End of training

3074525 verified 8 months ago

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	---
	license: other
	base_model: nvidia/mit-b0
	tags:
	- vision
	- image-segmentation
	- generated_from_trainer
	model-index:
	- name: segformer-b0-miic-tl
	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-miic-tl

	This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the yijisuk/ic-chip-sample dataset.
	It achieves the following results on the evaluation set:
	- Loss: 0.4495
	- Mean Iou: 0.4202
	- Mean Accuracy: 0.8404
	- Overall Accuracy: 0.8404
	- Accuracy Unlabeled: nan
	- Accuracy Circuit: 0.8404
	- Iou Unlabeled: 0.0
	- Iou Circuit: 0.8404

	## 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 Circuit \| Iou Unlabeled \| Iou Circuit \|
	\|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:-------------:\|:----------------:\|:------------------:\|:----------------:\|:-------------:\|:-----------:\|
	\| 0.465 \| 1.0 \| 20 \| 0.6113 \| 0.3296 \| 0.6593 \| 0.6593 \| nan \| 0.6593 \| 0.0 \| 0.6593 \|
	\| 0.4071 \| 2.0 \| 40 \| 0.4866 \| 0.3345 \| 0.6689 \| 0.6689 \| nan \| 0.6689 \| 0.0 \| 0.6689 \|
	\| 0.4429 \| 3.0 \| 60 \| 0.3963 \| 0.3623 \| 0.7245 \| 0.7245 \| nan \| 0.7245 \| 0.0 \| 0.7245 \|
	\| 0.2408 \| 4.0 \| 80 \| 0.3606 \| 0.4257 \| 0.8515 \| 0.8515 \| nan \| 0.8515 \| 0.0 \| 0.8515 \|
	\| 0.2002 \| 5.0 \| 100 \| 0.3594 \| 0.4345 \| 0.8690 \| 0.8690 \| nan \| 0.8690 \| 0.0 \| 0.8690 \|
	\| 0.1885 \| 6.0 \| 120 \| 0.3702 \| 0.4291 \| 0.8583 \| 0.8583 \| nan \| 0.8583 \| 0.0 \| 0.8583 \|
	\| 0.4626 \| 7.0 \| 140 \| 0.3858 \| 0.4128 \| 0.8256 \| 0.8256 \| nan \| 0.8256 \| 0.0 \| 0.8256 \|
	\| 0.0865 \| 8.0 \| 160 \| 0.3578 \| 0.4456 \| 0.8912 \| 0.8912 \| nan \| 0.8912 \| 0.0 \| 0.8912 \|
	\| 0.0752 \| 9.0 \| 180 \| 0.3595 \| 0.4387 \| 0.8774 \| 0.8774 \| nan \| 0.8774 \| 0.0 \| 0.8774 \|
	\| 0.2567 \| 10.0 \| 200 \| 0.4103 \| 0.3981 \| 0.7961 \| 0.7961 \| nan \| 0.7961 \| 0.0 \| 0.7961 \|
	\| 0.1419 \| 11.0 \| 220 \| 0.4053 \| 0.4229 \| 0.8458 \| 0.8458 \| nan \| 0.8458 \| 0.0 \| 0.8458 \|
	\| 0.0623 \| 12.0 \| 240 \| 0.3798 \| 0.4415 \| 0.8830 \| 0.8830 \| nan \| 0.8830 \| 0.0 \| 0.8830 \|
	\| 0.3336 \| 13.0 \| 260 \| 0.3855 \| 0.4374 \| 0.8748 \| 0.8748 \| nan \| 0.8748 \| 0.0 \| 0.8748 \|
	\| 0.1283 \| 14.0 \| 280 \| 0.3931 \| 0.4368 \| 0.8736 \| 0.8736 \| nan \| 0.8736 \| 0.0 \| 0.8736 \|
	\| 0.5155 \| 15.0 \| 300 \| 0.4108 \| 0.4268 \| 0.8535 \| 0.8535 \| nan \| 0.8535 \| 0.0 \| 0.8535 \|
	\| 1.2662 \| 16.0 \| 320 \| 0.4062 \| 0.4328 \| 0.8656 \| 0.8656 \| nan \| 0.8656 \| 0.0 \| 0.8656 \|
	\| 0.2631 \| 17.0 \| 340 \| 0.3825 \| 0.4464 \| 0.8929 \| 0.8929 \| nan \| 0.8929 \| 0.0 \| 0.8929 \|
	\| 0.1751 \| 18.0 \| 360 \| 0.3981 \| 0.4335 \| 0.8669 \| 0.8669 \| nan \| 0.8669 \| 0.0 \| 0.8669 \|
	\| 0.243 \| 19.0 \| 380 \| 0.3963 \| 0.4436 \| 0.8872 \| 0.8872 \| nan \| 0.8872 \| 0.0 \| 0.8872 \|
	\| 0.1779 \| 20.0 \| 400 \| 0.4413 \| 0.4060 \| 0.8119 \| 0.8119 \| nan \| 0.8119 \| 0.0 \| 0.8119 \|
	\| 0.0682 \| 21.0 \| 420 \| 0.4106 \| 0.4363 \| 0.8725 \| 0.8725 \| nan \| 0.8725 \| 0.0 \| 0.8725 \|
	\| 0.2943 \| 22.0 \| 440 \| 0.4052 \| 0.4386 \| 0.8771 \| 0.8771 \| nan \| 0.8771 \| 0.0 \| 0.8771 \|
	\| 0.118 \| 23.0 \| 460 \| 0.4260 \| 0.4197 \| 0.8394 \| 0.8394 \| nan \| 0.8394 \| 0.0 \| 0.8394 \|
	\| 0.0865 \| 24.0 \| 480 \| 0.4023 \| 0.4270 \| 0.8540 \| 0.8540 \| nan \| 0.8540 \| 0.0 \| 0.8540 \|
	\| 0.1693 \| 25.0 \| 500 \| 0.4276 \| 0.4199 \| 0.8399 \| 0.8399 \| nan \| 0.8399 \| 0.0 \| 0.8399 \|
	\| 0.1778 \| 26.0 \| 520 \| 0.4044 \| 0.4409 \| 0.8818 \| 0.8818 \| nan \| 0.8818 \| 0.0 \| 0.8818 \|
	\| 0.3617 \| 27.0 \| 540 \| 0.4405 \| 0.4121 \| 0.8242 \| 0.8242 \| nan \| 0.8242 \| 0.0 \| 0.8242 \|
	\| 0.1688 \| 28.0 \| 560 \| 0.4333 \| 0.4234 \| 0.8467 \| 0.8467 \| nan \| 0.8467 \| 0.0 \| 0.8467 \|
	\| 0.282 \| 29.0 \| 580 \| 0.4060 \| 0.4365 \| 0.8730 \| 0.8730 \| nan \| 0.8730 \| 0.0 \| 0.8730 \|
	\| 0.0992 \| 30.0 \| 600 \| 0.4297 \| 0.4196 \| 0.8393 \| 0.8393 \| nan \| 0.8393 \| 0.0 \| 0.8393 \|
	\| 1.379 \| 31.0 \| 620 \| 0.4389 \| 0.4193 \| 0.8386 \| 0.8386 \| nan \| 0.8386 \| 0.0 \| 0.8386 \|
	\| 0.1355 \| 32.0 \| 640 \| 0.4438 \| 0.4205 \| 0.8410 \| 0.8410 \| nan \| 0.8410 \| 0.0 \| 0.8410 \|
	\| 0.1067 \| 33.0 \| 660 \| 0.4271 \| 0.4299 \| 0.8598 \| 0.8598 \| nan \| 0.8598 \| 0.0 \| 0.8598 \|
	\| 1.0659 \| 34.0 \| 680 \| 0.4490 \| 0.4063 \| 0.8125 \| 0.8125 \| nan \| 0.8125 \| 0.0 \| 0.8125 \|
	\| 0.1481 \| 35.0 \| 700 \| 0.4317 \| 0.4279 \| 0.8557 \| 0.8557 \| nan \| 0.8557 \| 0.0 \| 0.8557 \|
	\| 1.385 \| 36.0 \| 720 \| 0.4215 \| 0.4322 \| 0.8644 \| 0.8644 \| nan \| 0.8644 \| 0.0 \| 0.8644 \|
	\| 0.3081 \| 37.0 \| 740 \| 0.4564 \| 0.4089 \| 0.8178 \| 0.8178 \| nan \| 0.8178 \| 0.0 \| 0.8178 \|
	\| 0.1989 \| 38.0 \| 760 \| 0.4345 \| 0.4241 \| 0.8482 \| 0.8482 \| nan \| 0.8482 \| 0.0 \| 0.8482 \|
	\| 0.1752 \| 39.0 \| 780 \| 0.4230 \| 0.4302 \| 0.8605 \| 0.8605 \| nan \| 0.8605 \| 0.0 \| 0.8605 \|
	\| 0.1489 \| 40.0 \| 800 \| 0.4253 \| 0.4231 \| 0.8462 \| 0.8462 \| nan \| 0.8462 \| 0.0 \| 0.8462 \|
	\| 0.1769 \| 41.0 \| 820 \| 0.4184 \| 0.4275 \| 0.8549 \| 0.8549 \| nan \| 0.8549 \| 0.0 \| 0.8549 \|
	\| 0.1927 \| 42.0 \| 840 \| 0.4162 \| 0.4314 \| 0.8629 \| 0.8629 \| nan \| 0.8629 \| 0.0 \| 0.8629 \|
	\| 0.2442 \| 43.0 \| 860 \| 0.4321 \| 0.4234 \| 0.8468 \| 0.8468 \| nan \| 0.8468 \| 0.0 \| 0.8468 \|
	\| 0.2513 \| 44.0 \| 880 \| 0.4280 \| 0.4258 \| 0.8515 \| 0.8515 \| nan \| 0.8515 \| 0.0 \| 0.8515 \|
	\| 0.7221 \| 45.0 \| 900 \| 0.4449 \| 0.4190 \| 0.8380 \| 0.8380 \| nan \| 0.8380 \| 0.0 \| 0.8380 \|
	\| 0.0675 \| 46.0 \| 920 \| 0.4369 \| 0.4210 \| 0.8419 \| 0.8419 \| nan \| 0.8419 \| 0.0 \| 0.8419 \|
	\| 0.1256 \| 47.0 \| 940 \| 0.4159 \| 0.4313 \| 0.8625 \| 0.8625 \| nan \| 0.8625 \| 0.0 \| 0.8625 \|
	\| 0.1251 \| 48.0 \| 960 \| 0.4312 \| 0.4249 \| 0.8498 \| 0.8498 \| nan \| 0.8498 \| 0.0 \| 0.8498 \|
	\| 0.2183 \| 49.0 \| 980 \| 0.4340 \| 0.4262 \| 0.8524 \| 0.8524 \| nan \| 0.8524 \| 0.0 \| 0.8524 \|
	\| 0.2148 \| 50.0 \| 1000 \| 0.4495 \| 0.4202 \| 0.8404 \| 0.8404 \| nan \| 0.8404 \| 0.0 \| 0.8404 \|


	### Framework versions

	- Transformers 4.36.2
	- Pytorch 1.11.0+cu115
	- Datasets 2.15.0
	- Tokenizers 0.15.0

	---
	license: other
	base_model: nvidia/mit-b0
	tags:
	- vision
	- image-segmentation
	- generated_from_trainer
	model-index:
	- name: segformer-b0-miic-tl
	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-miic-tl

	This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the yijisuk/ic-chip-sample dataset.
	It achieves the following results on the evaluation set:
	- Loss: 0.4495
	- Mean Iou: 0.4202
	- Mean Accuracy: 0.8404
	- Overall Accuracy: 0.8404
	- Accuracy Unlabeled: nan
	- Accuracy Circuit: 0.8404
	- Iou Unlabeled: 0.0
	- Iou Circuit: 0.8404

	## 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 Circuit \| Iou Unlabeled \| Iou Circuit \|
	\|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:-------------:\|:----------------:\|:------------------:\|:----------------:\|:-------------:\|:-----------:\|
	\| 0.465 \| 1.0 \| 20 \| 0.6113 \| 0.3296 \| 0.6593 \| 0.6593 \| nan \| 0.6593 \| 0.0 \| 0.6593 \|
	\| 0.4071 \| 2.0 \| 40 \| 0.4866 \| 0.3345 \| 0.6689 \| 0.6689 \| nan \| 0.6689 \| 0.0 \| 0.6689 \|
	\| 0.4429 \| 3.0 \| 60 \| 0.3963 \| 0.3623 \| 0.7245 \| 0.7245 \| nan \| 0.7245 \| 0.0 \| 0.7245 \|
	\| 0.2408 \| 4.0 \| 80 \| 0.3606 \| 0.4257 \| 0.8515 \| 0.8515 \| nan \| 0.8515 \| 0.0 \| 0.8515 \|
	\| 0.2002 \| 5.0 \| 100 \| 0.3594 \| 0.4345 \| 0.8690 \| 0.8690 \| nan \| 0.8690 \| 0.0 \| 0.8690 \|
	\| 0.1885 \| 6.0 \| 120 \| 0.3702 \| 0.4291 \| 0.8583 \| 0.8583 \| nan \| 0.8583 \| 0.0 \| 0.8583 \|
	\| 0.4626 \| 7.0 \| 140 \| 0.3858 \| 0.4128 \| 0.8256 \| 0.8256 \| nan \| 0.8256 \| 0.0 \| 0.8256 \|
	\| 0.0865 \| 8.0 \| 160 \| 0.3578 \| 0.4456 \| 0.8912 \| 0.8912 \| nan \| 0.8912 \| 0.0 \| 0.8912 \|
	\| 0.0752 \| 9.0 \| 180 \| 0.3595 \| 0.4387 \| 0.8774 \| 0.8774 \| nan \| 0.8774 \| 0.0 \| 0.8774 \|
	\| 0.2567 \| 10.0 \| 200 \| 0.4103 \| 0.3981 \| 0.7961 \| 0.7961 \| nan \| 0.7961 \| 0.0 \| 0.7961 \|
	\| 0.1419 \| 11.0 \| 220 \| 0.4053 \| 0.4229 \| 0.8458 \| 0.8458 \| nan \| 0.8458 \| 0.0 \| 0.8458 \|
	\| 0.0623 \| 12.0 \| 240 \| 0.3798 \| 0.4415 \| 0.8830 \| 0.8830 \| nan \| 0.8830 \| 0.0 \| 0.8830 \|
	\| 0.3336 \| 13.0 \| 260 \| 0.3855 \| 0.4374 \| 0.8748 \| 0.8748 \| nan \| 0.8748 \| 0.0 \| 0.8748 \|
	\| 0.1283 \| 14.0 \| 280 \| 0.3931 \| 0.4368 \| 0.8736 \| 0.8736 \| nan \| 0.8736 \| 0.0 \| 0.8736 \|
	\| 0.5155 \| 15.0 \| 300 \| 0.4108 \| 0.4268 \| 0.8535 \| 0.8535 \| nan \| 0.8535 \| 0.0 \| 0.8535 \|
	\| 1.2662 \| 16.0 \| 320 \| 0.4062 \| 0.4328 \| 0.8656 \| 0.8656 \| nan \| 0.8656 \| 0.0 \| 0.8656 \|
	\| 0.2631 \| 17.0 \| 340 \| 0.3825 \| 0.4464 \| 0.8929 \| 0.8929 \| nan \| 0.8929 \| 0.0 \| 0.8929 \|
	\| 0.1751 \| 18.0 \| 360 \| 0.3981 \| 0.4335 \| 0.8669 \| 0.8669 \| nan \| 0.8669 \| 0.0 \| 0.8669 \|
	\| 0.243 \| 19.0 \| 380 \| 0.3963 \| 0.4436 \| 0.8872 \| 0.8872 \| nan \| 0.8872 \| 0.0 \| 0.8872 \|
	\| 0.1779 \| 20.0 \| 400 \| 0.4413 \| 0.4060 \| 0.8119 \| 0.8119 \| nan \| 0.8119 \| 0.0 \| 0.8119 \|
	\| 0.0682 \| 21.0 \| 420 \| 0.4106 \| 0.4363 \| 0.8725 \| 0.8725 \| nan \| 0.8725 \| 0.0 \| 0.8725 \|
	\| 0.2943 \| 22.0 \| 440 \| 0.4052 \| 0.4386 \| 0.8771 \| 0.8771 \| nan \| 0.8771 \| 0.0 \| 0.8771 \|
	\| 0.118 \| 23.0 \| 460 \| 0.4260 \| 0.4197 \| 0.8394 \| 0.8394 \| nan \| 0.8394 \| 0.0 \| 0.8394 \|
	\| 0.0865 \| 24.0 \| 480 \| 0.4023 \| 0.4270 \| 0.8540 \| 0.8540 \| nan \| 0.8540 \| 0.0 \| 0.8540 \|
	\| 0.1693 \| 25.0 \| 500 \| 0.4276 \| 0.4199 \| 0.8399 \| 0.8399 \| nan \| 0.8399 \| 0.0 \| 0.8399 \|
	\| 0.1778 \| 26.0 \| 520 \| 0.4044 \| 0.4409 \| 0.8818 \| 0.8818 \| nan \| 0.8818 \| 0.0 \| 0.8818 \|
	\| 0.3617 \| 27.0 \| 540 \| 0.4405 \| 0.4121 \| 0.8242 \| 0.8242 \| nan \| 0.8242 \| 0.0 \| 0.8242 \|
	\| 0.1688 \| 28.0 \| 560 \| 0.4333 \| 0.4234 \| 0.8467 \| 0.8467 \| nan \| 0.8467 \| 0.0 \| 0.8467 \|
	\| 0.282 \| 29.0 \| 580 \| 0.4060 \| 0.4365 \| 0.8730 \| 0.8730 \| nan \| 0.8730 \| 0.0 \| 0.8730 \|
	\| 0.0992 \| 30.0 \| 600 \| 0.4297 \| 0.4196 \| 0.8393 \| 0.8393 \| nan \| 0.8393 \| 0.0 \| 0.8393 \|
	\| 1.379 \| 31.0 \| 620 \| 0.4389 \| 0.4193 \| 0.8386 \| 0.8386 \| nan \| 0.8386 \| 0.0 \| 0.8386 \|
	\| 0.1355 \| 32.0 \| 640 \| 0.4438 \| 0.4205 \| 0.8410 \| 0.8410 \| nan \| 0.8410 \| 0.0 \| 0.8410 \|
	\| 0.1067 \| 33.0 \| 660 \| 0.4271 \| 0.4299 \| 0.8598 \| 0.8598 \| nan \| 0.8598 \| 0.0 \| 0.8598 \|
	\| 1.0659 \| 34.0 \| 680 \| 0.4490 \| 0.4063 \| 0.8125 \| 0.8125 \| nan \| 0.8125 \| 0.0 \| 0.8125 \|
	\| 0.1481 \| 35.0 \| 700 \| 0.4317 \| 0.4279 \| 0.8557 \| 0.8557 \| nan \| 0.8557 \| 0.0 \| 0.8557 \|
	\| 1.385 \| 36.0 \| 720 \| 0.4215 \| 0.4322 \| 0.8644 \| 0.8644 \| nan \| 0.8644 \| 0.0 \| 0.8644 \|
	\| 0.3081 \| 37.0 \| 740 \| 0.4564 \| 0.4089 \| 0.8178 \| 0.8178 \| nan \| 0.8178 \| 0.0 \| 0.8178 \|
	\| 0.1989 \| 38.0 \| 760 \| 0.4345 \| 0.4241 \| 0.8482 \| 0.8482 \| nan \| 0.8482 \| 0.0 \| 0.8482 \|
	\| 0.1752 \| 39.0 \| 780 \| 0.4230 \| 0.4302 \| 0.8605 \| 0.8605 \| nan \| 0.8605 \| 0.0 \| 0.8605 \|
	\| 0.1489 \| 40.0 \| 800 \| 0.4253 \| 0.4231 \| 0.8462 \| 0.8462 \| nan \| 0.8462 \| 0.0 \| 0.8462 \|
	\| 0.1769 \| 41.0 \| 820 \| 0.4184 \| 0.4275 \| 0.8549 \| 0.8549 \| nan \| 0.8549 \| 0.0 \| 0.8549 \|
	\| 0.1927 \| 42.0 \| 840 \| 0.4162 \| 0.4314 \| 0.8629 \| 0.8629 \| nan \| 0.8629 \| 0.0 \| 0.8629 \|
	\| 0.2442 \| 43.0 \| 860 \| 0.4321 \| 0.4234 \| 0.8468 \| 0.8468 \| nan \| 0.8468 \| 0.0 \| 0.8468 \|
	\| 0.2513 \| 44.0 \| 880 \| 0.4280 \| 0.4258 \| 0.8515 \| 0.8515 \| nan \| 0.8515 \| 0.0 \| 0.8515 \|
	\| 0.7221 \| 45.0 \| 900 \| 0.4449 \| 0.4190 \| 0.8380 \| 0.8380 \| nan \| 0.8380 \| 0.0 \| 0.8380 \|
	\| 0.0675 \| 46.0 \| 920 \| 0.4369 \| 0.4210 \| 0.8419 \| 0.8419 \| nan \| 0.8419 \| 0.0 \| 0.8419 \|
	\| 0.1256 \| 47.0 \| 940 \| 0.4159 \| 0.4313 \| 0.8625 \| 0.8625 \| nan \| 0.8625 \| 0.0 \| 0.8625 \|
	\| 0.1251 \| 48.0 \| 960 \| 0.4312 \| 0.4249 \| 0.8498 \| 0.8498 \| nan \| 0.8498 \| 0.0 \| 0.8498 \|
	\| 0.2183 \| 49.0 \| 980 \| 0.4340 \| 0.4262 \| 0.8524 \| 0.8524 \| nan \| 0.8524 \| 0.0 \| 0.8524 \|
	\| 0.2148 \| 50.0 \| 1000 \| 0.4495 \| 0.4202 \| 0.8404 \| 0.8404 \| nan \| 0.8404 \| 0.0 \| 0.8404 \|


	### Framework versions

	- Transformers 4.36.2
	- Pytorch 1.11.0+cu115
	- Datasets 2.15.0
	- Tokenizers 0.15.0