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	---
	license: other
	base_model: nvidia/mit-b5
	tags:
	- vision
	- image-segmentation
	- generated_from_trainer
	model-index:
	- name: segformer-b5-finetuned-HikingHD
	results: []
	---

	<!-- This model card has been generated automatically according to the information the Trainer had access to. You
	should probably proofread and complete it, then remove this comment. -->

	# segformer-b5-finetuned-HikingHD

	This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the twdent/HikingHD dataset.
	It achieves the following results on the evaluation set:
	- Loss: 0.1077
	- Mean Iou: 0.6364
	- Mean Accuracy: 0.9770
	- Overall Accuracy: 0.9771
	- Accuracy Unlabeled: nan
	- Accuracy Traversable: 0.9758
	- Accuracy Non-traversable: 0.9781
	- Iou Unlabeled: 0.0
	- Iou Traversable: 0.9493
	- Iou Non-traversable: 0.9600
	- Local Testing:
	- Average inference time: 0.9943107657962376

	## 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 \|
	\|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:-------------:\|:----------------:\|:------------------:\|:--------------------:\|:------------------------:\|:-------------:\|:---------------:\|:-------------------:\|
	\| 0.1595 \| 1.33 \| 20 \| 0.1789 \| 0.9314 \| 0.9657 \| 0.9649 \| nan \| 0.9727 \| 0.9588 \| nan \| 0.9240 \| 0.9388 \|
	\| 0.3593 \| 2.67 \| 40 \| 0.1137 \| 0.9429 \| 0.9731 \| 0.9709 \| nan \| 0.9911 \| 0.9551 \| nan \| 0.9372 \| 0.9485 \|
	\| 0.1002 \| 4.0 \| 60 \| 0.0979 \| 0.9363 \| 0.9661 \| 0.9677 \| nan \| 0.9531 \| 0.9791 \| nan \| 0.9282 \| 0.9444 \|
	\| 0.0348 \| 5.33 \| 80 \| 0.0933 \| 0.9442 \| 0.9713 \| 0.9717 \| nan \| 0.9675 \| 0.9750 \| nan \| 0.9375 \| 0.9509 \|
	\| 0.0374 \| 6.67 \| 100 \| 0.0884 \| 0.9459 \| 0.9714 \| 0.9727 \| nan \| 0.9611 \| 0.9817 \| nan \| 0.9391 \| 0.9528 \|
	\| 0.0447 \| 8.0 \| 120 \| 0.0886 \| 0.9491 \| 0.9737 \| 0.9743 \| nan \| 0.9684 \| 0.9789 \| nan \| 0.9430 \| 0.9553 \|
	\| 0.0464 \| 9.33 \| 140 \| 0.0790 \| 0.9564 \| 0.9783 \| 0.9780 \| nan \| 0.9804 \| 0.9762 \| nan \| 0.9514 \| 0.9614 \|
	\| 0.0421 \| 10.67 \| 160 \| 0.0868 \| 0.9540 \| 0.9764 \| 0.9768 \| nan \| 0.9733 \| 0.9796 \| nan \| 0.9485 \| 0.9596 \|
	\| 0.0253 \| 12.0 \| 180 \| 0.0887 \| 0.9530 \| 0.9756 \| 0.9763 \| nan \| 0.9700 \| 0.9812 \| nan \| 0.9472 \| 0.9587 \|
	\| 0.0364 \| 13.33 \| 200 \| 0.0960 \| 0.9494 \| 0.9733 \| 0.9745 \| nan \| 0.9638 \| 0.9829 \| nan \| 0.9431 \| 0.9558 \|
	\| 0.0276 \| 14.67 \| 220 \| 0.0980 \| 0.9470 \| 0.9717 \| 0.9732 \| nan \| 0.9595 \| 0.9840 \| nan \| 0.9402 \| 0.9538 \|
	\| 0.0279 \| 16.0 \| 240 \| 0.0914 \| 0.9534 \| 0.9761 \| 0.9765 \| nan \| 0.9725 \| 0.9796 \| nan \| 0.9478 \| 0.9590 \|
	\| 0.026 \| 17.33 \| 260 \| 0.0886 \| 0.9557 \| 0.9778 \| 0.9777 \| nan \| 0.9792 \| 0.9764 \| nan \| 0.9506 \| 0.9609 \|
	\| 0.0228 \| 18.67 \| 280 \| 0.0888 \| 0.9547 \| 0.9775 \| 0.9771 \| nan \| 0.9804 \| 0.9745 \| nan \| 0.9495 \| 0.9599 \|
	\| 0.0259 \| 20.0 \| 300 \| 0.0984 \| 0.9505 \| 0.9742 \| 0.9750 \| nan \| 0.9679 \| 0.9806 \| nan \| 0.9444 \| 0.9565 \|
	\| 0.0306 \| 21.33 \| 320 \| 0.0890 \| 0.9542 \| 0.9763 \| 0.9769 \| nan \| 0.9716 \| 0.9811 \| nan \| 0.9487 \| 0.9598 \|
	\| 0.0305 \| 22.67 \| 340 \| 0.0967 \| 0.6352 \| 0.9752 \| 0.9762 \| nan \| 0.9669 \| 0.9834 \| 0.0 \| 0.9468 \| 0.9586 \|
	\| 0.0219 \| 24.0 \| 360 \| 0.0983 \| 0.9538 \| 0.9764 \| 0.9767 \| nan \| 0.9735 \| 0.9792 \| nan \| 0.9483 \| 0.9593 \|
	\| 0.023 \| 25.33 \| 380 \| 0.0940 \| 0.6368 \| 0.9771 \| 0.9774 \| nan \| 0.9743 \| 0.9799 \| 0.0 \| 0.9499 \| 0.9606 \|
	\| 0.0217 \| 26.67 \| 400 \| 0.0973 \| 0.6360 \| 0.9767 \| 0.9768 \| nan \| 0.9758 \| 0.9776 \| 0.0 \| 0.9486 \| 0.9595 \|
	\| 0.0267 \| 28.0 \| 420 \| 0.1023 \| 0.6360 \| 0.9770 \| 0.9768 \| nan \| 0.9792 \| 0.9749 \| 0.0 \| 0.9487 \| 0.9593 \|
	\| 0.0202 \| 29.33 \| 440 \| 0.0955 \| 0.6376 \| 0.9783 \| 0.9780 \| nan \| 0.9802 \| 0.9764 \| 0.0 \| 0.9514 \| 0.9615 \|
	\| 0.0225 \| 30.67 \| 460 \| 0.1016 \| 0.6360 \| 0.9763 \| 0.9768 \| nan \| 0.9727 \| 0.9800 \| 0.0 \| 0.9484 \| 0.9595 \|
	\| 0.0288 \| 32.0 \| 480 \| 0.1026 \| 0.6354 \| 0.9756 \| 0.9763 \| nan \| 0.9697 \| 0.9815 \| 0.0 \| 0.9473 \| 0.9588 \|
	\| 0.0209 \| 33.33 \| 500 \| 0.0977 \| 0.6370 \| 0.9771 \| 0.9776 \| nan \| 0.9735 \| 0.9808 \| 0.0 \| 0.9502 \| 0.9609 \|
	\| 0.0202 \| 34.67 \| 520 \| 0.1005 \| 0.6367 \| 0.9772 \| 0.9773 \| nan \| 0.9762 \| 0.9782 \| 0.0 \| 0.9497 \| 0.9604 \|
	\| 0.0194 \| 36.0 \| 540 \| 0.1032 \| 0.6365 \| 0.9771 \| 0.9772 \| nan \| 0.9766 \| 0.9776 \| 0.0 \| 0.9495 \| 0.9601 \|
	\| 0.0165 \| 37.33 \| 560 \| 0.1013 \| 0.6373 \| 0.9777 \| 0.9778 \| nan \| 0.9769 \| 0.9785 \| 0.0 \| 0.9508 \| 0.9612 \|
	\| 0.0226 \| 38.67 \| 580 \| 0.1005 \| 0.6367 \| 0.9771 \| 0.9773 \| nan \| 0.9752 \| 0.9790 \| 0.0 \| 0.9497 \| 0.9604 \|
	\| 0.0206 \| 40.0 \| 600 \| 0.1032 \| 0.6369 \| 0.9773 \| 0.9775 \| nan \| 0.9757 \| 0.9789 \| 0.0 \| 0.9501 \| 0.9607 \|
	\| 0.016 \| 41.33 \| 620 \| 0.1007 \| 0.6373 \| 0.9776 \| 0.9777 \| nan \| 0.9761 \| 0.9790 \| 0.0 \| 0.9506 \| 0.9611 \|
	\| 0.012 \| 42.67 \| 640 \| 0.1048 \| 0.6364 \| 0.9768 \| 0.9771 \| nan \| 0.9748 \| 0.9789 \| 0.0 \| 0.9493 \| 0.9601 \|
	\| 0.0261 \| 44.0 \| 660 \| 0.1073 \| 0.6364 \| 0.9770 \| 0.9771 \| nan \| 0.9760 \| 0.9780 \| 0.0 \| 0.9493 \| 0.9600 \|
	\| 0.0125 \| 45.33 \| 680 \| 0.1088 \| 0.6357 \| 0.9761 \| 0.9765 \| nan \| 0.9727 \| 0.9795 \| 0.0 \| 0.9479 \| 0.9591 \|
	\| 0.0259 \| 46.67 \| 700 \| 0.1073 \| 0.6365 \| 0.9770 \| 0.9772 \| nan \| 0.9760 \| 0.9780 \| 0.0 \| 0.9494 \| 0.9601 \|
	\| 0.0173 \| 48.0 \| 720 \| 0.1052 \| 0.6366 \| 0.9770 \| 0.9772 \| nan \| 0.9755 \| 0.9785 \| 0.0 \| 0.9495 \| 0.9602 \|
	\| 0.0162 \| 49.33 \| 740 \| 0.1077 \| 0.6364 \| 0.9770 \| 0.9771 \| nan \| 0.9758 \| 0.9781 \| 0.0 \| 0.9493 \| 0.9600 \|


	### Framework versions

	- Transformers 4.35.0.dev0
	- Pytorch 2.1.0+cu118
	- Datasets 2.14.5
	- Tokenizers 0.14.0