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	---
	license: apache-2.0
	base_model: microsoft/resnet-50
	tags:
	- generated_from_trainer
	metrics:
	- accuracy
	model-index:
	- name: resnet101-base_tobacco-cnn_tobacco3482_kd_CEKD_t5.0_a0.7
	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. -->

	# resnet101-base_tobacco-cnn_tobacco3482_kd_CEKD_t5.0_a0.7

	This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the None dataset.
	It achieves the following results on the evaluation set:
	- Loss: 0.7861
	- Accuracy: 0.705
	- Brier Loss: 0.4410
	- Nll: 2.6519
	- F1 Micro: 0.705
	- F1 Macro: 0.6403
	- Ece: 0.2724
	- Aurc: 0.1188

	## 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: 0.0001
	- train_batch_size: 64
	- eval_batch_size: 64
	- seed: 42
	- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
	- lr_scheduler_type: linear
	- lr_scheduler_warmup_ratio: 0.1
	- num_epochs: 50

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Brier Loss \| Nll \| F1 Micro \| F1 Macro \| Ece \| Aurc \|
	\|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:----------:\|:------:\|:--------:\|:--------:\|:------:\|:------:\|
	\| No log \| 1.0 \| 13 \| 1.7831 \| 0.165 \| 0.8966 \| 8.4414 \| 0.165 \| 0.1121 \| 0.2151 \| 0.8335 \|
	\| No log \| 2.0 \| 26 \| 1.7753 \| 0.145 \| 0.8958 \| 8.5715 \| 0.145 \| 0.0954 \| 0.1998 \| 0.8332 \|
	\| No log \| 3.0 \| 39 \| 1.7334 \| 0.175 \| 0.8877 \| 6.4682 \| 0.175 \| 0.0756 \| 0.2069 \| 0.7896 \|
	\| No log \| 4.0 \| 52 \| 1.6604 \| 0.185 \| 0.8723 \| 6.0351 \| 0.185 \| 0.0505 \| 0.2328 \| 0.7549 \|
	\| No log \| 5.0 \| 65 \| 1.5874 \| 0.18 \| 0.8560 \| 6.0732 \| 0.18 \| 0.0431 \| 0.2285 \| 0.7506 \|
	\| No log \| 6.0 \| 78 \| 1.5223 \| 0.185 \| 0.8415 \| 6.1638 \| 0.185 \| 0.0479 \| 0.2419 \| 0.7530 \|
	\| No log \| 7.0 \| 91 \| 1.4642 \| 0.35 \| 0.8239 \| 6.0328 \| 0.35 \| 0.1696 \| 0.3081 \| 0.5219 \|
	\| No log \| 8.0 \| 104 \| 1.3599 \| 0.35 \| 0.7825 \| 6.2102 \| 0.35 \| 0.1908 \| 0.2977 \| 0.4172 \|
	\| No log \| 9.0 \| 117 \| 1.3083 \| 0.385 \| 0.7566 \| 5.7128 \| 0.3850 \| 0.2203 \| 0.3012 \| 0.3842 \|
	\| No log \| 10.0 \| 130 \| 1.3151 \| 0.365 \| 0.7670 \| 5.1073 \| 0.3650 \| 0.2150 \| 0.2923 \| 0.4891 \|
	\| No log \| 11.0 \| 143 \| 1.3736 \| 0.295 \| 0.7950 \| 5.3584 \| 0.295 \| 0.1747 \| 0.2716 \| 0.6360 \|
	\| No log \| 12.0 \| 156 \| 1.2655 \| 0.425 \| 0.7380 \| 4.0312 \| 0.425 \| 0.2789 \| 0.3273 \| 0.3366 \|
	\| No log \| 13.0 \| 169 \| 1.1696 \| 0.475 \| 0.6901 \| 3.9627 \| 0.4750 \| 0.3083 \| 0.3011 \| 0.2825 \|
	\| No log \| 14.0 \| 182 \| 1.2992 \| 0.355 \| 0.7473 \| 3.9098 \| 0.3550 \| 0.2292 \| 0.2675 \| 0.4929 \|
	\| No log \| 15.0 \| 195 \| 1.1698 \| 0.51 \| 0.6881 \| 3.7143 \| 0.51 \| 0.3691 \| 0.3333 \| 0.3278 \|
	\| No log \| 16.0 \| 208 \| 1.0624 \| 0.515 \| 0.6274 \| 3.8387 \| 0.515 \| 0.3631 \| 0.2821 \| 0.2583 \|
	\| No log \| 17.0 \| 221 \| 1.0970 \| 0.565 \| 0.6421 \| 3.3302 \| 0.565 \| 0.4493 \| 0.3362 \| 0.2373 \|
	\| No log \| 18.0 \| 234 \| 1.0029 \| 0.625 \| 0.5883 \| 3.3820 \| 0.625 \| 0.4675 \| 0.3005 \| 0.1660 \|
	\| No log \| 19.0 \| 247 \| 1.0384 \| 0.605 \| 0.6093 \| 3.3183 \| 0.605 \| 0.4863 \| 0.3252 \| 0.2145 \|
	\| No log \| 20.0 \| 260 \| 1.0686 \| 0.62 \| 0.6234 \| 3.0246 \| 0.62 \| 0.5155 \| 0.3625 \| 0.2334 \|
	\| No log \| 21.0 \| 273 \| 0.9641 \| 0.62 \| 0.5685 \| 2.9225 \| 0.62 \| 0.5259 \| 0.3103 \| 0.2063 \|
	\| No log \| 22.0 \| 286 \| 1.0054 \| 0.665 \| 0.5849 \| 3.0792 \| 0.665 \| 0.5614 \| 0.3636 \| 0.1863 \|
	\| No log \| 23.0 \| 299 \| 0.9959 \| 0.675 \| 0.5734 \| 2.9829 \| 0.675 \| 0.5577 \| 0.3619 \| 0.1806 \|
	\| No log \| 24.0 \| 312 \| 0.9044 \| 0.675 \| 0.5267 \| 2.8952 \| 0.675 \| 0.5712 \| 0.2989 \| 0.1475 \|
	\| No log \| 25.0 \| 325 \| 0.9803 \| 0.655 \| 0.5627 \| 2.7501 \| 0.655 \| 0.5418 \| 0.3415 \| 0.1919 \|
	\| No log \| 26.0 \| 338 \| 0.8814 \| 0.65 \| 0.5176 \| 2.8421 \| 0.65 \| 0.5619 \| 0.2665 \| 0.1694 \|
	\| No log \| 27.0 \| 351 \| 0.8555 \| 0.69 \| 0.4928 \| 2.7870 \| 0.69 \| 0.5831 \| 0.3091 \| 0.1279 \|
	\| No log \| 28.0 \| 364 \| 0.8290 \| 0.69 \| 0.4777 \| 2.6377 \| 0.69 \| 0.5976 \| 0.2551 \| 0.1290 \|
	\| No log \| 29.0 \| 377 \| 0.8593 \| 0.685 \| 0.4949 \| 2.5880 \| 0.685 \| 0.5776 \| 0.3083 \| 0.1279 \|
	\| No log \| 30.0 \| 390 \| 0.8226 \| 0.685 \| 0.4678 \| 2.8938 \| 0.685 \| 0.5884 \| 0.2820 \| 0.1249 \|
	\| No log \| 31.0 \| 403 \| 0.8578 \| 0.69 \| 0.4857 \| 2.6150 \| 0.69 \| 0.6024 \| 0.3109 \| 0.1344 \|
	\| No log \| 32.0 \| 416 \| 0.8330 \| 0.685 \| 0.4753 \| 2.5999 \| 0.685 \| 0.6047 \| 0.2688 \| 0.1407 \|
	\| No log \| 33.0 \| 429 \| 0.8268 \| 0.7 \| 0.4683 \| 2.6138 \| 0.7 \| 0.6193 \| 0.2913 \| 0.1315 \|
	\| No log \| 34.0 \| 442 \| 0.8535 \| 0.715 \| 0.4749 \| 2.5059 \| 0.715 \| 0.6450 \| 0.2931 \| 0.1190 \|
	\| No log \| 35.0 \| 455 \| 0.8334 \| 0.665 \| 0.4752 \| 2.3839 \| 0.665 \| 0.5950 \| 0.2762 \| 0.1397 \|
	\| No log \| 36.0 \| 468 \| 0.8025 \| 0.71 \| 0.4553 \| 2.4803 \| 0.7100 \| 0.6302 \| 0.2889 \| 0.1178 \|
	\| No log \| 37.0 \| 481 \| 0.8142 \| 0.715 \| 0.4563 \| 2.6785 \| 0.715 \| 0.6426 \| 0.2989 \| 0.1048 \|
	\| No log \| 38.0 \| 494 \| 0.8124 \| 0.7 \| 0.4538 \| 2.5320 \| 0.7 \| 0.6332 \| 0.2594 \| 0.1132 \|
	\| 0.9303 \| 39.0 \| 507 \| 0.7888 \| 0.69 \| 0.4452 \| 2.6427 \| 0.69 \| 0.6269 \| 0.2583 \| 0.1224 \|
	\| 0.9303 \| 40.0 \| 520 \| 0.7907 \| 0.705 \| 0.4458 \| 2.6942 \| 0.705 \| 0.6367 \| 0.2688 \| 0.1155 \|
	\| 0.9303 \| 41.0 \| 533 \| 0.7918 \| 0.71 \| 0.4442 \| 2.4378 \| 0.7100 \| 0.6558 \| 0.2816 \| 0.1132 \|
	\| 0.9303 \| 42.0 \| 546 \| 0.8005 \| 0.725 \| 0.4479 \| 2.6088 \| 0.7250 \| 0.6576 \| 0.2914 \| 0.1049 \|
	\| 0.9303 \| 43.0 \| 559 \| 0.7879 \| 0.72 \| 0.4421 \| 2.7052 \| 0.72 \| 0.6592 \| 0.2741 \| 0.1122 \|
	\| 0.9303 \| 44.0 \| 572 \| 0.7910 \| 0.71 \| 0.4461 \| 2.6463 \| 0.7100 \| 0.6463 \| 0.3119 \| 0.1188 \|
	\| 0.9303 \| 45.0 \| 585 \| 0.7922 \| 0.705 \| 0.4450 \| 2.6453 \| 0.705 \| 0.6481 \| 0.2753 \| 0.1211 \|
	\| 0.9303 \| 46.0 \| 598 \| 0.7915 \| 0.715 \| 0.4429 \| 2.6970 \| 0.715 \| 0.6526 \| 0.2741 \| 0.1107 \|
	\| 0.9303 \| 47.0 \| 611 \| 0.7809 \| 0.705 \| 0.4370 \| 2.6841 \| 0.705 \| 0.6453 \| 0.2734 \| 0.1158 \|
	\| 0.9303 \| 48.0 \| 624 \| 0.7771 \| 0.705 \| 0.4350 \| 2.6168 \| 0.705 \| 0.6423 \| 0.2652 \| 0.1139 \|
	\| 0.9303 \| 49.0 \| 637 \| 0.7826 \| 0.705 \| 0.4377 \| 2.5091 \| 0.705 \| 0.6423 \| 0.2758 \| 0.1202 \|
	\| 0.9303 \| 50.0 \| 650 \| 0.7861 \| 0.705 \| 0.4410 \| 2.6519 \| 0.705 \| 0.6403 \| 0.2724 \| 0.1188 \|


	### Framework versions

	- Transformers 4.33.3
	- Pytorch 2.2.0.dev20231002
	- Datasets 2.7.1
	- Tokenizers 0.13.3

	---
	license: apache-2.0
	base_model: microsoft/resnet-50
	tags:
	- generated_from_trainer
	metrics:
	- accuracy
	model-index:
	- name: resnet101-base_tobacco-cnn_tobacco3482_kd_CEKD_t5.0_a0.7
	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. -->

	# resnet101-base_tobacco-cnn_tobacco3482_kd_CEKD_t5.0_a0.7

	This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the None dataset.
	It achieves the following results on the evaluation set:
	- Loss: 0.7861
	- Accuracy: 0.705
	- Brier Loss: 0.4410
	- Nll: 2.6519
	- F1 Micro: 0.705
	- F1 Macro: 0.6403
	- Ece: 0.2724
	- Aurc: 0.1188

	## 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: 0.0001
	- train_batch_size: 64
	- eval_batch_size: 64
	- seed: 42
	- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
	- lr_scheduler_type: linear
	- lr_scheduler_warmup_ratio: 0.1
	- num_epochs: 50

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Brier Loss \| Nll \| F1 Micro \| F1 Macro \| Ece \| Aurc \|
	\|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:----------:\|:------:\|:--------:\|:--------:\|:------:\|:------:\|
	\| No log \| 1.0 \| 13 \| 1.7831 \| 0.165 \| 0.8966 \| 8.4414 \| 0.165 \| 0.1121 \| 0.2151 \| 0.8335 \|
	\| No log \| 2.0 \| 26 \| 1.7753 \| 0.145 \| 0.8958 \| 8.5715 \| 0.145 \| 0.0954 \| 0.1998 \| 0.8332 \|
	\| No log \| 3.0 \| 39 \| 1.7334 \| 0.175 \| 0.8877 \| 6.4682 \| 0.175 \| 0.0756 \| 0.2069 \| 0.7896 \|
	\| No log \| 4.0 \| 52 \| 1.6604 \| 0.185 \| 0.8723 \| 6.0351 \| 0.185 \| 0.0505 \| 0.2328 \| 0.7549 \|
	\| No log \| 5.0 \| 65 \| 1.5874 \| 0.18 \| 0.8560 \| 6.0732 \| 0.18 \| 0.0431 \| 0.2285 \| 0.7506 \|
	\| No log \| 6.0 \| 78 \| 1.5223 \| 0.185 \| 0.8415 \| 6.1638 \| 0.185 \| 0.0479 \| 0.2419 \| 0.7530 \|
	\| No log \| 7.0 \| 91 \| 1.4642 \| 0.35 \| 0.8239 \| 6.0328 \| 0.35 \| 0.1696 \| 0.3081 \| 0.5219 \|
	\| No log \| 8.0 \| 104 \| 1.3599 \| 0.35 \| 0.7825 \| 6.2102 \| 0.35 \| 0.1908 \| 0.2977 \| 0.4172 \|
	\| No log \| 9.0 \| 117 \| 1.3083 \| 0.385 \| 0.7566 \| 5.7128 \| 0.3850 \| 0.2203 \| 0.3012 \| 0.3842 \|
	\| No log \| 10.0 \| 130 \| 1.3151 \| 0.365 \| 0.7670 \| 5.1073 \| 0.3650 \| 0.2150 \| 0.2923 \| 0.4891 \|
	\| No log \| 11.0 \| 143 \| 1.3736 \| 0.295 \| 0.7950 \| 5.3584 \| 0.295 \| 0.1747 \| 0.2716 \| 0.6360 \|
	\| No log \| 12.0 \| 156 \| 1.2655 \| 0.425 \| 0.7380 \| 4.0312 \| 0.425 \| 0.2789 \| 0.3273 \| 0.3366 \|
	\| No log \| 13.0 \| 169 \| 1.1696 \| 0.475 \| 0.6901 \| 3.9627 \| 0.4750 \| 0.3083 \| 0.3011 \| 0.2825 \|
	\| No log \| 14.0 \| 182 \| 1.2992 \| 0.355 \| 0.7473 \| 3.9098 \| 0.3550 \| 0.2292 \| 0.2675 \| 0.4929 \|
	\| No log \| 15.0 \| 195 \| 1.1698 \| 0.51 \| 0.6881 \| 3.7143 \| 0.51 \| 0.3691 \| 0.3333 \| 0.3278 \|
	\| No log \| 16.0 \| 208 \| 1.0624 \| 0.515 \| 0.6274 \| 3.8387 \| 0.515 \| 0.3631 \| 0.2821 \| 0.2583 \|
	\| No log \| 17.0 \| 221 \| 1.0970 \| 0.565 \| 0.6421 \| 3.3302 \| 0.565 \| 0.4493 \| 0.3362 \| 0.2373 \|
	\| No log \| 18.0 \| 234 \| 1.0029 \| 0.625 \| 0.5883 \| 3.3820 \| 0.625 \| 0.4675 \| 0.3005 \| 0.1660 \|
	\| No log \| 19.0 \| 247 \| 1.0384 \| 0.605 \| 0.6093 \| 3.3183 \| 0.605 \| 0.4863 \| 0.3252 \| 0.2145 \|
	\| No log \| 20.0 \| 260 \| 1.0686 \| 0.62 \| 0.6234 \| 3.0246 \| 0.62 \| 0.5155 \| 0.3625 \| 0.2334 \|
	\| No log \| 21.0 \| 273 \| 0.9641 \| 0.62 \| 0.5685 \| 2.9225 \| 0.62 \| 0.5259 \| 0.3103 \| 0.2063 \|
	\| No log \| 22.0 \| 286 \| 1.0054 \| 0.665 \| 0.5849 \| 3.0792 \| 0.665 \| 0.5614 \| 0.3636 \| 0.1863 \|
	\| No log \| 23.0 \| 299 \| 0.9959 \| 0.675 \| 0.5734 \| 2.9829 \| 0.675 \| 0.5577 \| 0.3619 \| 0.1806 \|
	\| No log \| 24.0 \| 312 \| 0.9044 \| 0.675 \| 0.5267 \| 2.8952 \| 0.675 \| 0.5712 \| 0.2989 \| 0.1475 \|
	\| No log \| 25.0 \| 325 \| 0.9803 \| 0.655 \| 0.5627 \| 2.7501 \| 0.655 \| 0.5418 \| 0.3415 \| 0.1919 \|
	\| No log \| 26.0 \| 338 \| 0.8814 \| 0.65 \| 0.5176 \| 2.8421 \| 0.65 \| 0.5619 \| 0.2665 \| 0.1694 \|
	\| No log \| 27.0 \| 351 \| 0.8555 \| 0.69 \| 0.4928 \| 2.7870 \| 0.69 \| 0.5831 \| 0.3091 \| 0.1279 \|
	\| No log \| 28.0 \| 364 \| 0.8290 \| 0.69 \| 0.4777 \| 2.6377 \| 0.69 \| 0.5976 \| 0.2551 \| 0.1290 \|
	\| No log \| 29.0 \| 377 \| 0.8593 \| 0.685 \| 0.4949 \| 2.5880 \| 0.685 \| 0.5776 \| 0.3083 \| 0.1279 \|
	\| No log \| 30.0 \| 390 \| 0.8226 \| 0.685 \| 0.4678 \| 2.8938 \| 0.685 \| 0.5884 \| 0.2820 \| 0.1249 \|
	\| No log \| 31.0 \| 403 \| 0.8578 \| 0.69 \| 0.4857 \| 2.6150 \| 0.69 \| 0.6024 \| 0.3109 \| 0.1344 \|
	\| No log \| 32.0 \| 416 \| 0.8330 \| 0.685 \| 0.4753 \| 2.5999 \| 0.685 \| 0.6047 \| 0.2688 \| 0.1407 \|
	\| No log \| 33.0 \| 429 \| 0.8268 \| 0.7 \| 0.4683 \| 2.6138 \| 0.7 \| 0.6193 \| 0.2913 \| 0.1315 \|
	\| No log \| 34.0 \| 442 \| 0.8535 \| 0.715 \| 0.4749 \| 2.5059 \| 0.715 \| 0.6450 \| 0.2931 \| 0.1190 \|
	\| No log \| 35.0 \| 455 \| 0.8334 \| 0.665 \| 0.4752 \| 2.3839 \| 0.665 \| 0.5950 \| 0.2762 \| 0.1397 \|
	\| No log \| 36.0 \| 468 \| 0.8025 \| 0.71 \| 0.4553 \| 2.4803 \| 0.7100 \| 0.6302 \| 0.2889 \| 0.1178 \|
	\| No log \| 37.0 \| 481 \| 0.8142 \| 0.715 \| 0.4563 \| 2.6785 \| 0.715 \| 0.6426 \| 0.2989 \| 0.1048 \|
	\| No log \| 38.0 \| 494 \| 0.8124 \| 0.7 \| 0.4538 \| 2.5320 \| 0.7 \| 0.6332 \| 0.2594 \| 0.1132 \|
	\| 0.9303 \| 39.0 \| 507 \| 0.7888 \| 0.69 \| 0.4452 \| 2.6427 \| 0.69 \| 0.6269 \| 0.2583 \| 0.1224 \|
	\| 0.9303 \| 40.0 \| 520 \| 0.7907 \| 0.705 \| 0.4458 \| 2.6942 \| 0.705 \| 0.6367 \| 0.2688 \| 0.1155 \|
	\| 0.9303 \| 41.0 \| 533 \| 0.7918 \| 0.71 \| 0.4442 \| 2.4378 \| 0.7100 \| 0.6558 \| 0.2816 \| 0.1132 \|
	\| 0.9303 \| 42.0 \| 546 \| 0.8005 \| 0.725 \| 0.4479 \| 2.6088 \| 0.7250 \| 0.6576 \| 0.2914 \| 0.1049 \|
	\| 0.9303 \| 43.0 \| 559 \| 0.7879 \| 0.72 \| 0.4421 \| 2.7052 \| 0.72 \| 0.6592 \| 0.2741 \| 0.1122 \|
	\| 0.9303 \| 44.0 \| 572 \| 0.7910 \| 0.71 \| 0.4461 \| 2.6463 \| 0.7100 \| 0.6463 \| 0.3119 \| 0.1188 \|
	\| 0.9303 \| 45.0 \| 585 \| 0.7922 \| 0.705 \| 0.4450 \| 2.6453 \| 0.705 \| 0.6481 \| 0.2753 \| 0.1211 \|
	\| 0.9303 \| 46.0 \| 598 \| 0.7915 \| 0.715 \| 0.4429 \| 2.6970 \| 0.715 \| 0.6526 \| 0.2741 \| 0.1107 \|
	\| 0.9303 \| 47.0 \| 611 \| 0.7809 \| 0.705 \| 0.4370 \| 2.6841 \| 0.705 \| 0.6453 \| 0.2734 \| 0.1158 \|
	\| 0.9303 \| 48.0 \| 624 \| 0.7771 \| 0.705 \| 0.4350 \| 2.6168 \| 0.705 \| 0.6423 \| 0.2652 \| 0.1139 \|
	\| 0.9303 \| 49.0 \| 637 \| 0.7826 \| 0.705 \| 0.4377 \| 2.5091 \| 0.705 \| 0.6423 \| 0.2758 \| 0.1202 \|
	\| 0.9303 \| 50.0 \| 650 \| 0.7861 \| 0.705 \| 0.4410 \| 2.6519 \| 0.705 \| 0.6403 \| 0.2724 \| 0.1188 \|


	### Framework versions

	- Transformers 4.33.3
	- Pytorch 2.2.0.dev20231002
	- Datasets 2.7.1
	- Tokenizers 0.13.3