update model card README.md

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	---
	license: other
	base_model: nvidia/mit-b5
	tags:
	- generated_from_trainer
	model-index:
	- name: segcrack9k_conglomerate_segformer
	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. -->

	# segcrack9k_conglomerate_segformer

	This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the None dataset.
	It achieves the following results on the evaluation set:
	- Loss: 0.0857
	- Mean Iou: 0.0010
	- Mean Accuracy: 0.0021
	- Overall Accuracy: 0.0021
	- Accuracy Background: nan
	- Accuracy Crack: 0.0021
	- Iou Background: 0.0
	- Iou Crack: 0.0021

	## 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: 8e-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 \|
	\|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:-------------:\|:----------------:\|:-------------------:\|:--------------:\|:--------------:\|:---------:\|
	\| 0.0716 \| 0.02 \| 100 \| 0.1132 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \|
	\| 0.0708 \| 0.04 \| 200 \| 0.1006 \| 0.0001 \| 0.0003 \| 0.0003 \| nan \| 0.0003 \| 0.0 \| 0.0003 \|
	\| 0.1661 \| 0.06 \| 300 \| 0.1042 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \|
	\| 0.0601 \| 0.08 \| 400 \| 0.1005 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \|
	\| 0.1034 \| 0.1 \| 500 \| 0.0980 \| 0.0237 \| 0.0474 \| 0.0474 \| nan \| 0.0474 \| 0.0 \| 0.0474 \|
	\| 0.0581 \| 0.12 \| 600 \| 0.0965 \| 0.0003 \| 0.0005 \| 0.0005 \| nan \| 0.0005 \| 0.0 \| 0.0005 \|
	\| 0.0561 \| 0.14 \| 700 \| 0.1023 \| 0.0038 \| 0.0075 \| 0.0075 \| nan \| 0.0075 \| 0.0 \| 0.0075 \|
	\| 0.1034 \| 0.16 \| 800 \| 0.0956 \| 0.0002 \| 0.0003 \| 0.0003 \| nan \| 0.0003 \| 0.0 \| 0.0003 \|
	\| 0.1341 \| 0.18 \| 900 \| 0.0985 \| 0.0185 \| 0.0369 \| 0.0369 \| nan \| 0.0369 \| 0.0 \| 0.0369 \|
	\| 0.1988 \| 0.2 \| 1000 \| 0.0946 \| 0.0059 \| 0.0118 \| 0.0118 \| nan \| 0.0118 \| 0.0 \| 0.0118 \|
	\| 0.0378 \| 0.22 \| 1100 \| 0.0945 \| 0.1402 \| 0.2804 \| 0.2804 \| nan \| 0.2804 \| 0.0 \| 0.2804 \|
	\| 0.0582 \| 0.24 \| 1200 \| 0.0907 \| 0.0488 \| 0.0976 \| 0.0976 \| nan \| 0.0976 \| 0.0 \| 0.0976 \|
	\| 0.1464 \| 0.26 \| 1300 \| 0.0971 \| 0.1701 \| 0.3401 \| 0.3401 \| nan \| 0.3401 \| 0.0 \| 0.3401 \|
	\| 0.0601 \| 0.28 \| 1400 \| 0.0893 \| 0.0222 \| 0.0444 \| 0.0444 \| nan \| 0.0444 \| 0.0 \| 0.0444 \|
	\| 0.0855 \| 0.3 \| 1500 \| 0.0910 \| 0.0307 \| 0.0613 \| 0.0613 \| nan \| 0.0613 \| 0.0 \| 0.0613 \|
	\| 0.1167 \| 0.32 \| 1600 \| 0.0895 \| 0.0143 \| 0.0286 \| 0.0286 \| nan \| 0.0286 \| 0.0 \| 0.0286 \|
	\| 0.0641 \| 0.34 \| 1700 \| 0.0918 \| 0.0073 \| 0.0145 \| 0.0145 \| nan \| 0.0145 \| 0.0 \| 0.0145 \|
	\| 0.0621 \| 0.36 \| 1800 \| 0.0927 \| 0.0181 \| 0.0363 \| 0.0363 \| nan \| 0.0363 \| 0.0 \| 0.0363 \|
	\| 0.0364 \| 0.38 \| 1900 \| 0.0884 \| 0.1397 \| 0.2794 \| 0.2794 \| nan \| 0.2794 \| 0.0 \| 0.2794 \|
	\| 0.1394 \| 0.4 \| 2000 \| 0.0903 \| 0.0000 \| 0.0000 \| 0.0000 \| nan \| 0.0000 \| 0.0 \| 0.0000 \|
	\| 0.0187 \| 0.42 \| 2100 \| 0.0914 \| 0.0124 \| 0.0248 \| 0.0248 \| nan \| 0.0248 \| 0.0 \| 0.0248 \|
	\| 0.1842 \| 0.44 \| 2200 \| 0.0908 \| 0.0045 \| 0.0090 \| 0.0090 \| nan \| 0.0090 \| 0.0 \| 0.0090 \|
	\| 0.0847 \| 0.46 \| 2300 \| 0.0896 \| 0.0031 \| 0.0062 \| 0.0062 \| nan \| 0.0062 \| 0.0 \| 0.0062 \|
	\| 0.0556 \| 0.48 \| 2400 \| 0.0871 \| 0.0016 \| 0.0033 \| 0.0033 \| nan \| 0.0033 \| 0.0 \| 0.0033 \|
	\| 0.0454 \| 0.51 \| 2500 \| 0.0896 \| 0.0005 \| 0.0010 \| 0.0010 \| nan \| 0.0010 \| 0.0 \| 0.0010 \|
	\| 0.1411 \| 0.53 \| 2600 \| 0.0876 \| 0.0095 \| 0.0190 \| 0.0190 \| nan \| 0.0190 \| 0.0 \| 0.0190 \|
	\| 0.1044 \| 0.55 \| 2700 \| 0.0936 \| 0.0001 \| 0.0002 \| 0.0002 \| nan \| 0.0002 \| 0.0 \| 0.0002 \|
	\| 0.1299 \| 0.57 \| 2800 \| 0.0938 \| 0.0008 \| 0.0017 \| 0.0017 \| nan \| 0.0017 \| 0.0 \| 0.0017 \|
	\| 0.0909 \| 0.59 \| 2900 \| 0.0877 \| 0.0012 \| 0.0024 \| 0.0024 \| nan \| 0.0024 \| 0.0 \| 0.0024 \|
	\| 0.0981 \| 0.61 \| 3000 \| 0.0914 \| 0.0012 \| 0.0024 \| 0.0024 \| nan \| 0.0024 \| 0.0 \| 0.0024 \|
	\| 0.0905 \| 0.63 \| 3100 \| 0.0880 \| 0.0077 \| 0.0153 \| 0.0153 \| nan \| 0.0153 \| 0.0 \| 0.0153 \|
	\| 0.2111 \| 0.65 \| 3200 \| 0.0877 \| 0.0000 \| 0.0001 \| 0.0001 \| nan \| 0.0001 \| 0.0 \| 0.0001 \|
	\| 0.3218 \| 0.67 \| 3300 \| 0.0860 \| 0.0036 \| 0.0072 \| 0.0072 \| nan \| 0.0072 \| 0.0 \| 0.0072 \|
	\| 0.1134 \| 0.69 \| 3400 \| 0.0864 \| 0.0075 \| 0.0151 \| 0.0151 \| nan \| 0.0151 \| 0.0 \| 0.0151 \|
	\| 0.2184 \| 0.71 \| 3500 \| 0.0907 \| 0.0000 \| 0.0000 \| 0.0000 \| nan \| 0.0000 \| 0.0 \| 0.0000 \|
	\| 0.1779 \| 0.73 \| 3600 \| 0.0877 \| 0.0029 \| 0.0059 \| 0.0059 \| nan \| 0.0059 \| 0.0 \| 0.0059 \|
	\| 0.3664 \| 0.75 \| 3700 \| 0.0878 \| 0.0001 \| 0.0001 \| 0.0001 \| nan \| 0.0001 \| 0.0 \| 0.0001 \|
	\| 0.0365 \| 0.77 \| 3800 \| 0.0870 \| 0.0000 \| 0.0000 \| 0.0000 \| nan \| 0.0000 \| 0.0 \| 0.0000 \|
	\| 0.0591 \| 0.79 \| 3900 \| 0.0877 \| 0.0000 \| 0.0001 \| 0.0001 \| nan \| 0.0001 \| 0.0 \| 0.0001 \|
	\| 0.0719 \| 0.81 \| 4000 \| 0.0871 \| 0.0004 \| 0.0008 \| 0.0008 \| nan \| 0.0008 \| 0.0 \| 0.0008 \|
	\| 0.0402 \| 0.83 \| 4100 \| 0.0874 \| 0.0011 \| 0.0022 \| 0.0022 \| nan \| 0.0022 \| 0.0 \| 0.0022 \|
	\| 0.0814 \| 0.85 \| 4200 \| 0.0887 \| 0.0008 \| 0.0017 \| 0.0017 \| nan \| 0.0017 \| 0.0 \| 0.0017 \|
	\| 0.0485 \| 0.87 \| 4300 \| 0.0871 \| 0.0025 \| 0.0050 \| 0.0050 \| nan \| 0.0050 \| 0.0 \| 0.0050 \|
	\| 0.0487 \| 0.89 \| 4400 \| 0.0864 \| 0.0004 \| 0.0007 \| 0.0007 \| nan \| 0.0007 \| 0.0 \| 0.0007 \|
	\| 0.0689 \| 0.91 \| 4500 \| 0.0859 \| 0.0002 \| 0.0004 \| 0.0004 \| nan \| 0.0004 \| 0.0 \| 0.0004 \|
	\| 0.0782 \| 0.93 \| 4600 \| 0.0858 \| 0.0018 \| 0.0036 \| 0.0036 \| nan \| 0.0036 \| 0.0 \| 0.0036 \|
	\| 0.2153 \| 0.95 \| 4700 \| 0.0855 \| 0.0004 \| 0.0008 \| 0.0008 \| nan \| 0.0008 \| 0.0 \| 0.0008 \|
	\| 0.1974 \| 0.97 \| 4800 \| 0.0860 \| 0.0004 \| 0.0009 \| 0.0009 \| nan \| 0.0009 \| 0.0 \| 0.0009 \|
	\| 0.0184 \| 0.99 \| 4900 \| 0.0857 \| 0.0010 \| 0.0021 \| 0.0021 \| nan \| 0.0021 \| 0.0 \| 0.0021 \|


	### Framework versions

	- Transformers 4.31.0
	- Pytorch 2.0.1+cu118
	- Datasets 2.13.1
	- Tokenizers 0.13.3

	---
	license: other
	base_model: nvidia/mit-b5
	tags:
	- generated_from_trainer
	model-index:
	- name: segcrack9k_conglomerate_segformer
	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. -->

	# segcrack9k_conglomerate_segformer

	This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the None dataset.
	It achieves the following results on the evaluation set:
	- Loss: 0.0857
	- Mean Iou: 0.0010
	- Mean Accuracy: 0.0021
	- Overall Accuracy: 0.0021
	- Accuracy Background: nan
	- Accuracy Crack: 0.0021
	- Iou Background: 0.0
	- Iou Crack: 0.0021

	## 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: 8e-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 \|
	\|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:-------------:\|:----------------:\|:-------------------:\|:--------------:\|:--------------:\|:---------:\|
	\| 0.0716 \| 0.02 \| 100 \| 0.1132 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \|
	\| 0.0708 \| 0.04 \| 200 \| 0.1006 \| 0.0001 \| 0.0003 \| 0.0003 \| nan \| 0.0003 \| 0.0 \| 0.0003 \|
	\| 0.1661 \| 0.06 \| 300 \| 0.1042 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \|
	\| 0.0601 \| 0.08 \| 400 \| 0.1005 \| 0.0 \| 0.0 \| 0.0 \| nan \| 0.0 \| 0.0 \| 0.0 \|
	\| 0.1034 \| 0.1 \| 500 \| 0.0980 \| 0.0237 \| 0.0474 \| 0.0474 \| nan \| 0.0474 \| 0.0 \| 0.0474 \|
	\| 0.0581 \| 0.12 \| 600 \| 0.0965 \| 0.0003 \| 0.0005 \| 0.0005 \| nan \| 0.0005 \| 0.0 \| 0.0005 \|
	\| 0.0561 \| 0.14 \| 700 \| 0.1023 \| 0.0038 \| 0.0075 \| 0.0075 \| nan \| 0.0075 \| 0.0 \| 0.0075 \|
	\| 0.1034 \| 0.16 \| 800 \| 0.0956 \| 0.0002 \| 0.0003 \| 0.0003 \| nan \| 0.0003 \| 0.0 \| 0.0003 \|
	\| 0.1341 \| 0.18 \| 900 \| 0.0985 \| 0.0185 \| 0.0369 \| 0.0369 \| nan \| 0.0369 \| 0.0 \| 0.0369 \|
	\| 0.1988 \| 0.2 \| 1000 \| 0.0946 \| 0.0059 \| 0.0118 \| 0.0118 \| nan \| 0.0118 \| 0.0 \| 0.0118 \|
	\| 0.0378 \| 0.22 \| 1100 \| 0.0945 \| 0.1402 \| 0.2804 \| 0.2804 \| nan \| 0.2804 \| 0.0 \| 0.2804 \|
	\| 0.0582 \| 0.24 \| 1200 \| 0.0907 \| 0.0488 \| 0.0976 \| 0.0976 \| nan \| 0.0976 \| 0.0 \| 0.0976 \|
	\| 0.1464 \| 0.26 \| 1300 \| 0.0971 \| 0.1701 \| 0.3401 \| 0.3401 \| nan \| 0.3401 \| 0.0 \| 0.3401 \|
	\| 0.0601 \| 0.28 \| 1400 \| 0.0893 \| 0.0222 \| 0.0444 \| 0.0444 \| nan \| 0.0444 \| 0.0 \| 0.0444 \|
	\| 0.0855 \| 0.3 \| 1500 \| 0.0910 \| 0.0307 \| 0.0613 \| 0.0613 \| nan \| 0.0613 \| 0.0 \| 0.0613 \|
	\| 0.1167 \| 0.32 \| 1600 \| 0.0895 \| 0.0143 \| 0.0286 \| 0.0286 \| nan \| 0.0286 \| 0.0 \| 0.0286 \|
	\| 0.0641 \| 0.34 \| 1700 \| 0.0918 \| 0.0073 \| 0.0145 \| 0.0145 \| nan \| 0.0145 \| 0.0 \| 0.0145 \|
	\| 0.0621 \| 0.36 \| 1800 \| 0.0927 \| 0.0181 \| 0.0363 \| 0.0363 \| nan \| 0.0363 \| 0.0 \| 0.0363 \|
	\| 0.0364 \| 0.38 \| 1900 \| 0.0884 \| 0.1397 \| 0.2794 \| 0.2794 \| nan \| 0.2794 \| 0.0 \| 0.2794 \|
	\| 0.1394 \| 0.4 \| 2000 \| 0.0903 \| 0.0000 \| 0.0000 \| 0.0000 \| nan \| 0.0000 \| 0.0 \| 0.0000 \|
	\| 0.0187 \| 0.42 \| 2100 \| 0.0914 \| 0.0124 \| 0.0248 \| 0.0248 \| nan \| 0.0248 \| 0.0 \| 0.0248 \|
	\| 0.1842 \| 0.44 \| 2200 \| 0.0908 \| 0.0045 \| 0.0090 \| 0.0090 \| nan \| 0.0090 \| 0.0 \| 0.0090 \|
	\| 0.0847 \| 0.46 \| 2300 \| 0.0896 \| 0.0031 \| 0.0062 \| 0.0062 \| nan \| 0.0062 \| 0.0 \| 0.0062 \|
	\| 0.0556 \| 0.48 \| 2400 \| 0.0871 \| 0.0016 \| 0.0033 \| 0.0033 \| nan \| 0.0033 \| 0.0 \| 0.0033 \|
	\| 0.0454 \| 0.51 \| 2500 \| 0.0896 \| 0.0005 \| 0.0010 \| 0.0010 \| nan \| 0.0010 \| 0.0 \| 0.0010 \|
	\| 0.1411 \| 0.53 \| 2600 \| 0.0876 \| 0.0095 \| 0.0190 \| 0.0190 \| nan \| 0.0190 \| 0.0 \| 0.0190 \|
	\| 0.1044 \| 0.55 \| 2700 \| 0.0936 \| 0.0001 \| 0.0002 \| 0.0002 \| nan \| 0.0002 \| 0.0 \| 0.0002 \|
	\| 0.1299 \| 0.57 \| 2800 \| 0.0938 \| 0.0008 \| 0.0017 \| 0.0017 \| nan \| 0.0017 \| 0.0 \| 0.0017 \|
	\| 0.0909 \| 0.59 \| 2900 \| 0.0877 \| 0.0012 \| 0.0024 \| 0.0024 \| nan \| 0.0024 \| 0.0 \| 0.0024 \|
	\| 0.0981 \| 0.61 \| 3000 \| 0.0914 \| 0.0012 \| 0.0024 \| 0.0024 \| nan \| 0.0024 \| 0.0 \| 0.0024 \|
	\| 0.0905 \| 0.63 \| 3100 \| 0.0880 \| 0.0077 \| 0.0153 \| 0.0153 \| nan \| 0.0153 \| 0.0 \| 0.0153 \|
	\| 0.2111 \| 0.65 \| 3200 \| 0.0877 \| 0.0000 \| 0.0001 \| 0.0001 \| nan \| 0.0001 \| 0.0 \| 0.0001 \|
	\| 0.3218 \| 0.67 \| 3300 \| 0.0860 \| 0.0036 \| 0.0072 \| 0.0072 \| nan \| 0.0072 \| 0.0 \| 0.0072 \|
	\| 0.1134 \| 0.69 \| 3400 \| 0.0864 \| 0.0075 \| 0.0151 \| 0.0151 \| nan \| 0.0151 \| 0.0 \| 0.0151 \|
	\| 0.2184 \| 0.71 \| 3500 \| 0.0907 \| 0.0000 \| 0.0000 \| 0.0000 \| nan \| 0.0000 \| 0.0 \| 0.0000 \|
	\| 0.1779 \| 0.73 \| 3600 \| 0.0877 \| 0.0029 \| 0.0059 \| 0.0059 \| nan \| 0.0059 \| 0.0 \| 0.0059 \|
	\| 0.3664 \| 0.75 \| 3700 \| 0.0878 \| 0.0001 \| 0.0001 \| 0.0001 \| nan \| 0.0001 \| 0.0 \| 0.0001 \|
	\| 0.0365 \| 0.77 \| 3800 \| 0.0870 \| 0.0000 \| 0.0000 \| 0.0000 \| nan \| 0.0000 \| 0.0 \| 0.0000 \|
	\| 0.0591 \| 0.79 \| 3900 \| 0.0877 \| 0.0000 \| 0.0001 \| 0.0001 \| nan \| 0.0001 \| 0.0 \| 0.0001 \|
	\| 0.0719 \| 0.81 \| 4000 \| 0.0871 \| 0.0004 \| 0.0008 \| 0.0008 \| nan \| 0.0008 \| 0.0 \| 0.0008 \|
	\| 0.0402 \| 0.83 \| 4100 \| 0.0874 \| 0.0011 \| 0.0022 \| 0.0022 \| nan \| 0.0022 \| 0.0 \| 0.0022 \|
	\| 0.0814 \| 0.85 \| 4200 \| 0.0887 \| 0.0008 \| 0.0017 \| 0.0017 \| nan \| 0.0017 \| 0.0 \| 0.0017 \|
	\| 0.0485 \| 0.87 \| 4300 \| 0.0871 \| 0.0025 \| 0.0050 \| 0.0050 \| nan \| 0.0050 \| 0.0 \| 0.0050 \|
	\| 0.0487 \| 0.89 \| 4400 \| 0.0864 \| 0.0004 \| 0.0007 \| 0.0007 \| nan \| 0.0007 \| 0.0 \| 0.0007 \|
	\| 0.0689 \| 0.91 \| 4500 \| 0.0859 \| 0.0002 \| 0.0004 \| 0.0004 \| nan \| 0.0004 \| 0.0 \| 0.0004 \|
	\| 0.0782 \| 0.93 \| 4600 \| 0.0858 \| 0.0018 \| 0.0036 \| 0.0036 \| nan \| 0.0036 \| 0.0 \| 0.0036 \|
	\| 0.2153 \| 0.95 \| 4700 \| 0.0855 \| 0.0004 \| 0.0008 \| 0.0008 \| nan \| 0.0008 \| 0.0 \| 0.0008 \|
	\| 0.1974 \| 0.97 \| 4800 \| 0.0860 \| 0.0004 \| 0.0009 \| 0.0009 \| nan \| 0.0009 \| 0.0 \| 0.0009 \|
	\| 0.0184 \| 0.99 \| 4900 \| 0.0857 \| 0.0010 \| 0.0021 \| 0.0021 \| nan \| 0.0021 \| 0.0 \| 0.0021 \|


	### Framework versions

	- Transformers 4.31.0
	- Pytorch 2.0.1+cu118
	- Datasets 2.13.1
	- Tokenizers 0.13.3