CrackSeg-MIT-b0-aug / README.md

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	---
	license: other
	base_model: nvidia/mit-b0
	tags:
	- generated_from_trainer
	model-index:
	- name: CrackSeg-MIT-b0-aug
	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. -->

	# CrackSeg-MIT-b0-aug

	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: 0.0578
	- Mean Iou: 0.3169
	- Mean Accuracy: 0.6337
	- Overall Accuracy: 0.6337
	- Accuracy Background: nan
	- Accuracy Crack: 0.6337
	- Iou Background: 0.0
	- Iou Crack: 0.6337

	## 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: 2

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \| Mean Iou \| Mean Accuracy \| Overall Accuracy \| Accuracy Background \| Accuracy Crack \| Iou Background \| Iou Crack \|
	\|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:-------------:\|:----------------:\|:-------------------:\|:--------------:\|:--------------:\|:---------:\|
	\| 0.2102 \| 0.04 \| 100 \| 0.1362 \| 0.1116 \| 0.2232 \| 0.2232 \| nan \| 0.2232 \| 0.0 \| 0.2232 \|
	\| 0.065 \| 0.08 \| 200 \| 0.1125 \| 0.0153 \| 0.0305 \| 0.0305 \| nan \| 0.0305 \| 0.0 \| 0.0305 \|
	\| 0.1738 \| 0.12 \| 300 \| 0.1165 \| 0.1976 \| 0.3953 \| 0.3953 \| nan \| 0.3953 \| 0.0 \| 0.3953 \|
	\| 0.0476 \| 0.17 \| 400 \| 0.1979 \| 0.0120 \| 0.0241 \| 0.0241 \| nan \| 0.0241 \| 0.0 \| 0.0241 \|
	\| 0.0524 \| 0.21 \| 500 \| 0.1063 \| 0.0533 \| 0.1066 \| 0.1066 \| nan \| 0.1066 \| 0.0 \| 0.1066 \|
	\| 0.0496 \| 0.25 \| 600 \| 0.1154 \| 0.1646 \| 0.3292 \| 0.3292 \| nan \| 0.3292 \| 0.0 \| 0.3292 \|
	\| 0.0497 \| 0.29 \| 700 \| 0.0795 \| 0.3184 \| 0.6368 \| 0.6368 \| nan \| 0.6368 \| 0.0 \| 0.6368 \|
	\| 0.032 \| 0.33 \| 800 \| 0.0905 \| 0.1792 \| 0.3583 \| 0.3583 \| nan \| 0.3583 \| 0.0 \| 0.3583 \|
	\| 0.1207 \| 0.37 \| 900 \| 0.0738 \| 0.2401 \| 0.4802 \| 0.4802 \| nan \| 0.4802 \| 0.0 \| 0.4802 \|
	\| 0.0511 \| 0.41 \| 1000 \| 0.0883 \| 0.2591 \| 0.5182 \| 0.5182 \| nan \| 0.5182 \| 0.0 \| 0.5182 \|
	\| 0.0264 \| 0.46 \| 1100 \| 0.0815 \| 0.1655 \| 0.3309 \| 0.3309 \| nan \| 0.3309 \| 0.0 \| 0.3309 \|
	\| 0.0719 \| 0.5 \| 1200 \| 0.0772 \| 0.3040 \| 0.6080 \| 0.6080 \| nan \| 0.6080 \| 0.0 \| 0.6080 \|
	\| 0.042 \| 0.54 \| 1300 \| 0.0707 \| 0.2797 \| 0.5593 \| 0.5593 \| nan \| 0.5593 \| 0.0 \| 0.5593 \|
	\| 0.167 \| 0.58 \| 1400 \| 0.0685 \| 0.3609 \| 0.7218 \| 0.7218 \| nan \| 0.7218 \| 0.0 \| 0.7218 \|
	\| 0.0206 \| 0.62 \| 1500 \| 0.0655 \| 0.2469 \| 0.4937 \| 0.4937 \| nan \| 0.4937 \| 0.0 \| 0.4937 \|
	\| 0.0211 \| 0.66 \| 1600 \| 0.0937 \| 0.3334 \| 0.6668 \| 0.6668 \| nan \| 0.6668 \| 0.0 \| 0.6668 \|
	\| 0.0659 \| 0.7 \| 1700 \| 0.0750 \| 0.2382 \| 0.4764 \| 0.4764 \| nan \| 0.4764 \| 0.0 \| 0.4764 \|
	\| 0.0478 \| 0.75 \| 1800 \| 0.0693 \| 0.2944 \| 0.5888 \| 0.5888 \| nan \| 0.5888 \| 0.0 \| 0.5888 \|
	\| 0.0287 \| 0.79 \| 1900 \| 0.0710 \| 0.2395 \| 0.4790 \| 0.4790 \| nan \| 0.4790 \| 0.0 \| 0.4790 \|
	\| 0.0359 \| 0.83 \| 2000 \| 0.0580 \| 0.3385 \| 0.6771 \| 0.6771 \| nan \| 0.6771 \| 0.0 \| 0.6771 \|
	\| 0.0309 \| 0.87 \| 2100 \| 0.0744 \| 0.2153 \| 0.4305 \| 0.4305 \| nan \| 0.4305 \| 0.0 \| 0.4305 \|
	\| 0.0039 \| 0.91 \| 2200 \| 0.0636 \| 0.2974 \| 0.5947 \| 0.5947 \| nan \| 0.5947 \| 0.0 \| 0.5947 \|
	\| 0.0152 \| 0.95 \| 2300 \| 0.0635 \| 0.3215 \| 0.6430 \| 0.6430 \| nan \| 0.6430 \| 0.0 \| 0.6430 \|
	\| 0.0233 \| 0.99 \| 2400 \| 0.0668 \| 0.3039 \| 0.6077 \| 0.6077 \| nan \| 0.6077 \| 0.0 \| 0.6077 \|
	\| 0.0088 \| 1.04 \| 2500 \| 0.0673 \| 0.3352 \| 0.6704 \| 0.6704 \| nan \| 0.6704 \| 0.0 \| 0.6704 \|
	\| 0.0756 \| 1.08 \| 2600 \| 0.0599 \| 0.3310 \| 0.6621 \| 0.6621 \| nan \| 0.6621 \| 0.0 \| 0.6621 \|
	\| 0.0522 \| 1.12 \| 2700 \| 0.0674 \| 0.2943 \| 0.5885 \| 0.5885 \| nan \| 0.5885 \| 0.0 \| 0.5885 \|
	\| 0.0595 \| 1.16 \| 2800 \| 0.0828 \| 0.2382 \| 0.4763 \| 0.4763 \| nan \| 0.4763 \| 0.0 \| 0.4763 \|
	\| 0.0135 \| 1.2 \| 2900 \| 0.0574 \| 0.2901 \| 0.5802 \| 0.5802 \| nan \| 0.5802 \| 0.0 \| 0.5802 \|
	\| 0.0289 \| 1.24 \| 3000 \| 0.0700 \| 0.3186 \| 0.6372 \| 0.6372 \| nan \| 0.6372 \| 0.0 \| 0.6372 \|
	\| 0.0403 \| 1.28 \| 3100 \| 0.0761 \| 0.3741 \| 0.7483 \| 0.7483 \| nan \| 0.7483 \| 0.0 \| 0.7483 \|
	\| 0.0131 \| 1.33 \| 3200 \| 0.0600 \| 0.3285 \| 0.6570 \| 0.6570 \| nan \| 0.6570 \| 0.0 \| 0.6570 \|
	\| 0.0957 \| 1.37 \| 3300 \| 0.0633 \| 0.3400 \| 0.6801 \| 0.6801 \| nan \| 0.6801 \| 0.0 \| 0.6801 \|
	\| 0.0152 \| 1.41 \| 3400 \| 0.0678 \| 0.3479 \| 0.6958 \| 0.6958 \| nan \| 0.6958 \| 0.0 \| 0.6958 \|
	\| 0.0235 \| 1.45 \| 3500 \| 0.0636 \| 0.3416 \| 0.6832 \| 0.6832 \| nan \| 0.6832 \| 0.0 \| 0.6832 \|
	\| 0.0304 \| 1.49 \| 3600 \| 0.0596 \| 0.3606 \| 0.7211 \| 0.7211 \| nan \| 0.7211 \| 0.0 \| 0.7211 \|
	\| 0.0012 \| 1.53 \| 3700 \| 0.0605 \| 0.2992 \| 0.5983 \| 0.5983 \| nan \| 0.5983 \| 0.0 \| 0.5983 \|
	\| 0.0435 \| 1.57 \| 3800 \| 0.0563 \| 0.3283 \| 0.6566 \| 0.6566 \| nan \| 0.6566 \| 0.0 \| 0.6566 \|
	\| 0.05 \| 1.61 \| 3900 \| 0.0601 \| 0.3314 \| 0.6628 \| 0.6628 \| nan \| 0.6628 \| 0.0 \| 0.6628 \|
	\| 0.063 \| 1.66 \| 4000 \| 0.0617 \| 0.3307 \| 0.6614 \| 0.6614 \| nan \| 0.6614 \| 0.0 \| 0.6614 \|
	\| 0.0552 \| 1.7 \| 4100 \| 0.0626 \| 0.3580 \| 0.7161 \| 0.7161 \| nan \| 0.7161 \| 0.0 \| 0.7161 \|
	\| 0.0153 \| 1.74 \| 4200 \| 0.0622 \| 0.2864 \| 0.5728 \| 0.5728 \| nan \| 0.5728 \| 0.0 \| 0.5728 \|
	\| 0.0446 \| 1.78 \| 4300 \| 0.0612 \| 0.3224 \| 0.6448 \| 0.6448 \| nan \| 0.6448 \| 0.0 \| 0.6448 \|
	\| 0.0203 \| 1.82 \| 4400 \| 0.0589 \| 0.3167 \| 0.6334 \| 0.6334 \| nan \| 0.6334 \| 0.0 \| 0.6334 \|
	\| 0.0424 \| 1.86 \| 4500 \| 0.0567 \| 0.3443 \| 0.6887 \| 0.6887 \| nan \| 0.6887 \| 0.0 \| 0.6887 \|
	\| 0.0103 \| 1.9 \| 4600 \| 0.0591 \| 0.3282 \| 0.6563 \| 0.6563 \| nan \| 0.6563 \| 0.0 \| 0.6563 \|
	\| 0.0831 \| 1.95 \| 4700 \| 0.0573 \| 0.3224 \| 0.6447 \| 0.6447 \| nan \| 0.6447 \| 0.0 \| 0.6447 \|
	\| 0.1301 \| 1.99 \| 4800 \| 0.0578 \| 0.3169 \| 0.6337 \| 0.6337 \| nan \| 0.6337 \| 0.0 \| 0.6337 \|


	### Framework versions

	- Transformers 4.32.0
	- Pytorch 2.0.1+cu118
	- Datasets 2.14.4
	- Tokenizers 0.13.3

	---
	license: other
	base_model: nvidia/mit-b0
	tags:
	- generated_from_trainer
	model-index:
	- name: CrackSeg-MIT-b0-aug
	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. -->

	# CrackSeg-MIT-b0-aug

	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: 0.0578
	- Mean Iou: 0.3169
	- Mean Accuracy: 0.6337
	- Overall Accuracy: 0.6337
	- Accuracy Background: nan
	- Accuracy Crack: 0.6337
	- Iou Background: 0.0
	- Iou Crack: 0.6337

	## 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: 2

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \| Mean Iou \| Mean Accuracy \| Overall Accuracy \| Accuracy Background \| Accuracy Crack \| Iou Background \| Iou Crack \|
	\|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:-------------:\|:----------------:\|:-------------------:\|:--------------:\|:--------------:\|:---------:\|
	\| 0.2102 \| 0.04 \| 100 \| 0.1362 \| 0.1116 \| 0.2232 \| 0.2232 \| nan \| 0.2232 \| 0.0 \| 0.2232 \|
	\| 0.065 \| 0.08 \| 200 \| 0.1125 \| 0.0153 \| 0.0305 \| 0.0305 \| nan \| 0.0305 \| 0.0 \| 0.0305 \|
	\| 0.1738 \| 0.12 \| 300 \| 0.1165 \| 0.1976 \| 0.3953 \| 0.3953 \| nan \| 0.3953 \| 0.0 \| 0.3953 \|
	\| 0.0476 \| 0.17 \| 400 \| 0.1979 \| 0.0120 \| 0.0241 \| 0.0241 \| nan \| 0.0241 \| 0.0 \| 0.0241 \|
	\| 0.0524 \| 0.21 \| 500 \| 0.1063 \| 0.0533 \| 0.1066 \| 0.1066 \| nan \| 0.1066 \| 0.0 \| 0.1066 \|
	\| 0.0496 \| 0.25 \| 600 \| 0.1154 \| 0.1646 \| 0.3292 \| 0.3292 \| nan \| 0.3292 \| 0.0 \| 0.3292 \|
	\| 0.0497 \| 0.29 \| 700 \| 0.0795 \| 0.3184 \| 0.6368 \| 0.6368 \| nan \| 0.6368 \| 0.0 \| 0.6368 \|
	\| 0.032 \| 0.33 \| 800 \| 0.0905 \| 0.1792 \| 0.3583 \| 0.3583 \| nan \| 0.3583 \| 0.0 \| 0.3583 \|
	\| 0.1207 \| 0.37 \| 900 \| 0.0738 \| 0.2401 \| 0.4802 \| 0.4802 \| nan \| 0.4802 \| 0.0 \| 0.4802 \|
	\| 0.0511 \| 0.41 \| 1000 \| 0.0883 \| 0.2591 \| 0.5182 \| 0.5182 \| nan \| 0.5182 \| 0.0 \| 0.5182 \|
	\| 0.0264 \| 0.46 \| 1100 \| 0.0815 \| 0.1655 \| 0.3309 \| 0.3309 \| nan \| 0.3309 \| 0.0 \| 0.3309 \|
	\| 0.0719 \| 0.5 \| 1200 \| 0.0772 \| 0.3040 \| 0.6080 \| 0.6080 \| nan \| 0.6080 \| 0.0 \| 0.6080 \|
	\| 0.042 \| 0.54 \| 1300 \| 0.0707 \| 0.2797 \| 0.5593 \| 0.5593 \| nan \| 0.5593 \| 0.0 \| 0.5593 \|
	\| 0.167 \| 0.58 \| 1400 \| 0.0685 \| 0.3609 \| 0.7218 \| 0.7218 \| nan \| 0.7218 \| 0.0 \| 0.7218 \|
	\| 0.0206 \| 0.62 \| 1500 \| 0.0655 \| 0.2469 \| 0.4937 \| 0.4937 \| nan \| 0.4937 \| 0.0 \| 0.4937 \|
	\| 0.0211 \| 0.66 \| 1600 \| 0.0937 \| 0.3334 \| 0.6668 \| 0.6668 \| nan \| 0.6668 \| 0.0 \| 0.6668 \|
	\| 0.0659 \| 0.7 \| 1700 \| 0.0750 \| 0.2382 \| 0.4764 \| 0.4764 \| nan \| 0.4764 \| 0.0 \| 0.4764 \|
	\| 0.0478 \| 0.75 \| 1800 \| 0.0693 \| 0.2944 \| 0.5888 \| 0.5888 \| nan \| 0.5888 \| 0.0 \| 0.5888 \|
	\| 0.0287 \| 0.79 \| 1900 \| 0.0710 \| 0.2395 \| 0.4790 \| 0.4790 \| nan \| 0.4790 \| 0.0 \| 0.4790 \|
	\| 0.0359 \| 0.83 \| 2000 \| 0.0580 \| 0.3385 \| 0.6771 \| 0.6771 \| nan \| 0.6771 \| 0.0 \| 0.6771 \|
	\| 0.0309 \| 0.87 \| 2100 \| 0.0744 \| 0.2153 \| 0.4305 \| 0.4305 \| nan \| 0.4305 \| 0.0 \| 0.4305 \|
	\| 0.0039 \| 0.91 \| 2200 \| 0.0636 \| 0.2974 \| 0.5947 \| 0.5947 \| nan \| 0.5947 \| 0.0 \| 0.5947 \|
	\| 0.0152 \| 0.95 \| 2300 \| 0.0635 \| 0.3215 \| 0.6430 \| 0.6430 \| nan \| 0.6430 \| 0.0 \| 0.6430 \|
	\| 0.0233 \| 0.99 \| 2400 \| 0.0668 \| 0.3039 \| 0.6077 \| 0.6077 \| nan \| 0.6077 \| 0.0 \| 0.6077 \|
	\| 0.0088 \| 1.04 \| 2500 \| 0.0673 \| 0.3352 \| 0.6704 \| 0.6704 \| nan \| 0.6704 \| 0.0 \| 0.6704 \|
	\| 0.0756 \| 1.08 \| 2600 \| 0.0599 \| 0.3310 \| 0.6621 \| 0.6621 \| nan \| 0.6621 \| 0.0 \| 0.6621 \|
	\| 0.0522 \| 1.12 \| 2700 \| 0.0674 \| 0.2943 \| 0.5885 \| 0.5885 \| nan \| 0.5885 \| 0.0 \| 0.5885 \|
	\| 0.0595 \| 1.16 \| 2800 \| 0.0828 \| 0.2382 \| 0.4763 \| 0.4763 \| nan \| 0.4763 \| 0.0 \| 0.4763 \|
	\| 0.0135 \| 1.2 \| 2900 \| 0.0574 \| 0.2901 \| 0.5802 \| 0.5802 \| nan \| 0.5802 \| 0.0 \| 0.5802 \|
	\| 0.0289 \| 1.24 \| 3000 \| 0.0700 \| 0.3186 \| 0.6372 \| 0.6372 \| nan \| 0.6372 \| 0.0 \| 0.6372 \|
	\| 0.0403 \| 1.28 \| 3100 \| 0.0761 \| 0.3741 \| 0.7483 \| 0.7483 \| nan \| 0.7483 \| 0.0 \| 0.7483 \|
	\| 0.0131 \| 1.33 \| 3200 \| 0.0600 \| 0.3285 \| 0.6570 \| 0.6570 \| nan \| 0.6570 \| 0.0 \| 0.6570 \|
	\| 0.0957 \| 1.37 \| 3300 \| 0.0633 \| 0.3400 \| 0.6801 \| 0.6801 \| nan \| 0.6801 \| 0.0 \| 0.6801 \|
	\| 0.0152 \| 1.41 \| 3400 \| 0.0678 \| 0.3479 \| 0.6958 \| 0.6958 \| nan \| 0.6958 \| 0.0 \| 0.6958 \|
	\| 0.0235 \| 1.45 \| 3500 \| 0.0636 \| 0.3416 \| 0.6832 \| 0.6832 \| nan \| 0.6832 \| 0.0 \| 0.6832 \|
	\| 0.0304 \| 1.49 \| 3600 \| 0.0596 \| 0.3606 \| 0.7211 \| 0.7211 \| nan \| 0.7211 \| 0.0 \| 0.7211 \|
	\| 0.0012 \| 1.53 \| 3700 \| 0.0605 \| 0.2992 \| 0.5983 \| 0.5983 \| nan \| 0.5983 \| 0.0 \| 0.5983 \|
	\| 0.0435 \| 1.57 \| 3800 \| 0.0563 \| 0.3283 \| 0.6566 \| 0.6566 \| nan \| 0.6566 \| 0.0 \| 0.6566 \|
	\| 0.05 \| 1.61 \| 3900 \| 0.0601 \| 0.3314 \| 0.6628 \| 0.6628 \| nan \| 0.6628 \| 0.0 \| 0.6628 \|
	\| 0.063 \| 1.66 \| 4000 \| 0.0617 \| 0.3307 \| 0.6614 \| 0.6614 \| nan \| 0.6614 \| 0.0 \| 0.6614 \|
	\| 0.0552 \| 1.7 \| 4100 \| 0.0626 \| 0.3580 \| 0.7161 \| 0.7161 \| nan \| 0.7161 \| 0.0 \| 0.7161 \|
	\| 0.0153 \| 1.74 \| 4200 \| 0.0622 \| 0.2864 \| 0.5728 \| 0.5728 \| nan \| 0.5728 \| 0.0 \| 0.5728 \|
	\| 0.0446 \| 1.78 \| 4300 \| 0.0612 \| 0.3224 \| 0.6448 \| 0.6448 \| nan \| 0.6448 \| 0.0 \| 0.6448 \|
	\| 0.0203 \| 1.82 \| 4400 \| 0.0589 \| 0.3167 \| 0.6334 \| 0.6334 \| nan \| 0.6334 \| 0.0 \| 0.6334 \|
	\| 0.0424 \| 1.86 \| 4500 \| 0.0567 \| 0.3443 \| 0.6887 \| 0.6887 \| nan \| 0.6887 \| 0.0 \| 0.6887 \|
	\| 0.0103 \| 1.9 \| 4600 \| 0.0591 \| 0.3282 \| 0.6563 \| 0.6563 \| nan \| 0.6563 \| 0.0 \| 0.6563 \|
	\| 0.0831 \| 1.95 \| 4700 \| 0.0573 \| 0.3224 \| 0.6447 \| 0.6447 \| nan \| 0.6447 \| 0.0 \| 0.6447 \|
	\| 0.1301 \| 1.99 \| 4800 \| 0.0578 \| 0.3169 \| 0.6337 \| 0.6337 \| nan \| 0.6337 \| 0.0 \| 0.6337 \|


	### Framework versions

	- Transformers 4.32.0
	- Pytorch 2.0.1+cu118
	- Datasets 2.14.4
	- Tokenizers 0.13.3