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	---
	license: other
	tags:
	- generated_from_trainer
	model-index:
	- name: segformer-b0-finetuned-segments-toolwear
	results: []
	---

	<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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	# segformer-b0-finetuned-segments-toolwear

	This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co./nvidia/mit-b0) on an unknown dataset.
	It achieves the following results on the evaluation set:
	- Loss: 0.0387
	- Mean Iou: 0.4153
	- Mean Accuracy: 0.8306
	- Overall Accuracy: 0.8306
	- Accuracy Unlabeled: nan
	- Accuracy Tool: nan
	- Accuracy Wear: 0.8306
	- Iou Unlabeled: 0.0
	- Iou Tool: nan
	- Iou Wear: 0.8306

	## 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 Tool \| Accuracy Wear \| Iou Unlabeled \| Iou Tool \| Iou Wear \|
	\|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:-------------:\|:----------------:\|:------------------:\|:-------------:\|:-------------:\|:-------------:\|:--------:\|:--------:\|
	\| 0.8874 \| 1.18 \| 20 \| 0.8947 \| 0.4560 \| 0.9119 \| 0.9119 \| nan \| nan \| 0.9119 \| 0.0 \| nan \| 0.9119 \|
	\| 0.5792 \| 2.35 \| 40 \| 0.5701 \| 0.2551 \| 0.7653 \| 0.7653 \| nan \| nan \| 0.7653 \| 0.0 \| 0.0 \| 0.7653 \|
	\| 0.5031 \| 3.53 \| 60 \| 0.4364 \| 0.4652 \| 0.9305 \| 0.9305 \| nan \| nan \| 0.9305 \| 0.0 \| nan \| 0.9305 \|
	\| 0.4025 \| 4.71 \| 80 \| 0.4218 \| 0.4529 \| 0.9058 \| 0.9058 \| nan \| nan \| 0.9058 \| 0.0 \| nan \| 0.9058 \|
	\| 0.3212 \| 5.88 \| 100 \| 0.3115 \| 0.4447 \| 0.8894 \| 0.8894 \| nan \| nan \| 0.8894 \| 0.0 \| nan \| 0.8894 \|
	\| 0.2797 \| 7.06 \| 120 \| 0.2646 \| 0.3291 \| 0.6582 \| 0.6582 \| nan \| nan \| 0.6582 \| 0.0 \| nan \| 0.6582 \|
	\| 0.2143 \| 8.24 \| 140 \| 0.2223 \| 0.4177 \| 0.8354 \| 0.8354 \| nan \| nan \| 0.8354 \| 0.0 \| nan \| 0.8354 \|
	\| 0.1951 \| 9.41 \| 160 \| 0.1815 \| 0.4313 \| 0.8625 \| 0.8625 \| nan \| nan \| 0.8625 \| 0.0 \| nan \| 0.8625 \|
	\| 0.1475 \| 10.59 \| 180 \| 0.1571 \| 0.4014 \| 0.8029 \| 0.8029 \| nan \| nan \| 0.8029 \| 0.0 \| nan \| 0.8029 \|
	\| 0.1523 \| 11.76 \| 200 \| 0.1386 \| 0.4242 \| 0.8485 \| 0.8485 \| nan \| nan \| 0.8485 \| 0.0 \| nan \| 0.8485 \|
	\| 0.1324 \| 12.94 \| 220 \| 0.1127 \| 0.4429 \| 0.8858 \| 0.8858 \| nan \| nan \| 0.8858 \| 0.0 \| nan \| 0.8858 \|
	\| 0.0977 \| 14.12 \| 240 \| 0.1064 \| 0.4458 \| 0.8916 \| 0.8916 \| nan \| nan \| 0.8916 \| 0.0 \| nan \| 0.8916 \|
	\| 0.0858 \| 15.29 \| 260 \| 0.0915 \| 0.4561 \| 0.9122 \| 0.9122 \| nan \| nan \| 0.9122 \| 0.0 \| nan \| 0.9122 \|
	\| 0.0782 \| 16.47 \| 280 \| 0.0934 \| 0.4611 \| 0.9223 \| 0.9223 \| nan \| nan \| 0.9223 \| 0.0 \| nan \| 0.9223 \|
	\| 0.0763 \| 17.65 \| 300 \| 0.0757 \| 0.4542 \| 0.9084 \| 0.9084 \| nan \| nan \| 0.9084 \| 0.0 \| nan \| 0.9084 \|
	\| 0.0665 \| 18.82 \| 320 \| 0.0718 \| 0.4259 \| 0.8518 \| 0.8518 \| nan \| nan \| 0.8518 \| 0.0 \| nan \| 0.8518 \|
	\| 0.0658 \| 20.0 \| 340 \| 0.0636 \| 0.3842 \| 0.7685 \| 0.7685 \| nan \| nan \| 0.7685 \| 0.0 \| nan \| 0.7685 \|
	\| 0.0672 \| 21.18 \| 360 \| 0.0590 \| 0.4212 \| 0.8425 \| 0.8425 \| nan \| nan \| 0.8425 \| 0.0 \| nan \| 0.8425 \|
	\| 0.05 \| 22.35 \| 380 \| 0.0586 \| 0.4502 \| 0.9005 \| 0.9005 \| nan \| nan \| 0.9005 \| 0.0 \| nan \| 0.9005 \|
	\| 0.0525 \| 23.53 \| 400 \| 0.0546 \| 0.3913 \| 0.7827 \| 0.7827 \| nan \| nan \| 0.7827 \| 0.0 \| nan \| 0.7827 \|
	\| 0.0451 \| 24.71 \| 420 \| 0.0528 \| 0.4383 \| 0.8767 \| 0.8767 \| nan \| nan \| 0.8767 \| 0.0 \| nan \| 0.8767 \|
	\| 0.0407 \| 25.88 \| 440 \| 0.0494 \| 0.4337 \| 0.8675 \| 0.8675 \| nan \| nan \| 0.8675 \| 0.0 \| nan \| 0.8675 \|
	\| 0.0462 \| 27.06 \| 460 \| 0.0510 \| 0.3397 \| 0.6795 \| 0.6795 \| nan \| nan \| 0.6795 \| 0.0 \| nan \| 0.6795 \|
	\| 0.0376 \| 28.24 \| 480 \| 0.0451 \| 0.4271 \| 0.8541 \| 0.8541 \| nan \| nan \| 0.8541 \| 0.0 \| nan \| 0.8541 \|
	\| 0.0349 \| 29.41 \| 500 \| 0.0456 \| 0.4173 \| 0.8346 \| 0.8346 \| nan \| nan \| 0.8346 \| 0.0 \| nan \| 0.8346 \|
	\| 0.0406 \| 30.59 \| 520 \| 0.0449 \| 0.3863 \| 0.7726 \| 0.7726 \| nan \| nan \| 0.7726 \| 0.0 \| nan \| 0.7726 \|
	\| 0.0333 \| 31.76 \| 540 \| 0.0438 \| 0.4361 \| 0.8721 \| 0.8721 \| nan \| nan \| 0.8721 \| 0.0 \| nan \| 0.8721 \|
	\| 0.0331 \| 32.94 \| 560 \| 0.0480 \| 0.3417 \| 0.6834 \| 0.6834 \| nan \| nan \| 0.6834 \| 0.0 \| nan \| 0.6834 \|
	\| 0.0756 \| 34.12 \| 580 \| 0.0420 \| 0.4362 \| 0.8723 \| 0.8723 \| nan \| nan \| 0.8723 \| 0.0 \| nan \| 0.8723 \|
	\| 0.0295 \| 35.29 \| 600 \| 0.0437 \| 0.3674 \| 0.7349 \| 0.7349 \| nan \| nan \| 0.7349 \| 0.0 \| nan \| 0.7349 \|
	\| 0.0325 \| 36.47 \| 620 \| 0.0409 \| 0.4087 \| 0.8174 \| 0.8174 \| nan \| nan \| 0.8174 \| 0.0 \| nan \| 0.8174 \|
	\| 0.0299 \| 37.65 \| 640 \| 0.0405 \| 0.4150 \| 0.8299 \| 0.8299 \| nan \| nan \| 0.8299 \| 0.0 \| nan \| 0.8299 \|
	\| 0.0384 \| 38.82 \| 660 \| 0.0416 \| 0.3690 \| 0.7380 \| 0.7380 \| nan \| nan \| 0.7380 \| 0.0 \| nan \| 0.7380 \|
	\| 0.0269 \| 40.0 \| 680 \| 0.0393 \| 0.4356 \| 0.8713 \| 0.8713 \| nan \| nan \| 0.8713 \| 0.0 \| nan \| 0.8713 \|
	\| 0.025 \| 41.18 \| 700 \| 0.0389 \| 0.3976 \| 0.7952 \| 0.7952 \| nan \| nan \| 0.7952 \| 0.0 \| nan \| 0.7952 \|
	\| 0.0256 \| 42.35 \| 720 \| 0.0392 \| 0.3729 \| 0.7459 \| 0.7459 \| nan \| nan \| 0.7459 \| 0.0 \| nan \| 0.7459 \|
	\| 0.0303 \| 43.53 \| 740 \| 0.0400 \| 0.3869 \| 0.7738 \| 0.7738 \| nan \| nan \| 0.7738 \| 0.0 \| nan \| 0.7738 \|
	\| 0.0244 \| 44.71 \| 760 \| 0.0389 \| 0.4022 \| 0.8044 \| 0.8044 \| nan \| nan \| 0.8044 \| 0.0 \| nan \| 0.8044 \|
	\| 0.03 \| 45.88 \| 780 \| 0.0387 \| 0.4003 \| 0.8006 \| 0.8006 \| nan \| nan \| 0.8006 \| 0.0 \| nan \| 0.8006 \|
	\| 0.0238 \| 47.06 \| 800 \| 0.0384 \| 0.4073 \| 0.8147 \| 0.8147 \| nan \| nan \| 0.8147 \| 0.0 \| nan \| 0.8147 \|
	\| 0.0278 \| 48.24 \| 820 \| 0.0394 \| 0.4151 \| 0.8302 \| 0.8302 \| nan \| nan \| 0.8302 \| 0.0 \| nan \| 0.8302 \|
	\| 0.0281 \| 49.41 \| 840 \| 0.0387 \| 0.4153 \| 0.8306 \| 0.8306 \| nan \| nan \| 0.8306 \| 0.0 \| nan \| 0.8306 \|


	### Framework versions

	- Transformers 4.28.0
	- Pytorch 2.1.0+cu121
	- Datasets 2.16.0
	- Tokenizers 0.13.3