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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: 0.0354
	- Mean Iou: 0.3022
	- Mean Accuracy: 0.6045
	- Overall Accuracy: 0.6045
	- Accuracy Unlabeled: nan
	- Accuracy Tool: nan
	- Accuracy Wear: 0.6045
	- Iou Unlabeled: 0.0
	- Iou Tool: nan
	- Iou Wear: 0.6045

	## 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 \|
	\|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:-------------:\|:----------------:\|:------------------:\|:-------------:\|:-------------:\|:-------------:\|:--------:\|:--------:\|
	\| 0.8671 \| 1.18 \| 20 \| 0.9263 \| 0.4061 \| 0.8122 \| 0.8122 \| nan \| nan \| 0.8122 \| 0.0 \| nan \| 0.8122 \|
	\| 0.5691 \| 2.35 \| 40 \| 0.5998 \| 0.2895 \| 0.5790 \| 0.5790 \| nan \| nan \| 0.5790 \| 0.0 \| nan \| 0.5790 \|
	\| 0.4378 \| 3.53 \| 60 \| 0.3948 \| 0.3106 \| 0.6213 \| 0.6213 \| nan \| nan \| 0.6213 \| 0.0 \| nan \| 0.6213 \|
	\| 0.3842 \| 4.71 \| 80 \| 0.3190 \| 0.2679 \| 0.5357 \| 0.5357 \| nan \| nan \| 0.5357 \| 0.0 \| nan \| 0.5357 \|
	\| 0.3234 \| 5.88 \| 100 \| 0.2883 \| 0.3574 \| 0.7148 \| 0.7148 \| nan \| nan \| 0.7148 \| 0.0 \| nan \| 0.7148 \|
	\| 0.2731 \| 7.06 \| 120 \| 0.2392 \| 0.3456 \| 0.6911 \| 0.6911 \| nan \| nan \| 0.6911 \| 0.0 \| nan \| 0.6911 \|
	\| 0.2137 \| 8.24 \| 140 \| 0.1850 \| 0.1844 \| 0.3688 \| 0.3688 \| nan \| nan \| 0.3688 \| 0.0 \| nan \| 0.3688 \|
	\| 0.1798 \| 9.41 \| 160 \| 0.1692 \| 0.2757 \| 0.5515 \| 0.5515 \| nan \| nan \| 0.5515 \| 0.0 \| nan \| 0.5515 \|
	\| 0.1607 \| 10.59 \| 180 \| 0.1338 \| 0.2978 \| 0.5956 \| 0.5956 \| nan \| nan \| 0.5956 \| 0.0 \| nan \| 0.5956 \|
	\| 0.1399 \| 11.76 \| 200 \| 0.1218 \| 0.2906 \| 0.5811 \| 0.5811 \| nan \| nan \| 0.5811 \| 0.0 \| nan \| 0.5811 \|
	\| 0.1173 \| 12.94 \| 220 \| 0.1030 \| 0.2612 \| 0.5224 \| 0.5224 \| nan \| nan \| 0.5224 \| 0.0 \| nan \| 0.5224 \|
	\| 0.0922 \| 14.12 \| 240 \| 0.0976 \| 0.2817 \| 0.5633 \| 0.5633 \| nan \| nan \| 0.5633 \| 0.0 \| nan \| 0.5633 \|
	\| 0.081 \| 15.29 \| 260 \| 0.0795 \| 0.3154 \| 0.6308 \| 0.6308 \| nan \| nan \| 0.6308 \| 0.0 \| nan \| 0.6308 \|
	\| 0.0852 \| 16.47 \| 280 \| 0.0716 \| 0.2188 \| 0.4377 \| 0.4377 \| nan \| nan \| 0.4377 \| 0.0 \| nan \| 0.4377 \|
	\| 0.0709 \| 17.65 \| 300 \| 0.0680 \| 0.2691 \| 0.5382 \| 0.5382 \| nan \| nan \| 0.5382 \| 0.0 \| nan \| 0.5382 \|
	\| 0.073 \| 18.82 \| 320 \| 0.0611 \| 0.2830 \| 0.5660 \| 0.5660 \| nan \| nan \| 0.5660 \| 0.0 \| nan \| 0.5660 \|
	\| 0.0602 \| 20.0 \| 340 \| 0.0592 \| 0.2829 \| 0.5657 \| 0.5657 \| nan \| nan \| 0.5657 \| 0.0 \| nan \| 0.5657 \|
	\| 0.0547 \| 21.18 \| 360 \| 0.0577 \| 0.2842 \| 0.5684 \| 0.5684 \| nan \| nan \| 0.5684 \| 0.0 \| nan \| 0.5684 \|
	\| 0.0554 \| 22.35 \| 380 \| 0.0537 \| 0.2613 \| 0.5226 \| 0.5226 \| nan \| nan \| 0.5226 \| 0.0 \| nan \| 0.5226 \|
	\| 0.0515 \| 23.53 \| 400 \| 0.0523 \| 0.3076 \| 0.6152 \| 0.6152 \| nan \| nan \| 0.6152 \| 0.0 \| nan \| 0.6152 \|
	\| 0.0444 \| 24.71 \| 420 \| 0.0487 \| 0.3063 \| 0.6126 \| 0.6126 \| nan \| nan \| 0.6126 \| 0.0 \| nan \| 0.6126 \|
	\| 0.088 \| 25.88 \| 440 \| 0.0467 \| 0.3041 \| 0.6082 \| 0.6082 \| nan \| nan \| 0.6082 \| 0.0 \| nan \| 0.6082 \|
	\| 0.0472 \| 27.06 \| 460 \| 0.0437 \| 0.2623 \| 0.5245 \| 0.5245 \| nan \| nan \| 0.5245 \| 0.0 \| nan \| 0.5245 \|
	\| 0.0396 \| 28.24 \| 480 \| 0.0474 \| 0.3352 \| 0.6704 \| 0.6704 \| nan \| nan \| 0.6704 \| 0.0 \| nan \| 0.6704 \|
	\| 0.0351 \| 29.41 \| 500 \| 0.0436 \| 0.3060 \| 0.6120 \| 0.6120 \| nan \| nan \| 0.6120 \| 0.0 \| nan \| 0.6120 \|
	\| 0.0392 \| 30.59 \| 520 \| 0.0428 \| 0.2975 \| 0.5951 \| 0.5951 \| nan \| nan \| 0.5951 \| 0.0 \| nan \| 0.5951 \|
	\| 0.0317 \| 31.76 \| 540 \| 0.0431 \| 0.3253 \| 0.6507 \| 0.6507 \| nan \| nan \| 0.6507 \| 0.0 \| nan \| 0.6507 \|
	\| 0.0391 \| 32.94 \| 560 \| 0.0404 \| 0.2863 \| 0.5726 \| 0.5726 \| nan \| nan \| 0.5726 \| 0.0 \| nan \| 0.5726 \|
	\| 0.0309 \| 34.12 \| 580 \| 0.0408 \| 0.3215 \| 0.6429 \| 0.6429 \| nan \| nan \| 0.6429 \| 0.0 \| nan \| 0.6429 \|
	\| 0.0493 \| 35.29 \| 600 \| 0.0381 \| 0.2581 \| 0.5162 \| 0.5162 \| nan \| nan \| 0.5162 \| 0.0 \| nan \| 0.5162 \|
	\| 0.0321 \| 36.47 \| 620 \| 0.0376 \| 0.3147 \| 0.6293 \| 0.6293 \| nan \| nan \| 0.6293 \| 0.0 \| nan \| 0.6293 \|
	\| 0.0333 \| 37.65 \| 640 \| 0.0372 \| 0.3118 \| 0.6236 \| 0.6236 \| nan \| nan \| 0.6236 \| 0.0 \| nan \| 0.6236 \|
	\| 0.0295 \| 38.82 \| 660 \| 0.0362 \| 0.3036 \| 0.6072 \| 0.6072 \| nan \| nan \| 0.6072 \| 0.0 \| nan \| 0.6072 \|
	\| 0.0302 \| 40.0 \| 680 \| 0.0365 \| 0.3157 \| 0.6314 \| 0.6314 \| nan \| nan \| 0.6314 \| 0.0 \| nan \| 0.6314 \|
	\| 0.0272 \| 41.18 \| 700 \| 0.0367 \| 0.3012 \| 0.6024 \| 0.6024 \| nan \| nan \| 0.6024 \| 0.0 \| nan \| 0.6024 \|
	\| 0.0278 \| 42.35 \| 720 \| 0.0353 \| 0.2935 \| 0.5870 \| 0.5870 \| nan \| nan \| 0.5870 \| 0.0 \| nan \| 0.5870 \|
	\| 0.0283 \| 43.53 \| 740 \| 0.0353 \| 0.2970 \| 0.5940 \| 0.5940 \| nan \| nan \| 0.5940 \| 0.0 \| nan \| 0.5940 \|
	\| 0.0256 \| 44.71 \| 760 \| 0.0355 \| 0.3090 \| 0.6181 \| 0.6181 \| nan \| nan \| 0.6181 \| 0.0 \| nan \| 0.6181 \|
	\| 0.0365 \| 45.88 \| 780 \| 0.0358 \| 0.3008 \| 0.6015 \| 0.6015 \| nan \| nan \| 0.6015 \| 0.0 \| nan \| 0.6015 \|
	\| 0.025 \| 47.06 \| 800 \| 0.0353 \| 0.2965 \| 0.5930 \| 0.5930 \| nan \| nan \| 0.5930 \| 0.0 \| nan \| 0.5930 \|
	\| 0.0299 \| 48.24 \| 820 \| 0.0361 \| 0.3109 \| 0.6219 \| 0.6219 \| nan \| nan \| 0.6219 \| 0.0 \| nan \| 0.6219 \|
	\| 0.0239 \| 49.41 \| 840 \| 0.0354 \| 0.3022 \| 0.6045 \| 0.6045 \| nan \| nan \| 0.6045 \| 0.0 \| nan \| 0.6045 \|


	### Framework versions

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