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	---
	license: apache-2.0
	base_model: microsoft/swinv2-tiny-patch4-window8-256
	tags:
	- generated_from_trainer
	metrics:
	- accuracy
	model-index:
	- name: swim2-base-model
	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. -->

	# swim2-base-model

	This model is a fine-tuned version of [microsoft/swinv2-tiny-patch4-window8-256](https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256) on an unknown dataset.
	It achieves the following results on the evaluation set:
	- Loss: 0.9308
	- Accuracy: 0.5227

	## 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: 5e-05
	- 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: 3

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \|
	\|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|
	\| 0.5804 \| 0.05 \| 100 \| 0.5395 \| 0.7388 \|
	\| 0.2244 \| 0.09 \| 200 \| 0.3057 \| 0.8787 \|
	\| 0.134 \| 0.14 \| 300 \| 0.2218 \| 0.9129 \|
	\| 0.1786 \| 0.18 \| 400 \| 0.1567 \| 0.9373 \|
	\| 0.0924 \| 0.23 \| 500 \| 0.1360 \| 0.9464 \|
	\| 0.1217 \| 0.27 \| 600 \| 0.1732 \| 0.9349 \|
	\| 0.144 \| 0.32 \| 700 \| 0.1233 \| 0.9538 \|
	\| 0.0917 \| 0.37 \| 800 \| 0.1655 \| 0.9379 \|
	\| 0.1005 \| 0.41 \| 900 \| 0.1047 \| 0.9632 \|
	\| 0.1391 \| 0.46 \| 1000 \| 0.1281 \| 0.9554 \|
	\| 0.0488 \| 0.5 \| 1100 \| 0.0965 \| 0.9688 \|
	\| 0.0587 \| 0.55 \| 1200 \| 0.1926 \| 0.9460 \|
	\| 0.0827 \| 0.59 \| 1300 \| 0.0982 \| 0.9630 \|
	\| 0.035 \| 0.64 \| 1400 \| 0.1011 \| 0.9676 \|
	\| 0.0529 \| 0.69 \| 1500 \| 0.0984 \| 0.9646 \|
	\| 0.0653 \| 0.73 \| 1600 \| 0.0877 \| 0.9666 \|
	\| 0.0749 \| 0.78 \| 1700 \| 0.1208 \| 0.9604 \|
	\| 0.0686 \| 0.82 \| 1800 \| 0.0742 \| 0.9719 \|
	\| 0.039 \| 0.87 \| 1900 \| 0.0829 \| 0.9717 \|
	\| 0.0607 \| 0.91 \| 2000 \| 0.0767 \| 0.9746 \|
	\| 0.0478 \| 0.96 \| 2100 \| 0.0789 \| 0.9725 \|
	\| 0.0408 \| 1.01 \| 2200 \| 0.0750 \| 0.9757 \|
	\| 0.0228 \| 1.05 \| 2300 \| 0.0707 \| 0.9773 \|
	\| 0.0431 \| 1.1 \| 2400 \| 0.0690 \| 0.9787 \|
	\| 0.0675 \| 1.14 \| 2500 \| 0.0712 \| 0.9773 \|
	\| 0.0624 \| 1.19 \| 2600 \| 0.1109 \| 0.9640 \|
	\| 0.0843 \| 1.23 \| 2700 \| 0.1077 \| 0.9692 \|
	\| 0.0328 \| 1.28 \| 2800 \| 0.0663 \| 0.9795 \|
	\| 0.0724 \| 1.33 \| 2900 \| 0.0811 \| 0.9766 \|
	\| 0.0385 \| 1.37 \| 3000 \| 0.0820 \| 0.9732 \|
	\| 0.0315 \| 1.42 \| 3100 \| 0.0711 \| 0.9788 \|
	\| 0.0367 \| 1.46 \| 3200 \| 0.0806 \| 0.9765 \|
	\| 0.0382 \| 1.51 \| 3300 \| 0.1444 \| 0.9612 \|
	\| 0.024 \| 1.55 \| 3400 \| 0.1038 \| 0.9738 \|
	\| 0.0331 \| 1.6 \| 3500 \| 0.1181 \| 0.9660 \|
	\| 0.0419 \| 1.65 \| 3600 \| 0.0687 \| 0.9790 \|
	\| 0.0352 \| 1.69 \| 3700 \| 0.0687 \| 0.9789 \|
	\| 0.0588 \| 1.74 \| 3800 \| 0.0620 \| 0.9804 \|
	\| 0.0313 \| 1.78 \| 3900 \| 0.0975 \| 0.9722 \|
	\| 0.0421 \| 1.83 \| 4000 \| 0.0588 \| 0.9803 \|
	\| 0.0182 \| 1.87 \| 4100 \| 0.0601 \| 0.9819 \|
	\| 0.0323 \| 1.92 \| 4200 \| 0.0593 \| 0.9819 \|
	\| 0.0565 \| 1.97 \| 4300 \| 0.0537 \| 0.9820 \|
	\| 0.0266 \| 2.01 \| 4400 \| 0.0693 \| 0.9804 \|
	\| 0.0374 \| 2.06 \| 4500 \| 0.0610 \| 0.9819 \|
	\| 0.0246 \| 2.1 \| 4600 \| 0.0580 \| 0.9822 \|
	\| 0.0316 \| 2.15 \| 4700 \| 0.0674 \| 0.9804 \|
	\| 0.0415 \| 2.19 \| 4800 \| 0.0569 \| 0.9826 \|
	\| 0.0361 \| 2.24 \| 4900 \| 0.0550 \| 0.9840 \|
	\| 0.0298 \| 2.29 \| 5000 \| 0.0575 \| 0.9830 \|
	\| 0.0275 \| 2.33 \| 5100 \| 0.0600 \| 0.9836 \|
	\| 0.0194 \| 2.38 \| 5200 \| 0.0678 \| 0.9825 \|
	\| 0.0279 \| 2.42 \| 5300 \| 0.0608 \| 0.9838 \|
	\| 0.0585 \| 2.47 \| 5400 \| 0.0548 \| 0.9840 \|
	\| 0.0272 \| 2.51 \| 5500 \| 0.0565 \| 0.9841 \|
	\| 0.027 \| 2.56 \| 5600 \| 0.0565 \| 0.9840 \|
	\| 0.0154 \| 2.61 \| 5700 \| 0.0671 \| 0.9818 \|
	\| 0.0315 \| 2.65 \| 5800 \| 0.0554 \| 0.9851 \|
	\| 0.0351 \| 2.7 \| 5900 \| 0.0638 \| 0.9832 \|
	\| 0.0216 \| 2.74 \| 6000 \| 0.0517 \| 0.9851 \|
	\| 0.0218 \| 2.79 \| 6100 \| 0.0574 \| 0.9844 \|
	\| 0.0324 \| 2.83 \| 6200 \| 0.0526 \| 0.9851 \|
	\| 0.0365 \| 2.88 \| 6300 \| 0.0546 \| 0.9852 \|
	\| 0.0333 \| 2.93 \| 6400 \| 0.0523 \| 0.9854 \|
	\| 0.0121 \| 2.97 \| 6500 \| 0.0570 \| 0.9845 \|


	### Framework versions

	- Transformers 4.33.0
	- Pytorch 2.0.0+cu117
	- Datasets 2.19.1
	- Tokenizers 0.13.3

	---
	license: apache-2.0
	base_model: microsoft/swinv2-tiny-patch4-window8-256
	tags:
	- generated_from_trainer
	metrics:
	- accuracy
	model-index:
	- name: swim2-base-model
	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. -->

	# swim2-base-model

	This model is a fine-tuned version of [microsoft/swinv2-tiny-patch4-window8-256](https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256) on an unknown dataset.
	It achieves the following results on the evaluation set:
	- Loss: 0.9308
	- Accuracy: 0.5227

	## 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: 5e-05
	- 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: 3

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \|
	\|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|
	\| 0.5804 \| 0.05 \| 100 \| 0.5395 \| 0.7388 \|
	\| 0.2244 \| 0.09 \| 200 \| 0.3057 \| 0.8787 \|
	\| 0.134 \| 0.14 \| 300 \| 0.2218 \| 0.9129 \|
	\| 0.1786 \| 0.18 \| 400 \| 0.1567 \| 0.9373 \|
	\| 0.0924 \| 0.23 \| 500 \| 0.1360 \| 0.9464 \|
	\| 0.1217 \| 0.27 \| 600 \| 0.1732 \| 0.9349 \|
	\| 0.144 \| 0.32 \| 700 \| 0.1233 \| 0.9538 \|
	\| 0.0917 \| 0.37 \| 800 \| 0.1655 \| 0.9379 \|
	\| 0.1005 \| 0.41 \| 900 \| 0.1047 \| 0.9632 \|
	\| 0.1391 \| 0.46 \| 1000 \| 0.1281 \| 0.9554 \|
	\| 0.0488 \| 0.5 \| 1100 \| 0.0965 \| 0.9688 \|
	\| 0.0587 \| 0.55 \| 1200 \| 0.1926 \| 0.9460 \|
	\| 0.0827 \| 0.59 \| 1300 \| 0.0982 \| 0.9630 \|
	\| 0.035 \| 0.64 \| 1400 \| 0.1011 \| 0.9676 \|
	\| 0.0529 \| 0.69 \| 1500 \| 0.0984 \| 0.9646 \|
	\| 0.0653 \| 0.73 \| 1600 \| 0.0877 \| 0.9666 \|
	\| 0.0749 \| 0.78 \| 1700 \| 0.1208 \| 0.9604 \|
	\| 0.0686 \| 0.82 \| 1800 \| 0.0742 \| 0.9719 \|
	\| 0.039 \| 0.87 \| 1900 \| 0.0829 \| 0.9717 \|
	\| 0.0607 \| 0.91 \| 2000 \| 0.0767 \| 0.9746 \|
	\| 0.0478 \| 0.96 \| 2100 \| 0.0789 \| 0.9725 \|
	\| 0.0408 \| 1.01 \| 2200 \| 0.0750 \| 0.9757 \|
	\| 0.0228 \| 1.05 \| 2300 \| 0.0707 \| 0.9773 \|
	\| 0.0431 \| 1.1 \| 2400 \| 0.0690 \| 0.9787 \|
	\| 0.0675 \| 1.14 \| 2500 \| 0.0712 \| 0.9773 \|
	\| 0.0624 \| 1.19 \| 2600 \| 0.1109 \| 0.9640 \|
	\| 0.0843 \| 1.23 \| 2700 \| 0.1077 \| 0.9692 \|
	\| 0.0328 \| 1.28 \| 2800 \| 0.0663 \| 0.9795 \|
	\| 0.0724 \| 1.33 \| 2900 \| 0.0811 \| 0.9766 \|
	\| 0.0385 \| 1.37 \| 3000 \| 0.0820 \| 0.9732 \|
	\| 0.0315 \| 1.42 \| 3100 \| 0.0711 \| 0.9788 \|
	\| 0.0367 \| 1.46 \| 3200 \| 0.0806 \| 0.9765 \|
	\| 0.0382 \| 1.51 \| 3300 \| 0.1444 \| 0.9612 \|
	\| 0.024 \| 1.55 \| 3400 \| 0.1038 \| 0.9738 \|
	\| 0.0331 \| 1.6 \| 3500 \| 0.1181 \| 0.9660 \|
	\| 0.0419 \| 1.65 \| 3600 \| 0.0687 \| 0.9790 \|
	\| 0.0352 \| 1.69 \| 3700 \| 0.0687 \| 0.9789 \|
	\| 0.0588 \| 1.74 \| 3800 \| 0.0620 \| 0.9804 \|
	\| 0.0313 \| 1.78 \| 3900 \| 0.0975 \| 0.9722 \|
	\| 0.0421 \| 1.83 \| 4000 \| 0.0588 \| 0.9803 \|
	\| 0.0182 \| 1.87 \| 4100 \| 0.0601 \| 0.9819 \|
	\| 0.0323 \| 1.92 \| 4200 \| 0.0593 \| 0.9819 \|
	\| 0.0565 \| 1.97 \| 4300 \| 0.0537 \| 0.9820 \|
	\| 0.0266 \| 2.01 \| 4400 \| 0.0693 \| 0.9804 \|
	\| 0.0374 \| 2.06 \| 4500 \| 0.0610 \| 0.9819 \|
	\| 0.0246 \| 2.1 \| 4600 \| 0.0580 \| 0.9822 \|
	\| 0.0316 \| 2.15 \| 4700 \| 0.0674 \| 0.9804 \|
	\| 0.0415 \| 2.19 \| 4800 \| 0.0569 \| 0.9826 \|
	\| 0.0361 \| 2.24 \| 4900 \| 0.0550 \| 0.9840 \|
	\| 0.0298 \| 2.29 \| 5000 \| 0.0575 \| 0.9830 \|
	\| 0.0275 \| 2.33 \| 5100 \| 0.0600 \| 0.9836 \|
	\| 0.0194 \| 2.38 \| 5200 \| 0.0678 \| 0.9825 \|
	\| 0.0279 \| 2.42 \| 5300 \| 0.0608 \| 0.9838 \|
	\| 0.0585 \| 2.47 \| 5400 \| 0.0548 \| 0.9840 \|
	\| 0.0272 \| 2.51 \| 5500 \| 0.0565 \| 0.9841 \|
	\| 0.027 \| 2.56 \| 5600 \| 0.0565 \| 0.9840 \|
	\| 0.0154 \| 2.61 \| 5700 \| 0.0671 \| 0.9818 \|
	\| 0.0315 \| 2.65 \| 5800 \| 0.0554 \| 0.9851 \|
	\| 0.0351 \| 2.7 \| 5900 \| 0.0638 \| 0.9832 \|
	\| 0.0216 \| 2.74 \| 6000 \| 0.0517 \| 0.9851 \|
	\| 0.0218 \| 2.79 \| 6100 \| 0.0574 \| 0.9844 \|
	\| 0.0324 \| 2.83 \| 6200 \| 0.0526 \| 0.9851 \|
	\| 0.0365 \| 2.88 \| 6300 \| 0.0546 \| 0.9852 \|
	\| 0.0333 \| 2.93 \| 6400 \| 0.0523 \| 0.9854 \|
	\| 0.0121 \| 2.97 \| 6500 \| 0.0570 \| 0.9845 \|


	### Framework versions

	- Transformers 4.33.0
	- Pytorch 2.0.0+cu117
	- Datasets 2.19.1
	- Tokenizers 0.13.3