dungeon-maps-seg-v0.0.1
This model is a fine-tuned version of nvidia/mit-b0 on the cephelos/dungeon-maps-seg dataset. It achieves the following results on the evaluation set:
- Loss: 0.0361
- Mean Iou: 0.9518
- Mean Accuracy: 0.9783
- Overall Accuracy: 0.9893
- Accuracy Unlabeled: nan
- Accuracy Room: 0.9923
- Accuracy Wall: 0.9490
- Accuracy Outside: 0.9935
- Iou Unlabeled: nan
- Iou Room: 0.9857
- Iou Wall: 0.8788
- Iou Outside: 0.9911
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 6e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
Training results
Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabeled | Accuracy Room | Accuracy Wall | Accuracy Outside | Iou Unlabeled | Iou Room | Iou Wall | Iou Outside |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0.2922 | 0.7692 | 20 | 0.2745 | 0.8581 | 0.9561 | 0.9598 | nan | 0.9526 | 0.9466 | 0.9690 | nan | 0.9466 | 0.6646 | 0.9632 |
0.2099 | 1.5385 | 40 | 0.2072 | 0.8639 | 0.9584 | 0.9625 | nan | 0.9680 | 0.9472 | 0.9599 | nan | 0.9600 | 0.6732 | 0.9584 |
0.2009 | 2.3077 | 60 | 0.1688 | 0.8968 | 0.9623 | 0.9741 | nan | 0.9718 | 0.9316 | 0.9835 | nan | 0.9649 | 0.7477 | 0.9778 |
0.1258 | 3.0769 | 80 | 0.1482 | 0.8991 | 0.9676 | 0.9745 | nan | 0.9773 | 0.9492 | 0.9762 | nan | 0.9708 | 0.7529 | 0.9736 |
0.1624 | 3.8462 | 100 | 0.1333 | 0.9115 | 0.9682 | 0.9785 | nan | 0.9807 | 0.9410 | 0.9829 | nan | 0.9734 | 0.7817 | 0.9795 |
0.1098 | 4.6154 | 120 | 0.1079 | 0.9173 | 0.9624 | 0.9805 | nan | 0.9859 | 0.9145 | 0.9868 | nan | 0.9753 | 0.7950 | 0.9817 |
0.1629 | 5.3846 | 140 | 0.1041 | 0.9195 | 0.9711 | 0.9806 | nan | 0.9790 | 0.9462 | 0.9881 | nan | 0.9738 | 0.8013 | 0.9833 |
0.1243 | 6.1538 | 160 | 0.0872 | 0.9243 | 0.9675 | 0.9821 | nan | 0.9852 | 0.9288 | 0.9884 | nan | 0.9766 | 0.8125 | 0.9836 |
0.0974 | 6.9231 | 180 | 0.0996 | 0.9217 | 0.9731 | 0.9811 | nan | 0.9754 | 0.9525 | 0.9915 | nan | 0.9717 | 0.8073 | 0.9861 |
0.0861 | 7.6923 | 200 | 0.0798 | 0.9248 | 0.9706 | 0.9821 | nan | 0.9829 | 0.9403 | 0.9886 | nan | 0.9764 | 0.8142 | 0.9836 |
0.0928 | 8.4615 | 220 | 0.0718 | 0.9276 | 0.9740 | 0.9828 | nan | 0.9830 | 0.9507 | 0.9882 | nan | 0.9773 | 0.8209 | 0.9847 |
0.0583 | 9.2308 | 240 | 0.0726 | 0.9240 | 0.9686 | 0.9822 | nan | 0.9870 | 0.9326 | 0.9862 | nan | 0.9789 | 0.8111 | 0.9821 |
0.0886 | 10.0 | 260 | 0.0700 | 0.9296 | 0.9740 | 0.9835 | nan | 0.9845 | 0.9491 | 0.9885 | nan | 0.9786 | 0.8250 | 0.9852 |
0.1133 | 10.7692 | 280 | 0.0651 | 0.9322 | 0.9633 | 0.9848 | nan | 0.9912 | 0.9064 | 0.9922 | nan | 0.9794 | 0.8301 | 0.9872 |
0.0821 | 11.5385 | 300 | 0.0616 | 0.9302 | 0.9721 | 0.9836 | nan | 0.9833 | 0.9417 | 0.9912 | nan | 0.9779 | 0.8270 | 0.9857 |
0.07 | 12.3077 | 320 | 0.0586 | 0.9394 | 0.9690 | 0.9864 | nan | 0.9896 | 0.9232 | 0.9942 | nan | 0.9810 | 0.8485 | 0.9887 |
0.076 | 13.0769 | 340 | 0.0566 | 0.9349 | 0.9651 | 0.9854 | nan | 0.9919 | 0.9113 | 0.9920 | nan | 0.9803 | 0.8365 | 0.9878 |
0.0577 | 13.8462 | 360 | 0.0570 | 0.9378 | 0.9755 | 0.9857 | nan | 0.9850 | 0.9488 | 0.9926 | nan | 0.9797 | 0.8452 | 0.9886 |
0.1261 | 14.6154 | 380 | 0.0548 | 0.9403 | 0.9739 | 0.9864 | nan | 0.9867 | 0.9410 | 0.9939 | nan | 0.9808 | 0.8511 | 0.9891 |
0.0583 | 15.3846 | 400 | 0.0523 | 0.9428 | 0.9736 | 0.9871 | nan | 0.9895 | 0.9379 | 0.9934 | nan | 0.9820 | 0.8566 | 0.9896 |
0.0602 | 16.1538 | 420 | 0.0488 | 0.9409 | 0.9737 | 0.9866 | nan | 0.9899 | 0.9394 | 0.9917 | nan | 0.9820 | 0.8519 | 0.9887 |
0.0728 | 16.9231 | 440 | 0.0504 | 0.9380 | 0.9716 | 0.9860 | nan | 0.9907 | 0.9335 | 0.9905 | nan | 0.9819 | 0.8448 | 0.9873 |
0.0507 | 17.6923 | 460 | 0.0503 | 0.9378 | 0.9739 | 0.9858 | nan | 0.9892 | 0.9424 | 0.9901 | nan | 0.9820 | 0.8445 | 0.9869 |
0.077 | 18.4615 | 480 | 0.0474 | 0.9429 | 0.9740 | 0.9871 | nan | 0.9876 | 0.9396 | 0.9949 | nan | 0.9819 | 0.8570 | 0.9897 |
0.2137 | 19.2308 | 500 | 0.0500 | 0.9413 | 0.9763 | 0.9866 | nan | 0.9892 | 0.9489 | 0.9907 | nan | 0.9823 | 0.8532 | 0.9882 |
0.0991 | 20.0 | 520 | 0.0459 | 0.9440 | 0.9719 | 0.9875 | nan | 0.9899 | 0.9309 | 0.9950 | nan | 0.9827 | 0.8595 | 0.9898 |
0.0691 | 20.7692 | 540 | 0.0447 | 0.9451 | 0.9743 | 0.9877 | nan | 0.9906 | 0.9390 | 0.9933 | nan | 0.9831 | 0.8623 | 0.9897 |
0.0602 | 21.5385 | 560 | 0.0447 | 0.9462 | 0.9754 | 0.9879 | nan | 0.9885 | 0.9424 | 0.9952 | nan | 0.9828 | 0.8654 | 0.9904 |
0.0469 | 22.3077 | 580 | 0.0429 | 0.9466 | 0.9767 | 0.9879 | nan | 0.9889 | 0.9471 | 0.9940 | nan | 0.9830 | 0.8664 | 0.9903 |
0.0553 | 23.0769 | 600 | 0.0445 | 0.9468 | 0.9722 | 0.9882 | nan | 0.9913 | 0.9301 | 0.9952 | nan | 0.9832 | 0.8666 | 0.9906 |
0.0671 | 23.8462 | 620 | 0.0424 | 0.9455 | 0.9748 | 0.9878 | nan | 0.9900 | 0.9407 | 0.9938 | nan | 0.9833 | 0.8635 | 0.9898 |
0.0431 | 24.6154 | 640 | 0.0417 | 0.9475 | 0.9732 | 0.9883 | nan | 0.9921 | 0.9331 | 0.9943 | nan | 0.9836 | 0.8681 | 0.9907 |
0.0381 | 25.3846 | 660 | 0.0429 | 0.9449 | 0.9763 | 0.9876 | nan | 0.9881 | 0.9467 | 0.9942 | nan | 0.9827 | 0.8620 | 0.9901 |
0.0503 | 26.1538 | 680 | 0.0403 | 0.9471 | 0.9746 | 0.9882 | nan | 0.9924 | 0.9384 | 0.9929 | nan | 0.9841 | 0.8669 | 0.9902 |
0.0685 | 26.9231 | 700 | 0.0410 | 0.9496 | 0.9743 | 0.9888 | nan | 0.9913 | 0.9361 | 0.9957 | nan | 0.9842 | 0.8732 | 0.9912 |
0.0381 | 27.6923 | 720 | 0.0398 | 0.9494 | 0.9771 | 0.9887 | nan | 0.9906 | 0.9466 | 0.9942 | nan | 0.9843 | 0.8729 | 0.9909 |
0.0587 | 28.4615 | 740 | 0.0397 | 0.9500 | 0.9760 | 0.9889 | nan | 0.9913 | 0.9421 | 0.9947 | nan | 0.9843 | 0.8743 | 0.9913 |
0.0573 | 29.2308 | 760 | 0.0402 | 0.9489 | 0.9756 | 0.9887 | nan | 0.9913 | 0.9411 | 0.9945 | nan | 0.9845 | 0.8715 | 0.9908 |
0.0686 | 30.0 | 780 | 0.0386 | 0.9499 | 0.9763 | 0.9889 | nan | 0.9914 | 0.9433 | 0.9944 | nan | 0.9844 | 0.8740 | 0.9912 |
0.037 | 30.7692 | 800 | 0.0386 | 0.9503 | 0.9752 | 0.9890 | nan | 0.9925 | 0.9387 | 0.9944 | nan | 0.9849 | 0.8748 | 0.9911 |
0.0565 | 31.5385 | 820 | 0.0389 | 0.9497 | 0.9773 | 0.9888 | nan | 0.9898 | 0.9471 | 0.9950 | nan | 0.9840 | 0.8738 | 0.9913 |
0.0405 | 32.3077 | 840 | 0.0383 | 0.9483 | 0.9743 | 0.9886 | nan | 0.9933 | 0.9366 | 0.9930 | nan | 0.9848 | 0.8698 | 0.9903 |
0.0618 | 33.0769 | 860 | 0.0383 | 0.9497 | 0.9757 | 0.9889 | nan | 0.9920 | 0.9408 | 0.9942 | nan | 0.9847 | 0.8734 | 0.9910 |
0.0398 | 33.8462 | 880 | 0.0379 | 0.9494 | 0.9766 | 0.9888 | nan | 0.9917 | 0.9446 | 0.9936 | nan | 0.9846 | 0.8729 | 0.9908 |
0.0488 | 34.6154 | 900 | 0.0376 | 0.9501 | 0.9769 | 0.9889 | nan | 0.9915 | 0.9450 | 0.9941 | nan | 0.9851 | 0.8745 | 0.9907 |
0.0574 | 35.3846 | 920 | 0.0379 | 0.9512 | 0.9762 | 0.9892 | nan | 0.9914 | 0.9419 | 0.9953 | nan | 0.9849 | 0.8773 | 0.9914 |
0.0331 | 36.1538 | 940 | 0.0368 | 0.9514 | 0.9764 | 0.9893 | nan | 0.9921 | 0.9424 | 0.9947 | nan | 0.9852 | 0.8777 | 0.9913 |
0.0578 | 36.9231 | 960 | 0.0368 | 0.9520 | 0.9770 | 0.9894 | nan | 0.9916 | 0.9443 | 0.9951 | nan | 0.9852 | 0.8790 | 0.9917 |
0.0471 | 37.6923 | 980 | 0.0369 | 0.9517 | 0.9779 | 0.9893 | nan | 0.9912 | 0.9480 | 0.9947 | nan | 0.9852 | 0.8786 | 0.9915 |
0.0388 | 38.4615 | 1000 | 0.0369 | 0.9511 | 0.9776 | 0.9892 | nan | 0.9904 | 0.9473 | 0.9952 | nan | 0.9846 | 0.8770 | 0.9916 |
0.0455 | 39.2308 | 1020 | 0.0367 | 0.9517 | 0.9753 | 0.9894 | nan | 0.9928 | 0.9379 | 0.9950 | nan | 0.9853 | 0.8784 | 0.9915 |
0.0359 | 40.0 | 1040 | 0.0360 | 0.9516 | 0.9773 | 0.9893 | nan | 0.9917 | 0.9457 | 0.9945 | nan | 0.9853 | 0.8783 | 0.9913 |
0.0281 | 40.7692 | 1060 | 0.0363 | 0.9519 | 0.9775 | 0.9894 | nan | 0.9917 | 0.9462 | 0.9946 | nan | 0.9854 | 0.8790 | 0.9913 |
0.0394 | 41.5385 | 1080 | 0.0367 | 0.9508 | 0.9769 | 0.9891 | nan | 0.9922 | 0.9446 | 0.9939 | nan | 0.9854 | 0.8761 | 0.9909 |
0.0286 | 42.3077 | 1100 | 0.0360 | 0.9525 | 0.9761 | 0.9896 | nan | 0.9924 | 0.9405 | 0.9953 | nan | 0.9855 | 0.8804 | 0.9917 |
0.028 | 43.0769 | 1120 | 0.0363 | 0.9509 | 0.9791 | 0.9891 | nan | 0.9909 | 0.9530 | 0.9936 | nan | 0.9850 | 0.8767 | 0.9911 |
0.0523 | 43.8462 | 1140 | 0.0366 | 0.9526 | 0.9777 | 0.9895 | nan | 0.9919 | 0.9466 | 0.9947 | nan | 0.9856 | 0.8806 | 0.9915 |
0.0492 | 44.6154 | 1160 | 0.0364 | 0.9523 | 0.9764 | 0.9895 | nan | 0.9926 | 0.9419 | 0.9948 | nan | 0.9856 | 0.8799 | 0.9915 |
0.0331 | 45.3846 | 1180 | 0.0356 | 0.9523 | 0.9781 | 0.9894 | nan | 0.9906 | 0.9484 | 0.9954 | nan | 0.9852 | 0.8799 | 0.9917 |
0.0443 | 46.1538 | 1200 | 0.0358 | 0.9533 | 0.9772 | 0.9897 | nan | 0.9921 | 0.9443 | 0.9953 | nan | 0.9857 | 0.8824 | 0.9918 |
0.0331 | 46.9231 | 1220 | 0.0356 | 0.9527 | 0.9771 | 0.9896 | nan | 0.9929 | 0.9441 | 0.9943 | nan | 0.9858 | 0.8808 | 0.9915 |
0.0546 | 47.6923 | 1240 | 0.0357 | 0.9532 | 0.9774 | 0.9897 | nan | 0.9916 | 0.9450 | 0.9956 | nan | 0.9856 | 0.8821 | 0.9919 |
0.0297 | 48.4615 | 1260 | 0.0351 | 0.9526 | 0.9776 | 0.9896 | nan | 0.9925 | 0.9461 | 0.9942 | nan | 0.9857 | 0.8807 | 0.9915 |
0.053 | 49.2308 | 1280 | 0.0349 | 0.9527 | 0.9779 | 0.9896 | nan | 0.9921 | 0.9471 | 0.9945 | nan | 0.9856 | 0.8809 | 0.9916 |
0.0474 | 50.0 | 1300 | 0.0361 | 0.9518 | 0.9783 | 0.9893 | nan | 0.9923 | 0.9490 | 0.9935 | nan | 0.9857 | 0.8788 | 0.9911 |
Framework versions
- Transformers 4.40.2
- Pytorch 2.2.0+cpu
- Datasets 2.19.1
- Tokenizers 0.19.1
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Base model
nvidia/mit-b0
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this model