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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_a0.9
	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_a0.9

	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: 0.8809
	- Accuracy: 0.7
	- Brier Loss: 0.4126
	- Nll: 2.4279
	- F1 Micro: 0.7
	- F1 Macro: 0.6279
	- Ece: 0.2569
	- Aurc: 0.1111

	## 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 \| 2.1185 \| 0.165 \| 0.8967 \| 8.5399 \| 0.165 \| 0.1130 \| 0.2151 \| 0.8331 \|
	\| No log \| 2.0 \| 26 \| 2.1127 \| 0.13 \| 0.8958 \| 8.1152 \| 0.13 \| 0.0842 \| 0.1816 \| 0.8392 \|
	\| No log \| 3.0 \| 39 \| 2.0781 \| 0.165 \| 0.8888 \| 6.8828 \| 0.165 \| 0.0878 \| 0.2150 \| 0.8082 \|
	\| No log \| 4.0 \| 52 \| 2.0197 \| 0.22 \| 0.8762 \| 5.7578 \| 0.22 \| 0.1155 \| 0.2521 \| 0.7521 \|
	\| No log \| 5.0 \| 65 \| 1.9499 \| 0.205 \| 0.8601 \| 6.0641 \| 0.205 \| 0.0951 \| 0.2567 \| 0.7355 \|
	\| No log \| 6.0 \| 78 \| 1.9019 \| 0.25 \| 0.8483 \| 5.8930 \| 0.25 \| 0.1178 \| 0.2728 \| 0.6862 \|
	\| No log \| 7.0 \| 91 \| 1.8252 \| 0.28 \| 0.8301 \| 5.8062 \| 0.28 \| 0.1660 \| 0.2890 \| 0.6982 \|
	\| No log \| 8.0 \| 104 \| 1.8194 \| 0.28 \| 0.8275 \| 5.2642 \| 0.28 \| 0.1625 \| 0.2874 \| 0.6935 \|
	\| No log \| 9.0 \| 117 \| 1.7671 \| 0.355 \| 0.8109 \| 5.1326 \| 0.3550 \| 0.2211 \| 0.3018 \| 0.5678 \|
	\| No log \| 10.0 \| 130 \| 1.6582 \| 0.355 \| 0.7774 \| 5.2226 \| 0.3550 \| 0.2200 \| 0.2991 \| 0.5305 \|
	\| No log \| 11.0 \| 143 \| 1.5849 \| 0.395 \| 0.7422 \| 5.0239 \| 0.395 \| 0.2436 \| 0.2979 \| 0.3974 \|
	\| No log \| 12.0 \| 156 \| 1.4908 \| 0.46 \| 0.7001 \| 4.2790 \| 0.46 \| 0.3169 \| 0.3091 \| 0.3003 \|
	\| No log \| 13.0 \| 169 \| 1.6016 \| 0.395 \| 0.7496 \| 4.2149 \| 0.395 \| 0.2793 \| 0.2929 \| 0.4640 \|
	\| No log \| 14.0 \| 182 \| 1.4714 \| 0.475 \| 0.6971 \| 4.0742 \| 0.4750 \| 0.3299 \| 0.3177 \| 0.3613 \|
	\| No log \| 15.0 \| 195 \| 1.5007 \| 0.46 \| 0.7119 \| 3.8252 \| 0.46 \| 0.3145 \| 0.3111 \| 0.3954 \|
	\| No log \| 16.0 \| 208 \| 1.4352 \| 0.515 \| 0.6776 \| 3.4028 \| 0.515 \| 0.3948 \| 0.3376 \| 0.2993 \|
	\| No log \| 17.0 \| 221 \| 1.2890 \| 0.575 \| 0.6104 \| 3.4453 \| 0.575 \| 0.4478 \| 0.2940 \| 0.2119 \|
	\| No log \| 18.0 \| 234 \| 1.2190 \| 0.595 \| 0.5719 \| 3.2413 \| 0.595 \| 0.4662 \| 0.2608 \| 0.1981 \|
	\| No log \| 19.0 \| 247 \| 1.2287 \| 0.59 \| 0.5764 \| 3.2303 \| 0.59 \| 0.4857 \| 0.2811 \| 0.2020 \|
	\| No log \| 20.0 \| 260 \| 1.1726 \| 0.64 \| 0.5494 \| 2.9544 \| 0.64 \| 0.5307 \| 0.2993 \| 0.1708 \|
	\| No log \| 21.0 \| 273 \| 1.1305 \| 0.61 \| 0.5384 \| 2.9557 \| 0.61 \| 0.5170 \| 0.2771 \| 0.1949 \|
	\| No log \| 22.0 \| 286 \| 1.1256 \| 0.645 \| 0.5295 \| 2.7934 \| 0.645 \| 0.5381 \| 0.3181 \| 0.1629 \|
	\| No log \| 23.0 \| 299 \| 1.1209 \| 0.645 \| 0.5217 \| 2.8697 \| 0.645 \| 0.5432 \| 0.3055 \| 0.1687 \|
	\| No log \| 24.0 \| 312 \| 1.2513 \| 0.685 \| 0.5917 \| 2.7262 \| 0.685 \| 0.5639 \| 0.3779 \| 0.1833 \|
	\| No log \| 25.0 \| 325 \| 1.0321 \| 0.695 \| 0.4819 \| 2.7202 \| 0.695 \| 0.5896 \| 0.2810 \| 0.1280 \|
	\| No log \| 26.0 \| 338 \| 1.0405 \| 0.645 \| 0.4957 \| 2.6116 \| 0.645 \| 0.5661 \| 0.2515 \| 0.1700 \|
	\| No log \| 27.0 \| 351 \| 1.0580 \| 0.695 \| 0.4933 \| 2.7436 \| 0.695 \| 0.5996 \| 0.2967 \| 0.1339 \|
	\| No log \| 28.0 \| 364 \| 0.9740 \| 0.65 \| 0.4575 \| 2.5682 \| 0.65 \| 0.5731 \| 0.2513 \| 0.1384 \|
	\| No log \| 29.0 \| 377 \| 0.9934 \| 0.695 \| 0.4651 \| 2.5753 \| 0.695 \| 0.6108 \| 0.2775 \| 0.1171 \|
	\| No log \| 30.0 \| 390 \| 0.9900 \| 0.645 \| 0.4695 \| 2.6280 \| 0.645 \| 0.5668 \| 0.2459 \| 0.1558 \|
	\| No log \| 31.0 \| 403 \| 0.9671 \| 0.695 \| 0.4504 \| 2.8174 \| 0.695 \| 0.6094 \| 0.2505 \| 0.1188 \|
	\| No log \| 32.0 \| 416 \| 0.9327 \| 0.715 \| 0.4324 \| 2.5285 \| 0.715 \| 0.6415 \| 0.2565 \| 0.1086 \|
	\| No log \| 33.0 \| 429 \| 0.9628 \| 0.71 \| 0.4464 \| 2.5876 \| 0.7100 \| 0.6435 \| 0.2709 \| 0.1152 \|
	\| No log \| 34.0 \| 442 \| 0.9316 \| 0.715 \| 0.4353 \| 2.7111 \| 0.715 \| 0.6334 \| 0.2361 \| 0.1078 \|
	\| No log \| 35.0 \| 455 \| 0.9275 \| 0.7 \| 0.4364 \| 2.5226 \| 0.7 \| 0.6251 \| 0.2586 \| 0.1207 \|
	\| No log \| 36.0 \| 468 \| 0.9301 \| 0.7 \| 0.4346 \| 2.6464 \| 0.7 \| 0.6232 \| 0.2482 \| 0.1142 \|
	\| No log \| 37.0 \| 481 \| 0.9013 \| 0.695 \| 0.4194 \| 2.5575 \| 0.695 \| 0.6197 \| 0.2554 \| 0.1098 \|
	\| No log \| 38.0 \| 494 \| 0.9008 \| 0.695 \| 0.4196 \| 2.6270 \| 0.695 \| 0.6156 \| 0.2246 \| 0.1063 \|
	\| 1.0903 \| 39.0 \| 507 \| 0.9185 \| 0.71 \| 0.4311 \| 2.6290 \| 0.7100 \| 0.6362 \| 0.2626 \| 0.1165 \|
	\| 1.0903 \| 40.0 \| 520 \| 0.9053 \| 0.685 \| 0.4254 \| 2.5057 \| 0.685 \| 0.6239 \| 0.2210 \| 0.1171 \|
	\| 1.0903 \| 41.0 \| 533 \| 0.8955 \| 0.7 \| 0.4189 \| 2.4823 \| 0.7 \| 0.6291 \| 0.1995 \| 0.1103 \|
	\| 1.0903 \| 42.0 \| 546 \| 0.9012 \| 0.69 \| 0.4223 \| 2.5377 \| 0.69 \| 0.6195 \| 0.2486 \| 0.1119 \|
	\| 1.0903 \| 43.0 \| 559 \| 0.8894 \| 0.71 \| 0.4138 \| 2.6167 \| 0.7100 \| 0.6382 \| 0.2459 \| 0.1022 \|
	\| 1.0903 \| 44.0 \| 572 \| 0.8846 \| 0.695 \| 0.4132 \| 2.5130 \| 0.695 \| 0.6265 \| 0.2198 \| 0.1093 \|
	\| 1.0903 \| 45.0 \| 585 \| 0.8946 \| 0.69 \| 0.4190 \| 2.6357 \| 0.69 \| 0.6230 \| 0.2375 \| 0.1145 \|
	\| 1.0903 \| 46.0 \| 598 \| 0.8931 \| 0.705 \| 0.4168 \| 2.6306 \| 0.705 \| 0.6342 \| 0.2555 \| 0.1102 \|
	\| 1.0903 \| 47.0 \| 611 \| 0.8842 \| 0.71 \| 0.4160 \| 2.3021 \| 0.7100 \| 0.6347 \| 0.2096 \| 0.1120 \|
	\| 1.0903 \| 48.0 \| 624 \| 0.8805 \| 0.695 \| 0.4140 \| 2.3447 \| 0.695 \| 0.6237 \| 0.2181 \| 0.1128 \|
	\| 1.0903 \| 49.0 \| 637 \| 0.8816 \| 0.7 \| 0.4142 \| 2.4358 \| 0.7 \| 0.6295 \| 0.2550 \| 0.1112 \|
	\| 1.0903 \| 50.0 \| 650 \| 0.8809 \| 0.7 \| 0.4126 \| 2.4279 \| 0.7 \| 0.6279 \| 0.2569 \| 0.1111 \|


	### 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_a0.9
	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_a0.9

	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: 0.8809
	- Accuracy: 0.7
	- Brier Loss: 0.4126
	- Nll: 2.4279
	- F1 Micro: 0.7
	- F1 Macro: 0.6279
	- Ece: 0.2569
	- Aurc: 0.1111

	## 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 \| 2.1185 \| 0.165 \| 0.8967 \| 8.5399 \| 0.165 \| 0.1130 \| 0.2151 \| 0.8331 \|
	\| No log \| 2.0 \| 26 \| 2.1127 \| 0.13 \| 0.8958 \| 8.1152 \| 0.13 \| 0.0842 \| 0.1816 \| 0.8392 \|
	\| No log \| 3.0 \| 39 \| 2.0781 \| 0.165 \| 0.8888 \| 6.8828 \| 0.165 \| 0.0878 \| 0.2150 \| 0.8082 \|
	\| No log \| 4.0 \| 52 \| 2.0197 \| 0.22 \| 0.8762 \| 5.7578 \| 0.22 \| 0.1155 \| 0.2521 \| 0.7521 \|
	\| No log \| 5.0 \| 65 \| 1.9499 \| 0.205 \| 0.8601 \| 6.0641 \| 0.205 \| 0.0951 \| 0.2567 \| 0.7355 \|
	\| No log \| 6.0 \| 78 \| 1.9019 \| 0.25 \| 0.8483 \| 5.8930 \| 0.25 \| 0.1178 \| 0.2728 \| 0.6862 \|
	\| No log \| 7.0 \| 91 \| 1.8252 \| 0.28 \| 0.8301 \| 5.8062 \| 0.28 \| 0.1660 \| 0.2890 \| 0.6982 \|
	\| No log \| 8.0 \| 104 \| 1.8194 \| 0.28 \| 0.8275 \| 5.2642 \| 0.28 \| 0.1625 \| 0.2874 \| 0.6935 \|
	\| No log \| 9.0 \| 117 \| 1.7671 \| 0.355 \| 0.8109 \| 5.1326 \| 0.3550 \| 0.2211 \| 0.3018 \| 0.5678 \|
	\| No log \| 10.0 \| 130 \| 1.6582 \| 0.355 \| 0.7774 \| 5.2226 \| 0.3550 \| 0.2200 \| 0.2991 \| 0.5305 \|
	\| No log \| 11.0 \| 143 \| 1.5849 \| 0.395 \| 0.7422 \| 5.0239 \| 0.395 \| 0.2436 \| 0.2979 \| 0.3974 \|
	\| No log \| 12.0 \| 156 \| 1.4908 \| 0.46 \| 0.7001 \| 4.2790 \| 0.46 \| 0.3169 \| 0.3091 \| 0.3003 \|
	\| No log \| 13.0 \| 169 \| 1.6016 \| 0.395 \| 0.7496 \| 4.2149 \| 0.395 \| 0.2793 \| 0.2929 \| 0.4640 \|
	\| No log \| 14.0 \| 182 \| 1.4714 \| 0.475 \| 0.6971 \| 4.0742 \| 0.4750 \| 0.3299 \| 0.3177 \| 0.3613 \|
	\| No log \| 15.0 \| 195 \| 1.5007 \| 0.46 \| 0.7119 \| 3.8252 \| 0.46 \| 0.3145 \| 0.3111 \| 0.3954 \|
	\| No log \| 16.0 \| 208 \| 1.4352 \| 0.515 \| 0.6776 \| 3.4028 \| 0.515 \| 0.3948 \| 0.3376 \| 0.2993 \|
	\| No log \| 17.0 \| 221 \| 1.2890 \| 0.575 \| 0.6104 \| 3.4453 \| 0.575 \| 0.4478 \| 0.2940 \| 0.2119 \|
	\| No log \| 18.0 \| 234 \| 1.2190 \| 0.595 \| 0.5719 \| 3.2413 \| 0.595 \| 0.4662 \| 0.2608 \| 0.1981 \|
	\| No log \| 19.0 \| 247 \| 1.2287 \| 0.59 \| 0.5764 \| 3.2303 \| 0.59 \| 0.4857 \| 0.2811 \| 0.2020 \|
	\| No log \| 20.0 \| 260 \| 1.1726 \| 0.64 \| 0.5494 \| 2.9544 \| 0.64 \| 0.5307 \| 0.2993 \| 0.1708 \|
	\| No log \| 21.0 \| 273 \| 1.1305 \| 0.61 \| 0.5384 \| 2.9557 \| 0.61 \| 0.5170 \| 0.2771 \| 0.1949 \|
	\| No log \| 22.0 \| 286 \| 1.1256 \| 0.645 \| 0.5295 \| 2.7934 \| 0.645 \| 0.5381 \| 0.3181 \| 0.1629 \|
	\| No log \| 23.0 \| 299 \| 1.1209 \| 0.645 \| 0.5217 \| 2.8697 \| 0.645 \| 0.5432 \| 0.3055 \| 0.1687 \|
	\| No log \| 24.0 \| 312 \| 1.2513 \| 0.685 \| 0.5917 \| 2.7262 \| 0.685 \| 0.5639 \| 0.3779 \| 0.1833 \|
	\| No log \| 25.0 \| 325 \| 1.0321 \| 0.695 \| 0.4819 \| 2.7202 \| 0.695 \| 0.5896 \| 0.2810 \| 0.1280 \|
	\| No log \| 26.0 \| 338 \| 1.0405 \| 0.645 \| 0.4957 \| 2.6116 \| 0.645 \| 0.5661 \| 0.2515 \| 0.1700 \|
	\| No log \| 27.0 \| 351 \| 1.0580 \| 0.695 \| 0.4933 \| 2.7436 \| 0.695 \| 0.5996 \| 0.2967 \| 0.1339 \|
	\| No log \| 28.0 \| 364 \| 0.9740 \| 0.65 \| 0.4575 \| 2.5682 \| 0.65 \| 0.5731 \| 0.2513 \| 0.1384 \|
	\| No log \| 29.0 \| 377 \| 0.9934 \| 0.695 \| 0.4651 \| 2.5753 \| 0.695 \| 0.6108 \| 0.2775 \| 0.1171 \|
	\| No log \| 30.0 \| 390 \| 0.9900 \| 0.645 \| 0.4695 \| 2.6280 \| 0.645 \| 0.5668 \| 0.2459 \| 0.1558 \|
	\| No log \| 31.0 \| 403 \| 0.9671 \| 0.695 \| 0.4504 \| 2.8174 \| 0.695 \| 0.6094 \| 0.2505 \| 0.1188 \|
	\| No log \| 32.0 \| 416 \| 0.9327 \| 0.715 \| 0.4324 \| 2.5285 \| 0.715 \| 0.6415 \| 0.2565 \| 0.1086 \|
	\| No log \| 33.0 \| 429 \| 0.9628 \| 0.71 \| 0.4464 \| 2.5876 \| 0.7100 \| 0.6435 \| 0.2709 \| 0.1152 \|
	\| No log \| 34.0 \| 442 \| 0.9316 \| 0.715 \| 0.4353 \| 2.7111 \| 0.715 \| 0.6334 \| 0.2361 \| 0.1078 \|
	\| No log \| 35.0 \| 455 \| 0.9275 \| 0.7 \| 0.4364 \| 2.5226 \| 0.7 \| 0.6251 \| 0.2586 \| 0.1207 \|
	\| No log \| 36.0 \| 468 \| 0.9301 \| 0.7 \| 0.4346 \| 2.6464 \| 0.7 \| 0.6232 \| 0.2482 \| 0.1142 \|
	\| No log \| 37.0 \| 481 \| 0.9013 \| 0.695 \| 0.4194 \| 2.5575 \| 0.695 \| 0.6197 \| 0.2554 \| 0.1098 \|
	\| No log \| 38.0 \| 494 \| 0.9008 \| 0.695 \| 0.4196 \| 2.6270 \| 0.695 \| 0.6156 \| 0.2246 \| 0.1063 \|
	\| 1.0903 \| 39.0 \| 507 \| 0.9185 \| 0.71 \| 0.4311 \| 2.6290 \| 0.7100 \| 0.6362 \| 0.2626 \| 0.1165 \|
	\| 1.0903 \| 40.0 \| 520 \| 0.9053 \| 0.685 \| 0.4254 \| 2.5057 \| 0.685 \| 0.6239 \| 0.2210 \| 0.1171 \|
	\| 1.0903 \| 41.0 \| 533 \| 0.8955 \| 0.7 \| 0.4189 \| 2.4823 \| 0.7 \| 0.6291 \| 0.1995 \| 0.1103 \|
	\| 1.0903 \| 42.0 \| 546 \| 0.9012 \| 0.69 \| 0.4223 \| 2.5377 \| 0.69 \| 0.6195 \| 0.2486 \| 0.1119 \|
	\| 1.0903 \| 43.0 \| 559 \| 0.8894 \| 0.71 \| 0.4138 \| 2.6167 \| 0.7100 \| 0.6382 \| 0.2459 \| 0.1022 \|
	\| 1.0903 \| 44.0 \| 572 \| 0.8846 \| 0.695 \| 0.4132 \| 2.5130 \| 0.695 \| 0.6265 \| 0.2198 \| 0.1093 \|
	\| 1.0903 \| 45.0 \| 585 \| 0.8946 \| 0.69 \| 0.4190 \| 2.6357 \| 0.69 \| 0.6230 \| 0.2375 \| 0.1145 \|
	\| 1.0903 \| 46.0 \| 598 \| 0.8931 \| 0.705 \| 0.4168 \| 2.6306 \| 0.705 \| 0.6342 \| 0.2555 \| 0.1102 \|
	\| 1.0903 \| 47.0 \| 611 \| 0.8842 \| 0.71 \| 0.4160 \| 2.3021 \| 0.7100 \| 0.6347 \| 0.2096 \| 0.1120 \|
	\| 1.0903 \| 48.0 \| 624 \| 0.8805 \| 0.695 \| 0.4140 \| 2.3447 \| 0.695 \| 0.6237 \| 0.2181 \| 0.1128 \|
	\| 1.0903 \| 49.0 \| 637 \| 0.8816 \| 0.7 \| 0.4142 \| 2.4358 \| 0.7 \| 0.6295 \| 0.2550 \| 0.1112 \|
	\| 1.0903 \| 50.0 \| 650 \| 0.8809 \| 0.7 \| 0.4126 \| 2.4279 \| 0.7 \| 0.6279 \| 0.2569 \| 0.1111 \|


	### Framework versions

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