update model card README.md

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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
	should probably proofread and complete it, then remove this comment. -->

	# 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: 0.0360
	- Mean Iou: 0.3724
	- Mean Accuracy: 0.7448
	- Overall Accuracy: 0.7448
	- Accuracy Unlabeled: nan
	- Accuracy Tool: nan
	- Accuracy Wear: 0.7448
	- Iou Unlabeled: 0.0
	- Iou Tool: nan
	- Iou Wear: 0.7448

	## 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 \|
	\|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:-------------:\|:----------------:\|:------------------:\|:-------------:\|:-------------:\|:-------------:\|:--------:\|:--------:\|
	\| 0.9316 \| 1.18 \| 20 \| 0.9877 \| 0.4510 \| 0.9020 \| 0.9020 \| nan \| nan \| 0.9020 \| 0.0 \| nan \| 0.9020 \|
	\| 0.6902 \| 2.35 \| 40 \| 0.6556 \| 0.3765 \| 0.7531 \| 0.7531 \| nan \| nan \| 0.7531 \| 0.0 \| nan \| 0.7531 \|
	\| 0.532 \| 3.53 \| 60 \| 0.4585 \| 0.3435 \| 0.6871 \| 0.6871 \| nan \| nan \| 0.6871 \| 0.0 \| nan \| 0.6871 \|
	\| 0.4296 \| 4.71 \| 80 \| 0.3832 \| 0.3900 \| 0.7799 \| 0.7799 \| nan \| nan \| 0.7799 \| 0.0 \| nan \| 0.7799 \|
	\| 0.3318 \| 5.88 \| 100 \| 0.3255 \| 0.3739 \| 0.7478 \| 0.7478 \| nan \| nan \| 0.7478 \| 0.0 \| nan \| 0.7478 \|
	\| 0.281 \| 7.06 \| 120 \| 0.2480 \| 0.3109 \| 0.6219 \| 0.6219 \| nan \| nan \| 0.6219 \| 0.0 \| nan \| 0.6219 \|
	\| 0.2405 \| 8.24 \| 140 \| 0.2410 \| 0.2029 \| 0.4059 \| 0.4059 \| nan \| nan \| 0.4059 \| 0.0 \| nan \| 0.4059 \|
	\| 0.1945 \| 9.41 \| 160 \| 0.1969 \| 0.3366 \| 0.6733 \| 0.6733 \| nan \| nan \| 0.6733 \| 0.0 \| nan \| 0.6733 \|
	\| 0.1612 \| 10.59 \| 180 \| 0.1776 \| 0.3469 \| 0.6938 \| 0.6938 \| nan \| nan \| 0.6938 \| 0.0 \| nan \| 0.6938 \|
	\| 0.1653 \| 11.76 \| 200 \| 0.1455 \| 0.3758 \| 0.7515 \| 0.7515 \| nan \| nan \| 0.7515 \| 0.0 \| nan \| 0.7515 \|
	\| 0.1562 \| 12.94 \| 220 \| 0.1330 \| 0.2652 \| 0.5304 \| 0.5304 \| nan \| nan \| 0.5304 \| 0.0 \| nan \| 0.5304 \|
	\| 0.1053 \| 14.12 \| 240 \| 0.1145 \| 0.3511 \| 0.7022 \| 0.7022 \| nan \| nan \| 0.7022 \| 0.0 \| nan \| 0.7022 \|
	\| 0.1017 \| 15.29 \| 260 \| 0.0989 \| 0.3879 \| 0.7757 \| 0.7757 \| nan \| nan \| 0.7757 \| 0.0 \| nan \| 0.7757 \|
	\| 0.0809 \| 16.47 \| 280 \| 0.0859 \| 0.2622 \| 0.5243 \| 0.5243 \| nan \| nan \| 0.5243 \| 0.0 \| nan \| 0.5243 \|
	\| 0.0861 \| 17.65 \| 300 \| 0.0761 \| 0.3688 \| 0.7375 \| 0.7375 \| nan \| nan \| 0.7375 \| 0.0 \| nan \| 0.7375 \|
	\| 0.0695 \| 18.82 \| 320 \| 0.0720 \| 0.3786 \| 0.7572 \| 0.7572 \| nan \| nan \| 0.7572 \| 0.0 \| nan \| 0.7572 \|
	\| 0.0689 \| 20.0 \| 340 \| 0.0646 \| 0.3964 \| 0.7927 \| 0.7927 \| nan \| nan \| 0.7927 \| 0.0 \| nan \| 0.7927 \|
	\| 0.0592 \| 21.18 \| 360 \| 0.0657 \| 0.3063 \| 0.6126 \| 0.6126 \| nan \| nan \| 0.6126 \| 0.0 \| nan \| 0.6126 \|
	\| 0.0635 \| 22.35 \| 380 \| 0.0581 \| 0.3615 \| 0.7230 \| 0.7230 \| nan \| nan \| 0.7230 \| 0.0 \| nan \| 0.7230 \|
	\| 0.0511 \| 23.53 \| 400 \| 0.0526 \| 0.3622 \| 0.7245 \| 0.7245 \| nan \| nan \| 0.7245 \| 0.0 \| nan \| 0.7245 \|
	\| 0.0518 \| 24.71 \| 420 \| 0.0543 \| 0.3270 \| 0.6540 \| 0.6540 \| nan \| nan \| 0.6540 \| 0.0 \| nan \| 0.6540 \|
	\| 0.0448 \| 25.88 \| 440 \| 0.0522 \| 0.4141 \| 0.8282 \| 0.8282 \| nan \| nan \| 0.8282 \| 0.0 \| nan \| 0.8282 \|
	\| 0.0395 \| 27.06 \| 460 \| 0.0470 \| 0.3519 \| 0.7038 \| 0.7038 \| nan \| nan \| 0.7038 \| 0.0 \| nan \| 0.7038 \|
	\| 0.04 \| 28.24 \| 480 \| 0.0452 \| 0.3870 \| 0.7740 \| 0.7740 \| nan \| nan \| 0.7740 \| 0.0 \| nan \| 0.7740 \|
	\| 0.0386 \| 29.41 \| 500 \| 0.0439 \| 0.3801 \| 0.7603 \| 0.7603 \| nan \| nan \| 0.7603 \| 0.0 \| nan \| 0.7603 \|
	\| 0.0421 \| 30.59 \| 520 \| 0.0437 \| 0.4047 \| 0.8093 \| 0.8093 \| nan \| nan \| 0.8093 \| 0.0 \| nan \| 0.8093 \|
	\| 0.0356 \| 31.76 \| 540 \| 0.0427 \| 0.3675 \| 0.7349 \| 0.7349 \| nan \| nan \| 0.7349 \| 0.0 \| nan \| 0.7349 \|
	\| 0.0368 \| 32.94 \| 560 \| 0.0420 \| 0.3604 \| 0.7208 \| 0.7208 \| nan \| nan \| 0.7208 \| 0.0 \| nan \| 0.7208 \|
	\| 0.0368 \| 34.12 \| 580 \| 0.0408 \| 0.3589 \| 0.7179 \| 0.7179 \| nan \| nan \| 0.7179 \| 0.0 \| nan \| 0.7179 \|
	\| 0.032 \| 35.29 \| 600 \| 0.0395 \| 0.3664 \| 0.7329 \| 0.7329 \| nan \| nan \| 0.7329 \| 0.0 \| nan \| 0.7329 \|
	\| 0.03 \| 36.47 \| 620 \| 0.0394 \| 0.3691 \| 0.7382 \| 0.7382 \| nan \| nan \| 0.7382 \| 0.0 \| nan \| 0.7382 \|
	\| 0.028 \| 37.65 \| 640 \| 0.0383 \| 0.3731 \| 0.7462 \| 0.7462 \| nan \| nan \| 0.7462 \| 0.0 \| nan \| 0.7462 \|
	\| 0.0304 \| 38.82 \| 660 \| 0.0376 \| 0.3796 \| 0.7592 \| 0.7592 \| nan \| nan \| 0.7592 \| 0.0 \| nan \| 0.7592 \|
	\| 0.0314 \| 40.0 \| 680 \| 0.0382 \| 0.3602 \| 0.7204 \| 0.7204 \| nan \| nan \| 0.7204 \| 0.0 \| nan \| 0.7204 \|
	\| 0.0266 \| 41.18 \| 700 \| 0.0385 \| 0.3602 \| 0.7203 \| 0.7203 \| nan \| nan \| 0.7203 \| 0.0 \| nan \| 0.7203 \|
	\| 0.0305 \| 42.35 \| 720 \| 0.0375 \| 0.3413 \| 0.6827 \| 0.6827 \| nan \| nan \| 0.6827 \| 0.0 \| nan \| 0.6827 \|
	\| 0.0334 \| 43.53 \| 740 \| 0.0366 \| 0.3632 \| 0.7263 \| 0.7263 \| nan \| nan \| 0.7263 \| 0.0 \| nan \| 0.7263 \|
	\| 0.0269 \| 44.71 \| 760 \| 0.0359 \| 0.3698 \| 0.7396 \| 0.7396 \| nan \| nan \| 0.7396 \| 0.0 \| nan \| 0.7396 \|
	\| 0.0352 \| 45.88 \| 780 \| 0.0364 \| 0.3679 \| 0.7359 \| 0.7359 \| nan \| nan \| 0.7359 \| 0.0 \| nan \| 0.7359 \|
	\| 0.0398 \| 47.06 \| 800 \| 0.0366 \| 0.3504 \| 0.7008 \| 0.7008 \| nan \| nan \| 0.7008 \| 0.0 \| nan \| 0.7008 \|
	\| 0.0261 \| 48.24 \| 820 \| 0.0361 \| 0.3789 \| 0.7578 \| 0.7578 \| nan \| nan \| 0.7578 \| 0.0 \| nan \| 0.7578 \|
	\| 0.0252 \| 49.41 \| 840 \| 0.0360 \| 0.3724 \| 0.7448 \| 0.7448 \| nan \| nan \| 0.7448 \| 0.0 \| nan \| 0.7448 \|


	### Framework versions

	- Transformers 4.28.0
	- Pytorch 2.1.0+cu118
	- Datasets 2.15.0
	- Tokenizers 0.13.3