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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: 0.1009
	- Mean Iou: 0.2182
	- Mean Accuracy: 0.4365
	- Overall Accuracy: 0.4365
	- Accuracy Unlabeled: nan
	- Accuracy Tool: nan
	- Accuracy Wear: 0.4365
	- Iou Unlabeled: 0.0
	- Iou Tool: nan
	- Iou Wear: 0.4365

	## 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 \|
	\|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:-------------:\|:----------------:\|:------------------:\|:-------------:\|:-------------:\|:-------------:\|:--------:\|:--------:\|
	\| 0.8747 \| 1.18 \| 20 \| 0.9764 \| 0.1788 \| 0.5363 \| 0.5363 \| nan \| nan \| 0.5363 \| 0.0 \| 0.0 \| 0.5363 \|
	\| 0.6206 \| 2.35 \| 40 \| 0.6394 \| 0.1860 \| 0.3719 \| 0.3719 \| nan \| nan \| 0.3719 \| 0.0 \| nan \| 0.3719 \|
	\| 0.4963 \| 3.53 \| 60 \| 0.4309 \| 0.2230 \| 0.4460 \| 0.4460 \| nan \| nan \| 0.4460 \| 0.0 \| nan \| 0.4460 \|
	\| 0.3978 \| 4.71 \| 80 \| 0.3839 \| 0.3231 \| 0.6463 \| 0.6463 \| nan \| nan \| 0.6463 \| 0.0 \| nan \| 0.6463 \|
	\| 0.3171 \| 5.88 \| 100 \| 0.3193 \| 0.2653 \| 0.5306 \| 0.5306 \| nan \| nan \| 0.5306 \| 0.0 \| nan \| 0.5306 \|
	\| 0.3046 \| 7.06 \| 120 \| 0.2760 \| 0.1372 \| 0.2745 \| 0.2745 \| nan \| nan \| 0.2745 \| 0.0 \| nan \| 0.2745 \|
	\| 0.2558 \| 8.24 \| 140 \| 0.2181 \| 0.2549 \| 0.5097 \| 0.5097 \| nan \| nan \| 0.5097 \| 0.0 \| nan \| 0.5097 \|
	\| 0.225 \| 9.41 \| 160 \| 0.1933 \| 0.2673 \| 0.5345 \| 0.5345 \| nan \| nan \| 0.5345 \| 0.0 \| nan \| 0.5345 \|
	\| 0.1532 \| 10.59 \| 180 \| 0.1735 \| 0.2673 \| 0.5346 \| 0.5346 \| nan \| nan \| 0.5346 \| 0.0 \| nan \| 0.5346 \|
	\| 0.1505 \| 11.76 \| 200 \| 0.1660 \| 0.1857 \| 0.3715 \| 0.3715 \| nan \| nan \| 0.3715 \| 0.0 \| nan \| 0.3715 \|
	\| 0.1222 \| 12.94 \| 220 \| 0.1641 \| 0.1508 \| 0.3016 \| 0.3016 \| nan \| nan \| 0.3016 \| 0.0 \| nan \| 0.3016 \|
	\| 0.0921 \| 14.12 \| 240 \| 0.1363 \| 0.2869 \| 0.5738 \| 0.5738 \| nan \| nan \| 0.5738 \| 0.0 \| nan \| 0.5738 \|
	\| 0.0792 \| 15.29 \| 260 \| 0.1300 \| 0.2245 \| 0.4491 \| 0.4491 \| nan \| nan \| 0.4491 \| 0.0 \| nan \| 0.4491 \|
	\| 0.0804 \| 16.47 \| 280 \| 0.1338 \| 0.1910 \| 0.3820 \| 0.3820 \| nan \| nan \| 0.3820 \| 0.0 \| nan \| 0.3820 \|
	\| 0.0732 \| 17.65 \| 300 \| 0.1118 \| 0.2583 \| 0.5166 \| 0.5166 \| nan \| nan \| 0.5166 \| 0.0 \| nan \| 0.5166 \|
	\| 0.062 \| 18.82 \| 320 \| 0.1102 \| 0.2432 \| 0.4864 \| 0.4864 \| nan \| nan \| 0.4864 \| 0.0 \| nan \| 0.4864 \|
	\| 0.0582 \| 20.0 \| 340 \| 0.1023 \| 0.2547 \| 0.5095 \| 0.5095 \| nan \| nan \| 0.5095 \| 0.0 \| nan \| 0.5095 \|
	\| 0.056 \| 21.18 \| 360 \| 0.1151 \| 0.2111 \| 0.4222 \| 0.4222 \| nan \| nan \| 0.4222 \| 0.0 \| nan \| 0.4222 \|
	\| 0.0493 \| 22.35 \| 380 \| 0.1126 \| 0.2045 \| 0.4089 \| 0.4089 \| nan \| nan \| 0.4089 \| 0.0 \| nan \| 0.4089 \|
	\| 0.0633 \| 23.53 \| 400 \| 0.1065 \| 0.2220 \| 0.4440 \| 0.4440 \| nan \| nan \| 0.4440 \| 0.0 \| nan \| 0.4440 \|
	\| 0.0438 \| 24.71 \| 420 \| 0.0987 \| 0.2558 \| 0.5116 \| 0.5116 \| nan \| nan \| 0.5116 \| 0.0 \| nan \| 0.5116 \|
	\| 0.0451 \| 25.88 \| 440 \| 0.1060 \| 0.2326 \| 0.4652 \| 0.4652 \| nan \| nan \| 0.4652 \| 0.0 \| nan \| 0.4652 \|
	\| 0.0426 \| 27.06 \| 460 \| 0.0981 \| 0.2493 \| 0.4986 \| 0.4986 \| nan \| nan \| 0.4986 \| 0.0 \| nan \| 0.4986 \|
	\| 0.0397 \| 28.24 \| 480 \| 0.0955 \| 0.2485 \| 0.4970 \| 0.4970 \| nan \| nan \| 0.4970 \| 0.0 \| nan \| 0.4970 \|
	\| 0.0349 \| 29.41 \| 500 \| 0.0991 \| 0.2321 \| 0.4641 \| 0.4641 \| nan \| nan \| 0.4641 \| 0.0 \| nan \| 0.4641 \|
	\| 0.0337 \| 30.59 \| 520 \| 0.1048 \| 0.2111 \| 0.4222 \| 0.4222 \| nan \| nan \| 0.4222 \| 0.0 \| nan \| 0.4222 \|
	\| 0.0358 \| 31.76 \| 540 \| 0.0870 \| 0.2856 \| 0.5712 \| 0.5712 \| nan \| nan \| 0.5712 \| 0.0 \| nan \| 0.5712 \|
	\| 0.0322 \| 32.94 \| 560 \| 0.1061 \| 0.2085 \| 0.4170 \| 0.4170 \| nan \| nan \| 0.4170 \| 0.0 \| nan \| 0.4170 \|
	\| 0.028 \| 34.12 \| 580 \| 0.0950 \| 0.2399 \| 0.4798 \| 0.4798 \| nan \| nan \| 0.4798 \| 0.0 \| nan \| 0.4798 \|
	\| 0.0282 \| 35.29 \| 600 \| 0.0880 \| 0.2667 \| 0.5335 \| 0.5335 \| nan \| nan \| 0.5335 \| 0.0 \| nan \| 0.5335 \|
	\| 0.0266 \| 36.47 \| 620 \| 0.0952 \| 0.2457 \| 0.4914 \| 0.4914 \| nan \| nan \| 0.4914 \| 0.0 \| nan \| 0.4914 \|
	\| 0.0276 \| 37.65 \| 640 \| 0.0994 \| 0.2329 \| 0.4658 \| 0.4658 \| nan \| nan \| 0.4658 \| 0.0 \| nan \| 0.4658 \|
	\| 0.0306 \| 38.82 \| 660 \| 0.0978 \| 0.2314 \| 0.4627 \| 0.4627 \| nan \| nan \| 0.4627 \| 0.0 \| nan \| 0.4627 \|
	\| 0.0337 \| 40.0 \| 680 \| 0.0949 \| 0.2404 \| 0.4809 \| 0.4809 \| nan \| nan \| 0.4809 \| 0.0 \| nan \| 0.4809 \|
	\| 0.0243 \| 41.18 \| 700 \| 0.0948 \| 0.2382 \| 0.4765 \| 0.4765 \| nan \| nan \| 0.4765 \| 0.0 \| nan \| 0.4765 \|
	\| 0.0278 \| 42.35 \| 720 \| 0.0978 \| 0.2328 \| 0.4655 \| 0.4655 \| nan \| nan \| 0.4655 \| 0.0 \| nan \| 0.4655 \|
	\| 0.0317 \| 43.53 \| 740 \| 0.0975 \| 0.2337 \| 0.4675 \| 0.4675 \| nan \| nan \| 0.4675 \| 0.0 \| nan \| 0.4675 \|
	\| 0.0321 \| 44.71 \| 760 \| 0.0981 \| 0.2331 \| 0.4663 \| 0.4663 \| nan \| nan \| 0.4663 \| 0.0 \| nan \| 0.4663 \|
	\| 0.0318 \| 45.88 \| 780 \| 0.0955 \| 0.2374 \| 0.4748 \| 0.4748 \| nan \| nan \| 0.4748 \| 0.0 \| nan \| 0.4748 \|
	\| 0.0268 \| 47.06 \| 800 \| 0.0963 \| 0.2358 \| 0.4715 \| 0.4715 \| nan \| nan \| 0.4715 \| 0.0 \| nan \| 0.4715 \|
	\| 0.0268 \| 48.24 \| 820 \| 0.1001 \| 0.2229 \| 0.4459 \| 0.4459 \| nan \| nan \| 0.4459 \| 0.0 \| nan \| 0.4459 \|
	\| 0.0314 \| 49.41 \| 840 \| 0.1009 \| 0.2182 \| 0.4365 \| 0.4365 \| nan \| nan \| 0.4365 \| 0.0 \| nan \| 0.4365 \|


	### Framework versions

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