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	---
	license: other
	tags:
	- generated_from_trainer
	model-index:
	- name: segformer-b4-crack-segmentation-dataset
	results: []
	datasets:
	- varcoder/crack-segmentation-dataset
	language:
	- en
	library_name: transformers
	pipeline_tag: image-segmentation
	---

	<!-- 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-b4-crack-segmentation-dataset

	This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the None dataset.
	It achieves the following results on the evaluation set:
	- Loss: 0.0594
	- Mean Iou: 0.3346
	- Mean Accuracy: 0.6691
	- Overall Accuracy: 0.6691
	- Accuracy Background: nan
	- Accuracy Crack: 0.6691
	- Iou Background: 0.0
	- Iou Crack: 0.6691

	## 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: 1

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \| Mean Iou \| Mean Accuracy \| Overall Accuracy \| Accuracy Background \| Accuracy Crack \| Iou Background \| Iou Crack \|
	\|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:-------------:\|:----------------:\|:-------------------:\|:--------------:\|:--------------:\|:---------:\|
	\| 0.2287 \| 0.02 \| 100 \| 0.2515 \| 0.1734 \| 0.3468 \| 0.3468 \| nan \| 0.3468 \| 0.0 \| 0.3468 \|
	\| 0.1792 \| 0.04 \| 200 \| 0.1594 \| 0.1671 \| 0.3342 \| 0.3342 \| nan \| 0.3342 \| 0.0 \| 0.3342 \|
	\| 0.1177 \| 0.06 \| 300 \| 0.1762 \| 0.1044 \| 0.2088 \| 0.2088 \| nan \| 0.2088 \| 0.0 \| 0.2088 \|
	\| 0.0821 \| 0.08 \| 400 \| 0.1706 \| 0.2065 \| 0.4130 \| 0.4130 \| nan \| 0.4130 \| 0.0 \| 0.4130 \|
	\| 0.0666 \| 0.1 \| 500 \| 0.1507 \| 0.1931 \| 0.3863 \| 0.3863 \| nan \| 0.3863 \| 0.0 \| 0.3863 \|
	\| 0.0675 \| 0.12 \| 600 \| 0.1374 \| 0.3114 \| 0.6227 \| 0.6227 \| nan \| 0.6227 \| 0.0 \| 0.6227 \|
	\| 0.0267 \| 0.15 \| 700 \| 0.1400 \| 0.2171 \| 0.4342 \| 0.4342 \| nan \| 0.4342 \| 0.0 \| 0.4342 \|
	\| 0.0192 \| 0.17 \| 800 \| 0.1067 \| 0.1594 \| 0.3187 \| 0.3187 \| nan \| 0.3187 \| 0.0 \| 0.3187 \|
	\| 0.0711 \| 0.19 \| 900 \| 0.1002 \| 0.2915 \| 0.5830 \| 0.5830 \| nan \| 0.5830 \| 0.0 \| 0.5830 \|
	\| 0.0761 \| 0.21 \| 1000 \| 0.0785 \| 0.3099 \| 0.6199 \| 0.6199 \| nan \| 0.6199 \| 0.0 \| 0.6199 \|
	\| 0.0802 \| 0.23 \| 1100 \| 0.0829 \| 0.3086 \| 0.6173 \| 0.6173 \| nan \| 0.6173 \| 0.0 \| 0.6173 \|
	\| 0.1058 \| 0.25 \| 1200 \| 0.0895 \| 0.2139 \| 0.4278 \| 0.4278 \| nan \| 0.4278 \| 0.0 \| 0.4278 \|
	\| 0.0409 \| 0.27 \| 1300 \| 0.0792 \| 0.3237 \| 0.6475 \| 0.6475 \| nan \| 0.6475 \| 0.0 \| 0.6475 \|
	\| 0.063 \| 0.29 \| 1400 \| 0.0739 \| 0.3084 \| 0.6168 \| 0.6168 \| nan \| 0.6168 \| 0.0 \| 0.6168 \|
	\| 0.0669 \| 0.31 \| 1500 \| 0.0747 \| 0.3326 \| 0.6653 \| 0.6653 \| nan \| 0.6653 \| 0.0 \| 0.6653 \|
	\| 0.1277 \| 0.33 \| 1600 \| 0.0735 \| 0.3149 \| 0.6297 \| 0.6297 \| nan \| 0.6297 \| 0.0 \| 0.6297 \|
	\| 0.0388 \| 0.35 \| 1700 \| 0.0708 \| 0.2525 \| 0.5050 \| 0.5050 \| nan \| 0.5050 \| 0.0 \| 0.5050 \|
	\| 0.0332 \| 0.37 \| 1800 \| 0.0726 \| 0.2908 \| 0.5816 \| 0.5816 \| nan \| 0.5816 \| 0.0 \| 0.5816 \|
	\| 0.0435 \| 0.4 \| 1900 \| 0.0673 \| 0.2893 \| 0.5786 \| 0.5786 \| nan \| 0.5786 \| 0.0 \| 0.5786 \|
	\| 0.1297 \| 0.42 \| 2000 \| 0.0698 \| 0.3438 \| 0.6877 \| 0.6877 \| nan \| 0.6877 \| 0.0 \| 0.6877 \|
	\| 0.1202 \| 0.44 \| 2100 \| 0.0745 \| 0.2899 \| 0.5798 \| 0.5798 \| nan \| 0.5798 \| 0.0 \| 0.5798 \|
	\| 0.0549 \| 0.46 \| 2200 \| 0.0657 \| 0.3522 \| 0.7044 \| 0.7044 \| nan \| 0.7044 \| 0.0 \| 0.7044 \|
	\| 0.0223 \| 0.48 \| 2300 \| 0.0808 \| 0.2686 \| 0.5372 \| 0.5372 \| nan \| 0.5372 \| 0.0 \| 0.5372 \|
	\| 0.0464 \| 0.5 \| 2400 \| 0.0631 \| 0.3221 \| 0.6442 \| 0.6442 \| nan \| 0.6442 \| 0.0 \| 0.6442 \|
	\| 0.0364 \| 0.52 \| 2500 \| 0.0778 \| 0.3410 \| 0.6820 \| 0.6820 \| nan \| 0.6820 \| 0.0 \| 0.6820 \|
	\| 0.047 \| 0.54 \| 2600 \| 0.0689 \| 0.3489 \| 0.6978 \| 0.6978 \| nan \| 0.6978 \| 0.0 \| 0.6978 \|
	\| 0.0322 \| 0.56 \| 2700 \| 0.0640 \| 0.2863 \| 0.5727 \| 0.5727 \| nan \| 0.5727 \| 0.0 \| 0.5727 \|
	\| 0.0453 \| 0.58 \| 2800 \| 0.0574 \| 0.3340 \| 0.6681 \| 0.6681 \| nan \| 0.6681 \| 0.0 \| 0.6681 \|
	\| 0.0347 \| 0.6 \| 2900 \| 0.0611 \| 0.3289 \| 0.6578 \| 0.6578 \| nan \| 0.6578 \| 0.0 \| 0.6578 \|
	\| 0.0916 \| 0.62 \| 3000 \| 0.0609 \| 0.3357 \| 0.6714 \| 0.6714 \| nan \| 0.6714 \| 0.0 \| 0.6714 \|
	\| 0.0523 \| 0.65 \| 3100 \| 0.0557 \| 0.3318 \| 0.6637 \| 0.6637 \| nan \| 0.6637 \| 0.0 \| 0.6637 \|
	\| 0.1246 \| 0.67 \| 3200 \| 0.0558 \| 0.3294 \| 0.6588 \| 0.6588 \| nan \| 0.6588 \| 0.0 \| 0.6588 \|
	\| 0.0501 \| 0.69 \| 3300 \| 0.0697 \| 0.2955 \| 0.5910 \| 0.5910 \| nan \| 0.5910 \| 0.0 \| 0.5910 \|
	\| 0.0312 \| 0.71 \| 3400 \| 0.0604 \| 0.3414 \| 0.6827 \| 0.6827 \| nan \| 0.6827 \| 0.0 \| 0.6827 \|
	\| 0.0449 \| 0.73 \| 3500 \| 0.0612 \| 0.3305 \| 0.6611 \| 0.6611 \| nan \| 0.6611 \| 0.0 \| 0.6611 \|
	\| 0.0111 \| 0.75 \| 3600 \| 0.0617 \| 0.2930 \| 0.5860 \| 0.5860 \| nan \| 0.5860 \| 0.0 \| 0.5860 \|
	\| 0.0206 \| 0.77 \| 3700 \| 0.0627 \| 0.3663 \| 0.7326 \| 0.7326 \| nan \| 0.7326 \| 0.0 \| 0.7326 \|
	\| 0.051 \| 0.79 \| 3800 \| 0.0649 \| 0.3159 \| 0.6318 \| 0.6318 \| nan \| 0.6318 \| 0.0 \| 0.6318 \|
	\| 0.0243 \| 0.81 \| 3900 \| 0.0600 \| 0.3370 \| 0.6740 \| 0.6740 \| nan \| 0.6740 \| 0.0 \| 0.6740 \|
	\| 0.0108 \| 0.83 \| 4000 \| 0.0614 \| 0.3595 \| 0.7190 \| 0.7190 \| nan \| 0.7190 \| 0.0 \| 0.7190 \|
	\| 0.0951 \| 0.85 \| 4100 \| 0.0564 \| 0.3571 \| 0.7142 \| 0.7142 \| nan \| 0.7142 \| 0.0 \| 0.7142 \|
	\| 0.0731 \| 0.87 \| 4200 \| 0.0597 \| 0.3497 \| 0.6994 \| 0.6994 \| nan \| 0.6994 \| 0.0 \| 0.6994 \|
	\| 0.0307 \| 0.9 \| 4300 \| 0.0636 \| 0.3468 \| 0.6937 \| 0.6937 \| nan \| 0.6937 \| 0.0 \| 0.6937 \|
	\| 0.1039 \| 0.92 \| 4400 \| 0.0594 \| 0.3397 \| 0.6795 \| 0.6795 \| nan \| 0.6795 \| 0.0 \| 0.6795 \|
	\| 0.0083 \| 0.94 \| 4500 \| 0.0606 \| 0.3512 \| 0.7024 \| 0.7024 \| nan \| 0.7024 \| 0.0 \| 0.7024 \|
	\| 0.0113 \| 0.96 \| 4600 \| 0.0597 \| 0.3288 \| 0.6576 \| 0.6576 \| nan \| 0.6576 \| 0.0 \| 0.6576 \|
	\| 0.0417 \| 0.98 \| 4700 \| 0.0595 \| 0.3405 \| 0.6811 \| 0.6811 \| nan \| 0.6811 \| 0.0 \| 0.6811 \|
	\| 0.1944 \| 1.0 \| 4800 \| 0.0594 \| 0.3346 \| 0.6691 \| 0.6691 \| nan \| 0.6691 \| 0.0 \| 0.6691 \|


	### Framework versions

	- Transformers 4.30.2
	- Pytorch 2.0.1+cu118
	- Datasets 2.13.1
	- Tokenizers 0.13.3

	---
	license: other
	tags:
	- generated_from_trainer
	model-index:
	- name: segformer-b4-crack-segmentation-dataset
	results: []
	datasets:
	- varcoder/crack-segmentation-dataset
	language:
	- en
	library_name: transformers
	pipeline_tag: image-segmentation
	---

	<!-- 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-b4-crack-segmentation-dataset

	This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the None dataset.
	It achieves the following results on the evaluation set:
	- Loss: 0.0594
	- Mean Iou: 0.3346
	- Mean Accuracy: 0.6691
	- Overall Accuracy: 0.6691
	- Accuracy Background: nan
	- Accuracy Crack: 0.6691
	- Iou Background: 0.0
	- Iou Crack: 0.6691

	## 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: 1

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \| Mean Iou \| Mean Accuracy \| Overall Accuracy \| Accuracy Background \| Accuracy Crack \| Iou Background \| Iou Crack \|
	\|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:-------------:\|:----------------:\|:-------------------:\|:--------------:\|:--------------:\|:---------:\|
	\| 0.2287 \| 0.02 \| 100 \| 0.2515 \| 0.1734 \| 0.3468 \| 0.3468 \| nan \| 0.3468 \| 0.0 \| 0.3468 \|
	\| 0.1792 \| 0.04 \| 200 \| 0.1594 \| 0.1671 \| 0.3342 \| 0.3342 \| nan \| 0.3342 \| 0.0 \| 0.3342 \|
	\| 0.1177 \| 0.06 \| 300 \| 0.1762 \| 0.1044 \| 0.2088 \| 0.2088 \| nan \| 0.2088 \| 0.0 \| 0.2088 \|
	\| 0.0821 \| 0.08 \| 400 \| 0.1706 \| 0.2065 \| 0.4130 \| 0.4130 \| nan \| 0.4130 \| 0.0 \| 0.4130 \|
	\| 0.0666 \| 0.1 \| 500 \| 0.1507 \| 0.1931 \| 0.3863 \| 0.3863 \| nan \| 0.3863 \| 0.0 \| 0.3863 \|
	\| 0.0675 \| 0.12 \| 600 \| 0.1374 \| 0.3114 \| 0.6227 \| 0.6227 \| nan \| 0.6227 \| 0.0 \| 0.6227 \|
	\| 0.0267 \| 0.15 \| 700 \| 0.1400 \| 0.2171 \| 0.4342 \| 0.4342 \| nan \| 0.4342 \| 0.0 \| 0.4342 \|
	\| 0.0192 \| 0.17 \| 800 \| 0.1067 \| 0.1594 \| 0.3187 \| 0.3187 \| nan \| 0.3187 \| 0.0 \| 0.3187 \|
	\| 0.0711 \| 0.19 \| 900 \| 0.1002 \| 0.2915 \| 0.5830 \| 0.5830 \| nan \| 0.5830 \| 0.0 \| 0.5830 \|
	\| 0.0761 \| 0.21 \| 1000 \| 0.0785 \| 0.3099 \| 0.6199 \| 0.6199 \| nan \| 0.6199 \| 0.0 \| 0.6199 \|
	\| 0.0802 \| 0.23 \| 1100 \| 0.0829 \| 0.3086 \| 0.6173 \| 0.6173 \| nan \| 0.6173 \| 0.0 \| 0.6173 \|
	\| 0.1058 \| 0.25 \| 1200 \| 0.0895 \| 0.2139 \| 0.4278 \| 0.4278 \| nan \| 0.4278 \| 0.0 \| 0.4278 \|
	\| 0.0409 \| 0.27 \| 1300 \| 0.0792 \| 0.3237 \| 0.6475 \| 0.6475 \| nan \| 0.6475 \| 0.0 \| 0.6475 \|
	\| 0.063 \| 0.29 \| 1400 \| 0.0739 \| 0.3084 \| 0.6168 \| 0.6168 \| nan \| 0.6168 \| 0.0 \| 0.6168 \|
	\| 0.0669 \| 0.31 \| 1500 \| 0.0747 \| 0.3326 \| 0.6653 \| 0.6653 \| nan \| 0.6653 \| 0.0 \| 0.6653 \|
	\| 0.1277 \| 0.33 \| 1600 \| 0.0735 \| 0.3149 \| 0.6297 \| 0.6297 \| nan \| 0.6297 \| 0.0 \| 0.6297 \|
	\| 0.0388 \| 0.35 \| 1700 \| 0.0708 \| 0.2525 \| 0.5050 \| 0.5050 \| nan \| 0.5050 \| 0.0 \| 0.5050 \|
	\| 0.0332 \| 0.37 \| 1800 \| 0.0726 \| 0.2908 \| 0.5816 \| 0.5816 \| nan \| 0.5816 \| 0.0 \| 0.5816 \|
	\| 0.0435 \| 0.4 \| 1900 \| 0.0673 \| 0.2893 \| 0.5786 \| 0.5786 \| nan \| 0.5786 \| 0.0 \| 0.5786 \|
	\| 0.1297 \| 0.42 \| 2000 \| 0.0698 \| 0.3438 \| 0.6877 \| 0.6877 \| nan \| 0.6877 \| 0.0 \| 0.6877 \|
	\| 0.1202 \| 0.44 \| 2100 \| 0.0745 \| 0.2899 \| 0.5798 \| 0.5798 \| nan \| 0.5798 \| 0.0 \| 0.5798 \|
	\| 0.0549 \| 0.46 \| 2200 \| 0.0657 \| 0.3522 \| 0.7044 \| 0.7044 \| nan \| 0.7044 \| 0.0 \| 0.7044 \|
	\| 0.0223 \| 0.48 \| 2300 \| 0.0808 \| 0.2686 \| 0.5372 \| 0.5372 \| nan \| 0.5372 \| 0.0 \| 0.5372 \|
	\| 0.0464 \| 0.5 \| 2400 \| 0.0631 \| 0.3221 \| 0.6442 \| 0.6442 \| nan \| 0.6442 \| 0.0 \| 0.6442 \|
	\| 0.0364 \| 0.52 \| 2500 \| 0.0778 \| 0.3410 \| 0.6820 \| 0.6820 \| nan \| 0.6820 \| 0.0 \| 0.6820 \|
	\| 0.047 \| 0.54 \| 2600 \| 0.0689 \| 0.3489 \| 0.6978 \| 0.6978 \| nan \| 0.6978 \| 0.0 \| 0.6978 \|
	\| 0.0322 \| 0.56 \| 2700 \| 0.0640 \| 0.2863 \| 0.5727 \| 0.5727 \| nan \| 0.5727 \| 0.0 \| 0.5727 \|
	\| 0.0453 \| 0.58 \| 2800 \| 0.0574 \| 0.3340 \| 0.6681 \| 0.6681 \| nan \| 0.6681 \| 0.0 \| 0.6681 \|
	\| 0.0347 \| 0.6 \| 2900 \| 0.0611 \| 0.3289 \| 0.6578 \| 0.6578 \| nan \| 0.6578 \| 0.0 \| 0.6578 \|
	\| 0.0916 \| 0.62 \| 3000 \| 0.0609 \| 0.3357 \| 0.6714 \| 0.6714 \| nan \| 0.6714 \| 0.0 \| 0.6714 \|
	\| 0.0523 \| 0.65 \| 3100 \| 0.0557 \| 0.3318 \| 0.6637 \| 0.6637 \| nan \| 0.6637 \| 0.0 \| 0.6637 \|
	\| 0.1246 \| 0.67 \| 3200 \| 0.0558 \| 0.3294 \| 0.6588 \| 0.6588 \| nan \| 0.6588 \| 0.0 \| 0.6588 \|
	\| 0.0501 \| 0.69 \| 3300 \| 0.0697 \| 0.2955 \| 0.5910 \| 0.5910 \| nan \| 0.5910 \| 0.0 \| 0.5910 \|
	\| 0.0312 \| 0.71 \| 3400 \| 0.0604 \| 0.3414 \| 0.6827 \| 0.6827 \| nan \| 0.6827 \| 0.0 \| 0.6827 \|
	\| 0.0449 \| 0.73 \| 3500 \| 0.0612 \| 0.3305 \| 0.6611 \| 0.6611 \| nan \| 0.6611 \| 0.0 \| 0.6611 \|
	\| 0.0111 \| 0.75 \| 3600 \| 0.0617 \| 0.2930 \| 0.5860 \| 0.5860 \| nan \| 0.5860 \| 0.0 \| 0.5860 \|
	\| 0.0206 \| 0.77 \| 3700 \| 0.0627 \| 0.3663 \| 0.7326 \| 0.7326 \| nan \| 0.7326 \| 0.0 \| 0.7326 \|
	\| 0.051 \| 0.79 \| 3800 \| 0.0649 \| 0.3159 \| 0.6318 \| 0.6318 \| nan \| 0.6318 \| 0.0 \| 0.6318 \|
	\| 0.0243 \| 0.81 \| 3900 \| 0.0600 \| 0.3370 \| 0.6740 \| 0.6740 \| nan \| 0.6740 \| 0.0 \| 0.6740 \|
	\| 0.0108 \| 0.83 \| 4000 \| 0.0614 \| 0.3595 \| 0.7190 \| 0.7190 \| nan \| 0.7190 \| 0.0 \| 0.7190 \|
	\| 0.0951 \| 0.85 \| 4100 \| 0.0564 \| 0.3571 \| 0.7142 \| 0.7142 \| nan \| 0.7142 \| 0.0 \| 0.7142 \|
	\| 0.0731 \| 0.87 \| 4200 \| 0.0597 \| 0.3497 \| 0.6994 \| 0.6994 \| nan \| 0.6994 \| 0.0 \| 0.6994 \|
	\| 0.0307 \| 0.9 \| 4300 \| 0.0636 \| 0.3468 \| 0.6937 \| 0.6937 \| nan \| 0.6937 \| 0.0 \| 0.6937 \|
	\| 0.1039 \| 0.92 \| 4400 \| 0.0594 \| 0.3397 \| 0.6795 \| 0.6795 \| nan \| 0.6795 \| 0.0 \| 0.6795 \|
	\| 0.0083 \| 0.94 \| 4500 \| 0.0606 \| 0.3512 \| 0.7024 \| 0.7024 \| nan \| 0.7024 \| 0.0 \| 0.7024 \|
	\| 0.0113 \| 0.96 \| 4600 \| 0.0597 \| 0.3288 \| 0.6576 \| 0.6576 \| nan \| 0.6576 \| 0.0 \| 0.6576 \|
	\| 0.0417 \| 0.98 \| 4700 \| 0.0595 \| 0.3405 \| 0.6811 \| 0.6811 \| nan \| 0.6811 \| 0.0 \| 0.6811 \|
	\| 0.1944 \| 1.0 \| 4800 \| 0.0594 \| 0.3346 \| 0.6691 \| 0.6691 \| nan \| 0.6691 \| 0.0 \| 0.6691 \|


	### Framework versions

	- Transformers 4.30.2
	- Pytorch 2.0.1+cu118
	- Datasets 2.13.1
	- Tokenizers 0.13.3