prob_loss 0.343845 | coor_loss 2.758748 | box_score 0.176223 | time 4.516667 | 2020-12-14-15-46: epoch: 36 |loss 0.298594 | hm_loss 0.457214 | hp_loss 1.358216 | hm_hp_loss 0.856488 | hp_offset_loss 0.232209 | wh_loss 1.352356 | off_loss 0.206220 | dim_loss 0.118768 | rot_loss 1.277134 | prob_loss 9.036597 | coor_loss 2.373278 | box_score 0.298594 | time 3.600000 | 2020-12-14-15-49: epoch: 37 |loss 0.305660 | hm_loss 0.452113 | hp_loss 1.333471 | hm_hp_loss 0.841049 | hp_offset_loss 0.231337 | wh_loss 1.367597 | off_loss 0.204501 | dim_loss 0.119311 | rot_loss 1.277011 | prob_loss 9.108272 | coor_loss 2.290618 | box_score 0.305660 | time 3.533333 | 2020-12-14-15-53: epoch: 38 |loss 0.307939 | hm_loss 0.455601 | hp_loss 1.268248 | hm_hp_loss 0.840686 | hp_offset_loss 0.231416 | wh_loss 1.344721 | off_loss 0.205219 | dim_loss 0.117170 | rot_loss 1.276992 | prob_loss 9.124114 | coor_loss 2.393493 | box_score 0.307939 | time 3.550000 | 2020-12-14-15-57: epoch: 39 |loss 0.309141 | hm_loss 0.447587 | hp_loss 1.306559 | hm_hp_loss 0.823960 | hp_offset_loss 0.230819 | wh_loss 1.307130 | off_loss 0.204189 | dim_loss 0.116283 | rot_loss 1.274446 | prob_loss 9.079434 | coor_loss 2.295631 | box_score 0.309141 | time 3.583333 | 2020-12-14-16-00: epoch: 40 |loss 0.308153 | hm_loss 0.426134 | hp_loss 1.241959 | hm_hp_loss 0.811073 | hp_offset_loss 0.231470 | wh_loss 1.310461 | off_loss 0.203275 | dim_loss 0.115289 | rot_loss 1.274936 | prob_loss 9.154934 | coor_loss 2.228821 | box_score 0.308153 | time 3.683333 | loss 0.191965 | hm_loss 1.586497 | hp_loss 2.385655 | hm_hp_loss 1.763325 | hp_offset_loss 0.242399 | wh_loss 2.023015 | off_loss 0.235140 | dim_loss 0.216904 | rot_loss 1.562404 | prob_loss 0.365315 | coor_loss 2.601223 | box_score 0.191965 | time 5.433333 | 2020-12-14-16-10: epoch: 41 |loss 0.308941 | hm_loss 0.432578 | hp_loss 1.286097 | hm_hp_loss 0.808901 | hp_offset_loss 0.230110 | wh_loss 1.314326 | off_loss 0.202514 | dim_loss 0.113050 | rot_loss 1.271825 | prob_loss 9.033134 | coor_loss 2.334942 | box_score 0.308941 | time 3.566667 | 2020-12-14-16-13: epoch: 42 |loss 0.311761 | hm_loss 0.421720 | hp_loss 1.175166 | hm_hp_loss 0.796990 | hp_offset_loss 0.229538 | wh_loss 1.267671 | off_loss 0.201102 | dim_loss 0.112199 | rot_loss 1.271798 | prob_loss 9.100187 | coor_loss 2.201243 | box_score 0.311761 | time 3.500000 | 2020-12-14-16-17: epoch: 43 |loss 0.318431 | hm_loss 0.426519 | hp_loss 1.248158 | hm_hp_loss 0.789783 | hp_offset_loss 0.230064 | wh_loss 1.268461 | off_loss 0.200338 | dim_loss 0.108161 | rot_loss 1.266132 | prob_loss 9.209201 | coor_loss 2.154906 | box_score 0.318431 | time 3.500000 | 2020-12-14-16-20: epoch: 44 |loss 0.321733 | hm_loss 0.413429 | hp_loss 1.207436 | hm_hp_loss 0.771295 | hp_offset_loss 0.229294 | wh_loss 1.273384 | off_loss 0.200642 | dim_loss 0.109876 | rot_loss 1.267256 | prob_loss 9.268962 | coor_loss 2.139293 | box_score 0.321733 | time 3.500000 | 2020-12-14-16-24: epoch: 45 |loss 0.317989 | hm_loss 0.411886 | hp_loss 1.282801 | hm_hp_loss 0.774391 | 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wh_loss 1.796512 | off_loss 0.242694 | dim_loss 0.220356 | rot_loss 1.526281 | prob_loss 0.489928 | coor_loss 1.880812 | box_score 0.238273 | time 5.250000 | 2020-12-15-03-57: epoch: 191 |loss 0.548779 | hm_loss 0.260376 | hp_loss 0.985029 | hm_hp_loss 0.576692 | hp_offset_loss 0.217863 | wh_loss 0.849009 | off_loss 0.178999 | dim_loss 0.091987 | rot_loss 1.255712 | prob_loss 9.948807 | coor_loss 0.877675 | box_score 0.548779 | time 3.600000 | 2020-12-15-04-01: epoch: 192 |loss 0.548526 | hm_loss 0.261956 | hp_loss 0.879683 | hm_hp_loss 0.579298 | hp_offset_loss 0.218683 | wh_loss 0.847072 | off_loss 0.179133 | dim_loss 0.091479 | rot_loss 1.254805 | prob_loss 9.936442 | coor_loss 0.871179 | box_score 0.548526 | time 3.700000 | 2020-12-15-04-05: epoch: 193 |loss 0.550434 | hm_loss 0.259977 | hp_loss 0.881722 | hm_hp_loss 0.577618 | hp_offset_loss 0.218972 | wh_loss 0.825408 | off_loss 0.178871 | dim_loss 0.091456 | rot_loss 1.255631 | prob_loss 9.927031 | coor_loss 0.827294 | box_score 0.550434 | time 3.566667 | 2020-12-15-04-08: epoch: 194 |loss 0.550150 | hm_loss 0.261821 | hp_loss 0.881031 | hm_hp_loss 0.571452 | hp_offset_loss 0.218358 | wh_loss 0.833009 | off_loss 0.178587 | dim_loss 0.091112 | rot_loss 1.255568 | prob_loss 9.921176 | coor_loss 0.906775 | box_score 0.550150 | time 3.600000 | 2020-12-15-04-12: epoch: 195 |loss 0.550410 | hm_loss 0.255219 | hp_loss 0.919377 | hm_hp_loss 0.575713 | hp_offset_loss 0.218220 | wh_loss 0.823256 | off_loss 0.178594 | dim_loss 0.091251 | rot_loss 1.253353 | prob_loss 9.907903 | coor_loss 0.883110 | box_score 0.550410 | time 3.566667 | loss 0.238217 | hm_loss 2.014523 | hp_loss 2.244490 | hm_hp_loss 2.029320 | hp_offset_loss 0.246104 | wh_loss 1.797368 | off_loss 0.242988 | dim_loss 0.220478 | rot_loss 1.524609 | prob_loss 0.490382 | coor_loss 1.891905 | box_score 0.238217 | time 4.816667 | 2020-12-15-04-21: epoch: 196 |loss 0.547584 | hm_loss 0.254536 | hp_loss 0.959577 | hm_hp_loss 0.573229 | hp_offset_loss 0.218310 | wh_loss 0.840211 | off_loss 0.179846 | dim_loss 0.091404 | rot_loss 1.257601 | prob_loss 9.977168 | coor_loss 0.860413 | box_score 0.547584 | time 3.550000 | 2020-12-15-04-24: epoch: 197 |loss 0.554953 | hm_loss 0.255699 | hp_loss 0.927061 | hm_hp_loss 0.576498 | hp_offset_loss 0.218282 | wh_loss 0.827344 | off_loss 0.178323 | dim_loss 0.090910 | rot_loss 1.255435 | prob_loss 9.924938 | coor_loss 0.895039 | box_score 0.554953 | time 3.533333 | 2020-12-15-04-28: epoch: 198 |loss 0.550616 | hm_loss 0.255965 | hp_loss 0.954439 | hm_hp_loss 0.567895 | hp_offset_loss 0.217337 | wh_loss 0.821719 | off_loss 0.178626 | dim_loss 0.090760 | rot_loss 1.252727 | prob_loss 9.905042 | coor_loss 0.870823 | box_score 0.550616 | time 3.483333 | 2020-12-15-04-31: epoch: 199 |loss 0.550446 | hm_loss 0.259239 | hp_loss 0.913391 | hm_hp_loss 0.578470 | hp_offset_loss 0.217973 | wh_loss 0.814774 | off_loss 0.178985 | dim_loss 0.092087 | rot_loss 1.255307 | prob_loss 9.939948 | coor_loss 0.832668 | box_score 0.550446 | time 3.483333 | 2020-12-15-04-35: epoch: 200 |loss 0.551346 | hm_loss 0.258593 | hp_loss 0.903056 | hm_hp_loss 0.574036 | hp_offset_loss 0.218549 | wh_loss 0.832422 | off_loss 0.179020 | dim_loss 0.092313 | rot_loss 1.256031 | prob_loss 9.943285 | coor_loss 0.874030 | box_score 0.551346 | time 3.666667 | loss 0.239241 | hm_loss 2.010142 | hp_loss 2.238476 | hm_hp_loss 2.025955 | hp_offset_loss 0.246010 | wh_loss 1.800027 | off_loss 0.242647 | dim_loss 0.221048 | rot_loss 1.523112 | prob_loss 0.489701 | coor_loss 1.876206 | box_score 0.239241 | time 5.500000 |

enteguo commented 3 years ago

Here is the log of the first table in the README. You can check to see if it's matched with your loss.

Thank you very much. I have got gotten similar results as you provided.

Lizhuoling commented 3 years ago

Why your prob_loss in the validation phase (0.49 at epoch200) is much smaller than it in the training phase (9.94 at epoch200)?