vision image-segmentation generated_from_trainer

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segformer-b0-finetuned-HikingHD

This model is a fine-tuned version of nvidia/mit-b0 on the twdent/HikingHD dataset. It achieves the following results on the evaluation set:

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:

Training results

Training Loss Epoch Step Validation Loss Mean Iou Mean Accuracy Overall Accuracy Accuracy Unlabeled Accuracy Traversable Accuracy Non-traversable Iou Unlabeled Iou Traversable Iou Non-traversable
0.4142 1.33 20 0.6144 0.6186 0.9598 0.9615 nan 0.9452 0.9743 0.0 0.9184 0.9373
0.497 2.67 40 0.2661 0.6281 0.9718 0.9705 nan 0.9825 0.9612 0.0 0.9361 0.9482
0.2705 4.0 60 0.2025 0.6284 0.9698 0.9709 nan 0.9603 0.9792 0.0 0.9355 0.9497
0.1683 5.33 80 0.1914 0.6274 0.9704 0.9701 nan 0.9730 0.9678 0.0 0.9345 0.9478
0.1744 6.67 100 0.1896 0.6219 0.9630 0.9659 nan 0.9393 0.9867 0.0 0.9236 0.9420
0.1522 8.0 120 0.1575 0.9399 0.9680 0.9695 nan 0.9555 0.9805 nan 0.9322 0.9475
0.167 9.33 140 0.1299 0.9528 0.9765 0.9761 nan 0.9793 0.9737 nan 0.9474 0.9582
0.1733 10.67 160 0.1385 0.6274 0.9696 0.9701 nan 0.9659 0.9734 0.0 0.9340 0.9481
0.0839 12.0 180 0.1215 0.9487 0.9732 0.9741 nan 0.9662 0.9802 nan 0.9424 0.9550
0.1237 13.33 200 0.1326 0.9377 0.9666 0.9684 nan 0.9516 0.9815 nan 0.9296 0.9458
0.0814 14.67 220 0.1162 0.9459 0.9710 0.9727 nan 0.9568 0.9851 nan 0.9389 0.9529
0.0823 16.0 240 0.1176 0.9438 0.9703 0.9716 nan 0.9600 0.9806 nan 0.9367 0.9509
0.0758 17.33 260 0.1073 0.9481 0.9730 0.9738 nan 0.9671 0.9790 nan 0.9417 0.9544
0.0722 18.67 280 0.0999 0.9521 0.9755 0.9758 nan 0.9730 0.9780 nan 0.9464 0.9578
0.0745 20.0 300 0.1016 0.9493 0.9744 0.9744 nan 0.9745 0.9742 nan 0.9434 0.9552
0.0747 21.33 320 0.0970 0.9523 0.9754 0.9759 nan 0.9713 0.9795 nan 0.9465 0.9580
0.0698 22.67 340 0.1026 0.9497 0.9736 0.9746 nan 0.9652 0.9819 nan 0.9434 0.9559
0.0705 24.0 360 0.1242 0.9346 0.9642 0.9668 nan 0.9423 0.9860 nan 0.9257 0.9435
0.0859 25.33 380 0.1081 0.9423 0.9687 0.9708 nan 0.9512 0.9862 nan 0.9347 0.9499
0.0633 26.67 400 0.1094 0.9423 0.9687 0.9709 nan 0.9513 0.9861 nan 0.9347 0.9500
0.0753 28.0 420 0.1055 0.9433 0.9694 0.9714 nan 0.9535 0.9854 nan 0.9359 0.9508
0.0435 29.33 440 0.1014 0.9475 0.9721 0.9735 nan 0.9608 0.9835 nan 0.9409 0.9542
0.1026 30.67 460 0.1031 0.9459 0.9714 0.9727 nan 0.9610 0.9818 nan 0.9391 0.9527
0.0545 32.0 480 0.1023 0.9465 0.9716 0.9730 nan 0.9605 0.9827 nan 0.9397 0.9533
0.0515 33.33 500 0.0970 0.9491 0.9731 0.9743 nan 0.9628 0.9833 nan 0.9427 0.9555
0.082 34.67 520 0.0887 0.9549 0.9767 0.9773 nan 0.9725 0.9810 nan 0.9494 0.9604
0.0483 36.0 540 0.0939 0.9515 0.9746 0.9756 nan 0.9666 0.9825 nan 0.9455 0.9576
0.0563 37.33 560 0.0904 0.9539 0.9761 0.9768 nan 0.9709 0.9814 nan 0.9483 0.9596
0.0517 38.67 580 0.0907 0.9538 0.9760 0.9767 nan 0.9698 0.9822 nan 0.9481 0.9595
0.0508 40.0 600 0.1080 0.9435 0.9697 0.9714 nan 0.9556 0.9838 nan 0.9361 0.9508
0.0408 41.33 620 0.0969 0.9497 0.9735 0.9746 nan 0.9642 0.9827 nan 0.9434 0.9560
0.036 42.67 640 0.0973 0.9498 0.9735 0.9747 nan 0.9642 0.9828 nan 0.9435 0.9561
0.09 44.0 660 0.0932 0.9527 0.9753 0.9762 nan 0.9682 0.9824 nan 0.9469 0.9586
0.0402 45.33 680 0.0963 0.9506 0.9739 0.9751 nan 0.9644 0.9834 nan 0.9444 0.9568
0.0675 46.67 700 0.1034 0.9465 0.9715 0.9730 nan 0.9592 0.9838 nan 0.9397 0.9534
0.0431 48.0 720 0.0996 0.9486 0.9728 0.9741 nan 0.9623 0.9833 nan 0.9421 0.9551
0.0602 49.33 740 0.0985 0.9498 0.9737 0.9746 nan 0.9658 0.9815 nan 0.9435 0.9560

Framework versions