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swin-base-patch4-window7-224-in22k-finetuned-lora-ISIC-2019

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trantridat/SwinBase_lora r64 alpha16 dropout0.05 batchsize64 lr0.001

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	---
	license: apache-2.0
	base_model: microsoft/swin-base-patch4-window7-224-in22k
	tags:
	- generated_from_trainer
	datasets:
	- imagefolder
	metrics:
	- accuracy
	model-index:
	- name: swin-base-patch4-window7-224-in22k-finetuned-lora-ISIC-2019
	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. -->

	# swin-base-patch4-window7-224-in22k-finetuned-lora-ISIC-2019

	This model is a fine-tuned version of [microsoft/swin-base-patch4-window7-224-in22k](https://huggingface.co/microsoft/swin-base-patch4-window7-224-in22k) on the imagefolder dataset.
	It achieves the following results on the evaluation set:
	- Loss: 0.4229
	- Accuracy: 0.9008

	## 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: 0.001
	- train_batch_size: 64
	- eval_batch_size: 64
	- seed: 42
	- gradient_accumulation_steps: 4
	- total_train_batch_size: 256
	- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
	- lr_scheduler_type: linear
	- num_epochs: 100

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \|
	\|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|
	\| 0.858 \| 0.99 \| 62 \| 0.7349 \| 0.7339 \|
	\| 0.7403 \| 2.0 \| 125 \| 0.6364 \| 0.7762 \|
	\| 0.675 \| 2.99 \| 187 \| 0.5777 \| 0.7999 \|
	\| 0.6309 \| 4.0 \| 250 \| 0.5701 \| 0.7875 \|
	\| 0.5734 \| 4.99 \| 312 \| 0.5294 \| 0.8016 \|
	\| 0.5338 \| 6.0 \| 375 \| 0.5418 \| 0.8010 \|
	\| 0.5104 \| 6.99 \| 437 \| 0.5057 \| 0.8179 \|
	\| 0.5091 \| 8.0 \| 500 \| 0.5010 \| 0.8207 \|
	\| 0.4678 \| 8.99 \| 562 \| 0.4757 \| 0.8247 \|
	\| 0.467 \| 10.0 \| 625 \| 0.4579 \| 0.8151 \|
	\| 0.4416 \| 10.99 \| 687 \| 0.4650 \| 0.8315 \|
	\| 0.4277 \| 12.0 \| 750 \| 0.4405 \| 0.8405 \|
	\| 0.4261 \| 12.99 \| 812 \| 0.4414 \| 0.8388 \|
	\| 0.4016 \| 14.0 \| 875 \| 0.4392 \| 0.8286 \|
	\| 0.3729 \| 14.99 \| 937 \| 0.4471 \| 0.8281 \|
	\| 0.3813 \| 16.0 \| 1000 \| 0.4155 \| 0.8433 \|
	\| 0.3454 \| 16.99 \| 1062 \| 0.4322 \| 0.8365 \|
	\| 0.3639 \| 18.0 \| 1125 \| 0.4332 \| 0.8360 \|
	\| 0.3393 \| 18.99 \| 1187 \| 0.4190 \| 0.8523 \|
	\| 0.3135 \| 20.0 \| 1250 \| 0.4166 \| 0.8534 \|
	\| 0.3094 \| 20.99 \| 1312 \| 0.4005 \| 0.8563 \|
	\| 0.3263 \| 22.0 \| 1375 \| 0.4399 \| 0.8495 \|
	\| 0.3009 \| 22.99 \| 1437 \| 0.4122 \| 0.8523 \|
	\| 0.2804 \| 24.0 \| 1500 \| 0.4293 \| 0.8563 \|
	\| 0.2516 \| 24.99 \| 1562 \| 0.4289 \| 0.8563 \|
	\| 0.2763 \| 26.0 \| 1625 \| 0.4125 \| 0.8647 \|
	\| 0.2707 \| 26.99 \| 1687 \| 0.4231 \| 0.8664 \|
	\| 0.2585 \| 28.0 \| 1750 \| 0.4210 \| 0.8596 \|
	\| 0.2317 \| 28.99 \| 1812 \| 0.4296 \| 0.8602 \|
	\| 0.2118 \| 30.0 \| 1875 \| 0.4440 \| 0.8636 \|
	\| 0.2224 \| 30.99 \| 1937 \| 0.3928 \| 0.8726 \|
	\| 0.2166 \| 32.0 \| 2000 \| 0.4246 \| 0.8602 \|
	\| 0.2038 \| 32.99 \| 2062 \| 0.4146 \| 0.8709 \|
	\| 0.2183 \| 34.0 \| 2125 \| 0.4165 \| 0.8698 \|
	\| 0.22 \| 34.99 \| 2187 \| 0.4212 \| 0.8766 \|
	\| 0.206 \| 36.0 \| 2250 \| 0.4139 \| 0.8726 \|
	\| 0.199 \| 36.99 \| 2312 \| 0.3793 \| 0.8833 \|
	\| 0.1926 \| 38.0 \| 2375 \| 0.4127 \| 0.8839 \|
	\| 0.1648 \| 38.99 \| 2437 \| 0.4296 \| 0.8822 \|
	\| 0.1578 \| 40.0 \| 2500 \| 0.4132 \| 0.8833 \|
	\| 0.181 \| 40.99 \| 2562 \| 0.4217 \| 0.8777 \|
	\| 0.1735 \| 42.0 \| 2625 \| 0.4186 \| 0.8715 \|
	\| 0.1603 \| 42.99 \| 2687 \| 0.4117 \| 0.8805 \|
	\| 0.1516 \| 44.0 \| 2750 \| 0.4250 \| 0.8816 \|
	\| 0.1733 \| 44.99 \| 2812 \| 0.3914 \| 0.8844 \|
	\| 0.164 \| 46.0 \| 2875 \| 0.4369 \| 0.8828 \|
	\| 0.1519 \| 46.99 \| 2937 \| 0.4276 \| 0.8771 \|
	\| 0.1534 \| 48.0 \| 3000 \| 0.4421 \| 0.8822 \|
	\| 0.158 \| 48.99 \| 3062 \| 0.4240 \| 0.8873 \|
	\| 0.1531 \| 50.0 \| 3125 \| 0.4250 \| 0.8794 \|
	\| 0.1286 \| 50.99 \| 3187 \| 0.4228 \| 0.8732 \|
	\| 0.1396 \| 52.0 \| 3250 \| 0.4317 \| 0.8782 \|
	\| 0.1436 \| 52.99 \| 3312 \| 0.4361 \| 0.8856 \|
	\| 0.1411 \| 54.0 \| 3375 \| 0.4402 \| 0.8850 \|
	\| 0.1312 \| 54.99 \| 3437 \| 0.4327 \| 0.8884 \|
	\| 0.1359 \| 56.0 \| 3500 \| 0.4144 \| 0.8856 \|
	\| 0.1361 \| 56.99 \| 3562 \| 0.4181 \| 0.8867 \|
	\| 0.1272 \| 58.0 \| 3625 \| 0.4204 \| 0.8878 \|
	\| 0.1222 \| 58.99 \| 3687 \| 0.4137 \| 0.8884 \|
	\| 0.1272 \| 60.0 \| 3750 \| 0.4317 \| 0.8890 \|
	\| 0.1132 \| 60.99 \| 3812 \| 0.4351 \| 0.8918 \|
	\| 0.1239 \| 62.0 \| 3875 \| 0.4348 \| 0.8828 \|
	\| 0.1188 \| 62.99 \| 3937 \| 0.4258 \| 0.8861 \|
	\| 0.1203 \| 64.0 \| 4000 \| 0.4318 \| 0.8912 \|
	\| 0.1204 \| 64.99 \| 4062 \| 0.4055 \| 0.8952 \|
	\| 0.1053 \| 66.0 \| 4125 \| 0.4222 \| 0.8918 \|
	\| 0.1187 \| 66.99 \| 4187 \| 0.4248 \| 0.8946 \|
	\| 0.1129 \| 68.0 \| 4250 \| 0.4302 \| 0.8923 \|
	\| 0.1117 \| 68.99 \| 4312 \| 0.4149 \| 0.8968 \|
	\| 0.1194 \| 70.0 \| 4375 \| 0.4160 \| 0.8895 \|
	\| 0.1003 \| 70.99 \| 4437 \| 0.4256 \| 0.8946 \|
	\| 0.1088 \| 72.0 \| 4500 \| 0.4356 \| 0.8918 \|
	\| 0.11 \| 72.99 \| 4562 \| 0.4277 \| 0.8935 \|
	\| 0.1016 \| 74.0 \| 4625 \| 0.4095 \| 0.8952 \|
	\| 0.0906 \| 74.99 \| 4687 \| 0.4262 \| 0.8935 \|
	\| 0.0969 \| 76.0 \| 4750 \| 0.4057 \| 0.8940 \|
	\| 0.111 \| 76.99 \| 4812 \| 0.4099 \| 0.8997 \|
	\| 0.091 \| 78.0 \| 4875 \| 0.4232 \| 0.8963 \|
	\| 0.1013 \| 78.99 \| 4937 \| 0.4311 \| 0.8884 \|
	\| 0.119 \| 80.0 \| 5000 \| 0.4302 \| 0.8929 \|
	\| 0.0877 \| 80.99 \| 5062 \| 0.4369 \| 0.8923 \|
	\| 0.0926 \| 82.0 \| 5125 \| 0.4353 \| 0.8968 \|
	\| 0.0969 \| 82.99 \| 5187 \| 0.4336 \| 0.8952 \|
	\| 0.092 \| 84.0 \| 5250 \| 0.4214 \| 0.8935 \|
	\| 0.0914 \| 84.99 \| 5312 \| 0.4403 \| 0.8890 \|
	\| 0.0924 \| 86.0 \| 5375 \| 0.4285 \| 0.8929 \|
	\| 0.0964 \| 86.99 \| 5437 \| 0.4207 \| 0.8968 \|
	\| 0.0916 \| 88.0 \| 5500 \| 0.4254 \| 0.8946 \|
	\| 0.0962 \| 88.99 \| 5562 \| 0.4249 \| 0.8980 \|
	\| 0.0927 \| 90.0 \| 5625 \| 0.4242 \| 0.8935 \|
	\| 0.0993 \| 90.99 \| 5687 \| 0.4230 \| 0.8985 \|
	\| 0.0893 \| 92.0 \| 5750 \| 0.4229 \| 0.8980 \|
	\| 0.0878 \| 92.99 \| 5812 \| 0.4215 \| 0.8985 \|
	\| 0.0882 \| 94.0 \| 5875 \| 0.4262 \| 0.8980 \|
	\| 0.0854 \| 94.99 \| 5937 \| 0.4256 \| 0.8974 \|
	\| 0.0795 \| 96.0 \| 6000 \| 0.4229 \| 0.9008 \|
	\| 0.0931 \| 96.99 \| 6062 \| 0.4218 \| 0.8991 \|
	\| 0.0826 \| 98.0 \| 6125 \| 0.4235 \| 0.8985 \|
	\| 0.0926 \| 98.99 \| 6187 \| 0.4237 \| 0.8985 \|
	\| 0.0829 \| 99.2 \| 6200 \| 0.4238 \| 0.8985 \|


	### Framework versions

	- Transformers 4.32.1
	- Pytorch 2.0.1
	- Datasets 2.12.0
	- Tokenizers 0.13.2

	---
	license: apache-2.0
	base_model: microsoft/swin-base-patch4-window7-224-in22k
	tags:
	- generated_from_trainer
	datasets:
	- imagefolder
	metrics:
	- accuracy
	model-index:
	- name: swin-base-patch4-window7-224-in22k-finetuned-lora-ISIC-2019
	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. -->

	# swin-base-patch4-window7-224-in22k-finetuned-lora-ISIC-2019

	This model is a fine-tuned version of [microsoft/swin-base-patch4-window7-224-in22k](https://huggingface.co/microsoft/swin-base-patch4-window7-224-in22k) on the imagefolder dataset.
	It achieves the following results on the evaluation set:
	- Loss: 0.4229
	- Accuracy: 0.9008

	## 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: 0.001
	- train_batch_size: 64
	- eval_batch_size: 64
	- seed: 42
	- gradient_accumulation_steps: 4
	- total_train_batch_size: 256
	- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
	- lr_scheduler_type: linear
	- num_epochs: 100

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \|
	\|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|
	\| 0.858 \| 0.99 \| 62 \| 0.7349 \| 0.7339 \|
	\| 0.7403 \| 2.0 \| 125 \| 0.6364 \| 0.7762 \|
	\| 0.675 \| 2.99 \| 187 \| 0.5777 \| 0.7999 \|
	\| 0.6309 \| 4.0 \| 250 \| 0.5701 \| 0.7875 \|
	\| 0.5734 \| 4.99 \| 312 \| 0.5294 \| 0.8016 \|
	\| 0.5338 \| 6.0 \| 375 \| 0.5418 \| 0.8010 \|
	\| 0.5104 \| 6.99 \| 437 \| 0.5057 \| 0.8179 \|
	\| 0.5091 \| 8.0 \| 500 \| 0.5010 \| 0.8207 \|
	\| 0.4678 \| 8.99 \| 562 \| 0.4757 \| 0.8247 \|
	\| 0.467 \| 10.0 \| 625 \| 0.4579 \| 0.8151 \|
	\| 0.4416 \| 10.99 \| 687 \| 0.4650 \| 0.8315 \|
	\| 0.4277 \| 12.0 \| 750 \| 0.4405 \| 0.8405 \|
	\| 0.4261 \| 12.99 \| 812 \| 0.4414 \| 0.8388 \|
	\| 0.4016 \| 14.0 \| 875 \| 0.4392 \| 0.8286 \|
	\| 0.3729 \| 14.99 \| 937 \| 0.4471 \| 0.8281 \|
	\| 0.3813 \| 16.0 \| 1000 \| 0.4155 \| 0.8433 \|
	\| 0.3454 \| 16.99 \| 1062 \| 0.4322 \| 0.8365 \|
	\| 0.3639 \| 18.0 \| 1125 \| 0.4332 \| 0.8360 \|
	\| 0.3393 \| 18.99 \| 1187 \| 0.4190 \| 0.8523 \|
	\| 0.3135 \| 20.0 \| 1250 \| 0.4166 \| 0.8534 \|
	\| 0.3094 \| 20.99 \| 1312 \| 0.4005 \| 0.8563 \|
	\| 0.3263 \| 22.0 \| 1375 \| 0.4399 \| 0.8495 \|
	\| 0.3009 \| 22.99 \| 1437 \| 0.4122 \| 0.8523 \|
	\| 0.2804 \| 24.0 \| 1500 \| 0.4293 \| 0.8563 \|
	\| 0.2516 \| 24.99 \| 1562 \| 0.4289 \| 0.8563 \|
	\| 0.2763 \| 26.0 \| 1625 \| 0.4125 \| 0.8647 \|
	\| 0.2707 \| 26.99 \| 1687 \| 0.4231 \| 0.8664 \|
	\| 0.2585 \| 28.0 \| 1750 \| 0.4210 \| 0.8596 \|
	\| 0.2317 \| 28.99 \| 1812 \| 0.4296 \| 0.8602 \|
	\| 0.2118 \| 30.0 \| 1875 \| 0.4440 \| 0.8636 \|
	\| 0.2224 \| 30.99 \| 1937 \| 0.3928 \| 0.8726 \|
	\| 0.2166 \| 32.0 \| 2000 \| 0.4246 \| 0.8602 \|
	\| 0.2038 \| 32.99 \| 2062 \| 0.4146 \| 0.8709 \|
	\| 0.2183 \| 34.0 \| 2125 \| 0.4165 \| 0.8698 \|
	\| 0.22 \| 34.99 \| 2187 \| 0.4212 \| 0.8766 \|
	\| 0.206 \| 36.0 \| 2250 \| 0.4139 \| 0.8726 \|
	\| 0.199 \| 36.99 \| 2312 \| 0.3793 \| 0.8833 \|
	\| 0.1926 \| 38.0 \| 2375 \| 0.4127 \| 0.8839 \|
	\| 0.1648 \| 38.99 \| 2437 \| 0.4296 \| 0.8822 \|
	\| 0.1578 \| 40.0 \| 2500 \| 0.4132 \| 0.8833 \|
	\| 0.181 \| 40.99 \| 2562 \| 0.4217 \| 0.8777 \|
	\| 0.1735 \| 42.0 \| 2625 \| 0.4186 \| 0.8715 \|
	\| 0.1603 \| 42.99 \| 2687 \| 0.4117 \| 0.8805 \|
	\| 0.1516 \| 44.0 \| 2750 \| 0.4250 \| 0.8816 \|
	\| 0.1733 \| 44.99 \| 2812 \| 0.3914 \| 0.8844 \|
	\| 0.164 \| 46.0 \| 2875 \| 0.4369 \| 0.8828 \|
	\| 0.1519 \| 46.99 \| 2937 \| 0.4276 \| 0.8771 \|
	\| 0.1534 \| 48.0 \| 3000 \| 0.4421 \| 0.8822 \|
	\| 0.158 \| 48.99 \| 3062 \| 0.4240 \| 0.8873 \|
	\| 0.1531 \| 50.0 \| 3125 \| 0.4250 \| 0.8794 \|
	\| 0.1286 \| 50.99 \| 3187 \| 0.4228 \| 0.8732 \|
	\| 0.1396 \| 52.0 \| 3250 \| 0.4317 \| 0.8782 \|
	\| 0.1436 \| 52.99 \| 3312 \| 0.4361 \| 0.8856 \|
	\| 0.1411 \| 54.0 \| 3375 \| 0.4402 \| 0.8850 \|
	\| 0.1312 \| 54.99 \| 3437 \| 0.4327 \| 0.8884 \|
	\| 0.1359 \| 56.0 \| 3500 \| 0.4144 \| 0.8856 \|
	\| 0.1361 \| 56.99 \| 3562 \| 0.4181 \| 0.8867 \|
	\| 0.1272 \| 58.0 \| 3625 \| 0.4204 \| 0.8878 \|
	\| 0.1222 \| 58.99 \| 3687 \| 0.4137 \| 0.8884 \|
	\| 0.1272 \| 60.0 \| 3750 \| 0.4317 \| 0.8890 \|
	\| 0.1132 \| 60.99 \| 3812 \| 0.4351 \| 0.8918 \|
	\| 0.1239 \| 62.0 \| 3875 \| 0.4348 \| 0.8828 \|
	\| 0.1188 \| 62.99 \| 3937 \| 0.4258 \| 0.8861 \|
	\| 0.1203 \| 64.0 \| 4000 \| 0.4318 \| 0.8912 \|
	\| 0.1204 \| 64.99 \| 4062 \| 0.4055 \| 0.8952 \|
	\| 0.1053 \| 66.0 \| 4125 \| 0.4222 \| 0.8918 \|
	\| 0.1187 \| 66.99 \| 4187 \| 0.4248 \| 0.8946 \|
	\| 0.1129 \| 68.0 \| 4250 \| 0.4302 \| 0.8923 \|
	\| 0.1117 \| 68.99 \| 4312 \| 0.4149 \| 0.8968 \|
	\| 0.1194 \| 70.0 \| 4375 \| 0.4160 \| 0.8895 \|
	\| 0.1003 \| 70.99 \| 4437 \| 0.4256 \| 0.8946 \|
	\| 0.1088 \| 72.0 \| 4500 \| 0.4356 \| 0.8918 \|
	\| 0.11 \| 72.99 \| 4562 \| 0.4277 \| 0.8935 \|
	\| 0.1016 \| 74.0 \| 4625 \| 0.4095 \| 0.8952 \|
	\| 0.0906 \| 74.99 \| 4687 \| 0.4262 \| 0.8935 \|
	\| 0.0969 \| 76.0 \| 4750 \| 0.4057 \| 0.8940 \|
	\| 0.111 \| 76.99 \| 4812 \| 0.4099 \| 0.8997 \|
	\| 0.091 \| 78.0 \| 4875 \| 0.4232 \| 0.8963 \|
	\| 0.1013 \| 78.99 \| 4937 \| 0.4311 \| 0.8884 \|
	\| 0.119 \| 80.0 \| 5000 \| 0.4302 \| 0.8929 \|
	\| 0.0877 \| 80.99 \| 5062 \| 0.4369 \| 0.8923 \|
	\| 0.0926 \| 82.0 \| 5125 \| 0.4353 \| 0.8968 \|
	\| 0.0969 \| 82.99 \| 5187 \| 0.4336 \| 0.8952 \|
	\| 0.092 \| 84.0 \| 5250 \| 0.4214 \| 0.8935 \|
	\| 0.0914 \| 84.99 \| 5312 \| 0.4403 \| 0.8890 \|
	\| 0.0924 \| 86.0 \| 5375 \| 0.4285 \| 0.8929 \|
	\| 0.0964 \| 86.99 \| 5437 \| 0.4207 \| 0.8968 \|
	\| 0.0916 \| 88.0 \| 5500 \| 0.4254 \| 0.8946 \|
	\| 0.0962 \| 88.99 \| 5562 \| 0.4249 \| 0.8980 \|
	\| 0.0927 \| 90.0 \| 5625 \| 0.4242 \| 0.8935 \|
	\| 0.0993 \| 90.99 \| 5687 \| 0.4230 \| 0.8985 \|
	\| 0.0893 \| 92.0 \| 5750 \| 0.4229 \| 0.8980 \|
	\| 0.0878 \| 92.99 \| 5812 \| 0.4215 \| 0.8985 \|
	\| 0.0882 \| 94.0 \| 5875 \| 0.4262 \| 0.8980 \|
	\| 0.0854 \| 94.99 \| 5937 \| 0.4256 \| 0.8974 \|
	\| 0.0795 \| 96.0 \| 6000 \| 0.4229 \| 0.9008 \|
	\| 0.0931 \| 96.99 \| 6062 \| 0.4218 \| 0.8991 \|
	\| 0.0826 \| 98.0 \| 6125 \| 0.4235 \| 0.8985 \|
	\| 0.0926 \| 98.99 \| 6187 \| 0.4237 \| 0.8985 \|
	\| 0.0829 \| 99.2 \| 6200 \| 0.4238 \| 0.8985 \|


	### Framework versions

	- Transformers 4.32.1
	- Pytorch 2.0.1
	- Datasets 2.12.0
	- Tokenizers 0.13.2