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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:
- Loss: 0.0985
- Mean Iou: 0.9498
- Mean Accuracy: 0.9737
- Overall Accuracy: 0.9746
- Accuracy Unlabeled: nan
- Accuracy Traversable: 0.9658
- Accuracy Non-traversable: 0.9815
- Iou Unlabeled: nan
- Iou Traversable: 0.9435
- Iou Non-traversable: 0.9560
- Local tests:
- -Average inference time: 0.20031250105963813
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 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
- Transformers 4.35.0.dev0
- Pytorch 2.1.0+cu118
- Datasets 2.14.5
- Tokenizers 0.14.0