vision image-segmentation generated_from_trainer

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segformer-b1-finetuned-HikingHD

This model is a fine-tuned version of nvidia/mit-b1 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
0.3796 1.67 20 0.5835 0.6174 0.9605 0.9617 nan 0.9488 0.9721 0.0 0.9180 0.9343
0.3086 3.33 40 0.2597 0.9230 0.9589 0.9605 nan 0.9439 0.9739 nan 0.9143 0.9318
0.2717 5.0 60 0.2202 0.9386 0.9681 0.9687 nan 0.9626 0.9736 nan 0.9321 0.9451
0.2655 6.67 80 0.2127 0.9334 0.9658 0.9659 nan 0.9646 0.9670 nan 0.9267 0.9402
0.1603 8.33 100 0.1699 0.9383 0.9677 0.9686 nan 0.9601 0.9753 nan 0.9316 0.9450
0.2 10.0 120 0.1692 0.9289 0.9609 0.9637 nan 0.9342 0.9876 nan 0.9200 0.9378
0.1613 11.67 140 0.1389 0.9399 0.9676 0.9695 nan 0.9498 0.9853 nan 0.9328 0.9470
0.185 13.33 160 0.1612 0.9217 0.9566 0.9600 nan 0.9254 0.9878 nan 0.9116 0.9318
0.251 15.0 180 0.1461 0.9277 0.9603 0.9631 nan 0.9340 0.9865 nan 0.9187 0.9368
0.1038 16.67 200 0.1401 0.9248 0.9581 0.9616 nan 0.9258 0.9904 nan 0.9149 0.9346
0.0628 18.33 220 0.1556 0.9195 0.9548 0.9588 nan 0.9171 0.9924 nan 0.9086 0.9303
0.077 20.0 240 0.1439 0.9213 0.9561 0.9598 nan 0.9220 0.9902 nan 0.9110 0.9317
0.0714 21.67 260 0.1267 0.9344 0.9641 0.9666 nan 0.9404 0.9878 nan 0.9263 0.9425
0.081 23.33 280 0.1097 0.9397 0.9672 0.9694 nan 0.9470 0.9874 nan 0.9324 0.9470
0.09 25.0 300 0.1063 0.9402 0.9679 0.9696 nan 0.9522 0.9836 nan 0.9332 0.9472
0.0737 26.67 320 0.1045 0.9395 0.9674 0.9692 nan 0.9502 0.9845 nan 0.9323 0.9466
0.1173 28.33 340 0.1019 0.9427 0.9702 0.9708 nan 0.9644 0.9760 nan 0.9365 0.9488
0.0535 30.0 360 0.1132 0.9387 0.9674 0.9688 nan 0.9549 0.9799 nan 0.9317 0.9456
0.0693 31.67 380 0.1182 0.9340 0.9637 0.9664 nan 0.9389 0.9886 nan 0.9258 0.9422
0.0649 33.33 400 0.1108 0.9374 0.9662 0.9681 nan 0.9483 0.9841 nan 0.9300 0.9448
0.1581 35.0 420 0.1107 0.9368 0.9658 0.9678 nan 0.9473 0.9844 nan 0.9293 0.9443
0.0711 36.67 440 0.1011 0.9414 0.9690 0.9702 nan 0.9578 0.9801 nan 0.9348 0.9479
0.0743 38.33 460 0.1026 0.9400 0.9676 0.9695 nan 0.9500 0.9853 nan 0.9329 0.9471
0.0602 40.0 480 0.1029 0.9407 0.9681 0.9699 nan 0.9521 0.9841 nan 0.9337 0.9476
0.0768 41.67 500 0.1059 0.9386 0.9670 0.9688 nan 0.9502 0.9837 nan 0.9314 0.9458
0.0494 43.33 520 0.1076 0.9375 0.9663 0.9682 nan 0.9484 0.9842 nan 0.9302 0.9449
0.0359 45.0 540 0.1097 0.9369 0.9659 0.9679 nan 0.9473 0.9844 nan 0.9294 0.9444
0.0799 46.67 560 0.1070 0.9379 0.9666 0.9684 nan 0.9493 0.9838 nan 0.9306 0.9452
0.0685 48.33 580 0.1075 0.9378 0.9665 0.9684 nan 0.9489 0.9841 nan 0.9305 0.9452
0.0437 50.0 600 0.1067 0.9379 0.9665 0.9684 nan 0.9485 0.9845 nan 0.9305 0.9452

Framework versions