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	---
	license: apache-2.0
	tags:
	- image-classification
	- generated_from_trainer
	metrics:
	- accuracy
	model-index:
	- name: exper6_mesum5
	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. -->

	# exper6_mesum5

	This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the sudo-s/herbier_mesuem5 dataset.
	It achieves the following results on the evaluation set:
	- Loss: 0.8241
	- Accuracy: 0.8036

	## 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: 0.0002
	- train_batch_size: 16
	- eval_batch_size: 8
	- seed: 42
	- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
	- lr_scheduler_type: linear
	- num_epochs: 16
	- mixed_precision_training: Native AMP

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \|
	\|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|
	\| 3.9276 \| 0.23 \| 100 \| 3.8550 \| 0.2089 \|
	\| 3.0853 \| 0.47 \| 200 \| 3.1106 \| 0.3414 \|
	\| 2.604 \| 0.7 \| 300 \| 2.5732 \| 0.4379 \|
	\| 2.3183 \| 0.93 \| 400 \| 2.2308 \| 0.4882 \|
	\| 1.5326 \| 1.16 \| 500 \| 1.7903 \| 0.5828 \|
	\| 1.3367 \| 1.4 \| 600 \| 1.5524 \| 0.6349 \|
	\| 1.1544 \| 1.63 \| 700 \| 1.3167 \| 0.6645 \|
	\| 1.0788 \| 1.86 \| 800 \| 1.3423 \| 0.6385 \|
	\| 0.6762 \| 2.09 \| 900 \| 1.0780 \| 0.7124 \|
	\| 0.6483 \| 2.33 \| 1000 \| 1.0090 \| 0.7284 \|
	\| 0.6321 \| 2.56 \| 1100 \| 1.0861 \| 0.7024 \|
	\| 0.5558 \| 2.79 \| 1200 \| 0.9933 \| 0.7183 \|
	\| 0.342 \| 3.02 \| 1300 \| 0.8871 \| 0.7462 \|
	\| 0.2964 \| 3.26 \| 1400 \| 0.9330 \| 0.7408 \|
	\| 0.1959 \| 3.49 \| 1500 \| 0.9367 \| 0.7343 \|
	\| 0.368 \| 3.72 \| 1600 \| 0.8472 \| 0.7550 \|
	\| 0.1821 \| 3.95 \| 1700 \| 0.8937 \| 0.7568 \|
	\| 0.1851 \| 4.19 \| 1800 \| 0.9546 \| 0.7485 \|
	\| 0.1648 \| 4.42 \| 1900 \| 0.9790 \| 0.7355 \|
	\| 0.172 \| 4.65 \| 2000 \| 0.8947 \| 0.7627 \|
	\| 0.0928 \| 4.88 \| 2100 \| 1.0093 \| 0.7462 \|
	\| 0.0699 \| 5.12 \| 2200 \| 0.8374 \| 0.7639 \|
	\| 0.0988 \| 5.35 \| 2300 \| 0.9189 \| 0.7645 \|
	\| 0.0822 \| 5.58 \| 2400 \| 0.9512 \| 0.7580 \|
	\| 0.1223 \| 5.81 \| 2500 \| 1.0809 \| 0.7349 \|
	\| 0.0509 \| 6.05 \| 2600 \| 0.9297 \| 0.7769 \|
	\| 0.0511 \| 6.28 \| 2700 \| 0.8981 \| 0.7822 \|
	\| 0.0596 \| 6.51 \| 2800 \| 0.9468 \| 0.7704 \|
	\| 0.0494 \| 6.74 \| 2900 \| 0.9045 \| 0.7870 \|
	\| 0.0643 \| 6.98 \| 3000 \| 1.1559 \| 0.7391 \|
	\| 0.0158 \| 7.21 \| 3100 \| 0.8450 \| 0.7899 \|
	\| 0.0129 \| 7.44 \| 3200 \| 0.8241 \| 0.8036 \|
	\| 0.0441 \| 7.67 \| 3300 \| 0.9679 \| 0.7751 \|
	\| 0.0697 \| 7.91 \| 3400 \| 1.0387 \| 0.7751 \|
	\| 0.0084 \| 8.14 \| 3500 \| 0.9441 \| 0.7947 \|
	\| 0.0182 \| 8.37 \| 3600 \| 0.8967 \| 0.7994 \|
	\| 0.0042 \| 8.6 \| 3700 \| 0.8750 \| 0.8041 \|
	\| 0.0028 \| 8.84 \| 3800 \| 0.9349 \| 0.8041 \|
	\| 0.0053 \| 9.07 \| 3900 \| 0.9403 \| 0.7982 \|
	\| 0.0266 \| 9.3 \| 4000 \| 0.9966 \| 0.7959 \|
	\| 0.0022 \| 9.53 \| 4100 \| 0.9472 \| 0.8018 \|
	\| 0.0018 \| 9.77 \| 4200 \| 0.8717 \| 0.8136 \|
	\| 0.0018 \| 10.0 \| 4300 \| 0.8964 \| 0.8083 \|
	\| 0.0046 \| 10.23 \| 4400 \| 0.8623 \| 0.8160 \|
	\| 0.0037 \| 10.47 \| 4500 \| 0.8762 \| 0.8172 \|
	\| 0.0013 \| 10.7 \| 4600 \| 0.9028 \| 0.8142 \|
	\| 0.0013 \| 10.93 \| 4700 \| 0.9084 \| 0.8178 \|
	\| 0.0013 \| 11.16 \| 4800 \| 0.8733 \| 0.8213 \|
	\| 0.001 \| 11.4 \| 4900 \| 0.8823 \| 0.8207 \|
	\| 0.0009 \| 11.63 \| 5000 \| 0.8769 \| 0.8213 \|
	\| 0.0282 \| 11.86 \| 5100 \| 0.8791 \| 0.8219 \|
	\| 0.001 \| 12.09 \| 5200 \| 0.8673 \| 0.8249 \|
	\| 0.0016 \| 12.33 \| 5300 \| 0.8633 \| 0.8225 \|
	\| 0.0008 \| 12.56 \| 5400 \| 0.8766 \| 0.8195 \|
	\| 0.0008 \| 12.79 \| 5500 \| 0.8743 \| 0.8225 \|
	\| 0.0008 \| 13.02 \| 5600 \| 0.8752 \| 0.8231 \|
	\| 0.0008 \| 13.26 \| 5700 \| 0.8676 \| 0.8237 \|
	\| 0.0007 \| 13.49 \| 5800 \| 0.8677 \| 0.8237 \|
	\| 0.0008 \| 13.72 \| 5900 \| 0.8703 \| 0.8237 \|
	\| 0.0007 \| 13.95 \| 6000 \| 0.8725 \| 0.8237 \|
	\| 0.0006 \| 14.19 \| 6100 \| 0.8741 \| 0.8231 \|
	\| 0.0006 \| 14.42 \| 6200 \| 0.8758 \| 0.8237 \|
	\| 0.0008 \| 14.65 \| 6300 \| 0.8746 \| 0.8243 \|
	\| 0.0007 \| 14.88 \| 6400 \| 0.8759 \| 0.8243 \|
	\| 0.0007 \| 15.12 \| 6500 \| 0.8803 \| 0.8231 \|
	\| 0.0007 \| 15.35 \| 6600 \| 0.8808 \| 0.8237 \|
	\| 0.0007 \| 15.58 \| 6700 \| 0.8798 \| 0.8243 \|
	\| 0.0007 \| 15.81 \| 6800 \| 0.8805 \| 0.8243 \|


	### Framework versions

	- Transformers 4.20.1
	- Pytorch 1.12.0+cu113
	- Datasets 2.3.2
	- Tokenizers 0.12.1

	---
	license: apache-2.0
	tags:
	- image-classification
	- generated_from_trainer
	metrics:
	- accuracy
	model-index:
	- name: exper6_mesum5
	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. -->

	# exper6_mesum5

	This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the sudo-s/herbier_mesuem5 dataset.
	It achieves the following results on the evaluation set:
	- Loss: 0.8241
	- Accuracy: 0.8036

	## 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: 0.0002
	- train_batch_size: 16
	- eval_batch_size: 8
	- seed: 42
	- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
	- lr_scheduler_type: linear
	- num_epochs: 16
	- mixed_precision_training: Native AMP

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \|
	\|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|
	\| 3.9276 \| 0.23 \| 100 \| 3.8550 \| 0.2089 \|
	\| 3.0853 \| 0.47 \| 200 \| 3.1106 \| 0.3414 \|
	\| 2.604 \| 0.7 \| 300 \| 2.5732 \| 0.4379 \|
	\| 2.3183 \| 0.93 \| 400 \| 2.2308 \| 0.4882 \|
	\| 1.5326 \| 1.16 \| 500 \| 1.7903 \| 0.5828 \|
	\| 1.3367 \| 1.4 \| 600 \| 1.5524 \| 0.6349 \|
	\| 1.1544 \| 1.63 \| 700 \| 1.3167 \| 0.6645 \|
	\| 1.0788 \| 1.86 \| 800 \| 1.3423 \| 0.6385 \|
	\| 0.6762 \| 2.09 \| 900 \| 1.0780 \| 0.7124 \|
	\| 0.6483 \| 2.33 \| 1000 \| 1.0090 \| 0.7284 \|
	\| 0.6321 \| 2.56 \| 1100 \| 1.0861 \| 0.7024 \|
	\| 0.5558 \| 2.79 \| 1200 \| 0.9933 \| 0.7183 \|
	\| 0.342 \| 3.02 \| 1300 \| 0.8871 \| 0.7462 \|
	\| 0.2964 \| 3.26 \| 1400 \| 0.9330 \| 0.7408 \|
	\| 0.1959 \| 3.49 \| 1500 \| 0.9367 \| 0.7343 \|
	\| 0.368 \| 3.72 \| 1600 \| 0.8472 \| 0.7550 \|
	\| 0.1821 \| 3.95 \| 1700 \| 0.8937 \| 0.7568 \|
	\| 0.1851 \| 4.19 \| 1800 \| 0.9546 \| 0.7485 \|
	\| 0.1648 \| 4.42 \| 1900 \| 0.9790 \| 0.7355 \|
	\| 0.172 \| 4.65 \| 2000 \| 0.8947 \| 0.7627 \|
	\| 0.0928 \| 4.88 \| 2100 \| 1.0093 \| 0.7462 \|
	\| 0.0699 \| 5.12 \| 2200 \| 0.8374 \| 0.7639 \|
	\| 0.0988 \| 5.35 \| 2300 \| 0.9189 \| 0.7645 \|
	\| 0.0822 \| 5.58 \| 2400 \| 0.9512 \| 0.7580 \|
	\| 0.1223 \| 5.81 \| 2500 \| 1.0809 \| 0.7349 \|
	\| 0.0509 \| 6.05 \| 2600 \| 0.9297 \| 0.7769 \|
	\| 0.0511 \| 6.28 \| 2700 \| 0.8981 \| 0.7822 \|
	\| 0.0596 \| 6.51 \| 2800 \| 0.9468 \| 0.7704 \|
	\| 0.0494 \| 6.74 \| 2900 \| 0.9045 \| 0.7870 \|
	\| 0.0643 \| 6.98 \| 3000 \| 1.1559 \| 0.7391 \|
	\| 0.0158 \| 7.21 \| 3100 \| 0.8450 \| 0.7899 \|
	\| 0.0129 \| 7.44 \| 3200 \| 0.8241 \| 0.8036 \|
	\| 0.0441 \| 7.67 \| 3300 \| 0.9679 \| 0.7751 \|
	\| 0.0697 \| 7.91 \| 3400 \| 1.0387 \| 0.7751 \|
	\| 0.0084 \| 8.14 \| 3500 \| 0.9441 \| 0.7947 \|
	\| 0.0182 \| 8.37 \| 3600 \| 0.8967 \| 0.7994 \|
	\| 0.0042 \| 8.6 \| 3700 \| 0.8750 \| 0.8041 \|
	\| 0.0028 \| 8.84 \| 3800 \| 0.9349 \| 0.8041 \|
	\| 0.0053 \| 9.07 \| 3900 \| 0.9403 \| 0.7982 \|
	\| 0.0266 \| 9.3 \| 4000 \| 0.9966 \| 0.7959 \|
	\| 0.0022 \| 9.53 \| 4100 \| 0.9472 \| 0.8018 \|
	\| 0.0018 \| 9.77 \| 4200 \| 0.8717 \| 0.8136 \|
	\| 0.0018 \| 10.0 \| 4300 \| 0.8964 \| 0.8083 \|
	\| 0.0046 \| 10.23 \| 4400 \| 0.8623 \| 0.8160 \|
	\| 0.0037 \| 10.47 \| 4500 \| 0.8762 \| 0.8172 \|
	\| 0.0013 \| 10.7 \| 4600 \| 0.9028 \| 0.8142 \|
	\| 0.0013 \| 10.93 \| 4700 \| 0.9084 \| 0.8178 \|
	\| 0.0013 \| 11.16 \| 4800 \| 0.8733 \| 0.8213 \|
	\| 0.001 \| 11.4 \| 4900 \| 0.8823 \| 0.8207 \|
	\| 0.0009 \| 11.63 \| 5000 \| 0.8769 \| 0.8213 \|
	\| 0.0282 \| 11.86 \| 5100 \| 0.8791 \| 0.8219 \|
	\| 0.001 \| 12.09 \| 5200 \| 0.8673 \| 0.8249 \|
	\| 0.0016 \| 12.33 \| 5300 \| 0.8633 \| 0.8225 \|
	\| 0.0008 \| 12.56 \| 5400 \| 0.8766 \| 0.8195 \|
	\| 0.0008 \| 12.79 \| 5500 \| 0.8743 \| 0.8225 \|
	\| 0.0008 \| 13.02 \| 5600 \| 0.8752 \| 0.8231 \|
	\| 0.0008 \| 13.26 \| 5700 \| 0.8676 \| 0.8237 \|
	\| 0.0007 \| 13.49 \| 5800 \| 0.8677 \| 0.8237 \|
	\| 0.0008 \| 13.72 \| 5900 \| 0.8703 \| 0.8237 \|
	\| 0.0007 \| 13.95 \| 6000 \| 0.8725 \| 0.8237 \|
	\| 0.0006 \| 14.19 \| 6100 \| 0.8741 \| 0.8231 \|
	\| 0.0006 \| 14.42 \| 6200 \| 0.8758 \| 0.8237 \|
	\| 0.0008 \| 14.65 \| 6300 \| 0.8746 \| 0.8243 \|
	\| 0.0007 \| 14.88 \| 6400 \| 0.8759 \| 0.8243 \|
	\| 0.0007 \| 15.12 \| 6500 \| 0.8803 \| 0.8231 \|
	\| 0.0007 \| 15.35 \| 6600 \| 0.8808 \| 0.8237 \|
	\| 0.0007 \| 15.58 \| 6700 \| 0.8798 \| 0.8243 \|
	\| 0.0007 \| 15.81 \| 6800 \| 0.8805 \| 0.8243 \|


	### Framework versions

	- Transformers 4.20.1
	- Pytorch 1.12.0+cu113
	- Datasets 2.3.2
	- Tokenizers 0.12.1