modeversion1_m7_e4 / README.md

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	---
	license: apache-2.0
	tags:
	- image-classification
	- generated_from_trainer
	metrics:
	- accuracy
	model-index:
	- name: modeversion1_m7_e4
	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. -->

	# modeversion1_m7_e4

	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_mesuem7 dataset.
	It achieves the following results on the evaluation set:
	- Loss: 0.0902
	- Accuracy: 0.9731

	## 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: 4
	- mixed_precision_training: Native AMP

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \|
	\|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|
	\| 4.073 \| 0.06 \| 100 \| 3.9370 \| 0.1768 \|
	\| 3.4186 \| 0.12 \| 200 \| 3.2721 \| 0.2590 \|
	\| 2.6745 \| 0.18 \| 300 \| 2.6465 \| 0.3856 \|
	\| 2.2806 \| 0.23 \| 400 \| 2.2600 \| 0.4523 \|
	\| 1.9275 \| 0.29 \| 500 \| 1.9653 \| 0.5109 \|
	\| 1.6958 \| 0.35 \| 600 \| 1.6815 \| 0.6078 \|
	\| 1.2797 \| 0.41 \| 700 \| 1.4514 \| 0.6419 \|
	\| 1.3772 \| 0.47 \| 800 \| 1.3212 \| 0.6762 \|
	\| 1.1765 \| 0.53 \| 900 \| 1.1476 \| 0.7028 \|
	\| 1.0152 \| 0.59 \| 1000 \| 1.0357 \| 0.7313 \|
	\| 0.7861 \| 0.64 \| 1100 \| 1.0230 \| 0.7184 \|
	\| 1.0262 \| 0.7 \| 1200 \| 0.9469 \| 0.7386 \|
	\| 0.8905 \| 0.76 \| 1300 \| 0.8184 \| 0.7756 \|
	\| 0.6919 \| 0.82 \| 1400 \| 0.8083 \| 0.7711 \|
	\| 0.7494 \| 0.88 \| 1500 \| 0.7601 \| 0.7825 \|
	\| 0.5078 \| 0.94 \| 1600 \| 0.6884 \| 0.8056 \|
	\| 0.7134 \| 1.0 \| 1700 \| 0.6311 \| 0.8160 \|
	\| 0.4328 \| 1.06 \| 1800 \| 0.5740 \| 0.8252 \|
	\| 0.4971 \| 1.11 \| 1900 \| 0.5856 \| 0.8290 \|
	\| 0.5207 \| 1.17 \| 2000 \| 0.6219 \| 0.8167 \|
	\| 0.4027 \| 1.23 \| 2100 \| 0.5703 \| 0.8266 \|
	\| 0.5605 \| 1.29 \| 2200 \| 0.5217 \| 0.8372 \|
	\| 0.2723 \| 1.35 \| 2300 \| 0.4805 \| 0.8565 \|
	\| 0.401 \| 1.41 \| 2400 \| 0.4811 \| 0.8490 \|
	\| 0.3419 \| 1.47 \| 2500 \| 0.4619 \| 0.8608 \|
	\| 0.301 \| 1.52 \| 2600 \| 0.4318 \| 0.8712 \|
	\| 0.2872 \| 1.58 \| 2700 \| 0.4698 \| 0.8573 \|
	\| 0.2451 \| 1.64 \| 2800 \| 0.4210 \| 0.8729 \|
	\| 0.2211 \| 1.7 \| 2900 \| 0.3645 \| 0.8851 \|
	\| 0.3145 \| 1.76 \| 3000 \| 0.4139 \| 0.8715 \|
	\| 0.2001 \| 1.82 \| 3100 \| 0.3605 \| 0.8864 \|
	\| 0.3095 \| 1.88 \| 3200 \| 0.4274 \| 0.8675 \|
	\| 0.1915 \| 1.93 \| 3300 \| 0.2910 \| 0.9101 \|
	\| 0.2465 \| 1.99 \| 3400 \| 0.2726 \| 0.9103 \|
	\| 0.1218 \| 2.05 \| 3500 \| 0.2742 \| 0.9129 \|
	\| 0.0752 \| 2.11 \| 3600 \| 0.2572 \| 0.9183 \|
	\| 0.1067 \| 2.17 \| 3700 \| 0.2584 \| 0.9203 \|
	\| 0.0838 \| 2.23 \| 3800 \| 0.2458 \| 0.9212 \|
	\| 0.1106 \| 2.29 \| 3900 \| 0.2412 \| 0.9237 \|
	\| 0.092 \| 2.34 \| 4000 \| 0.2232 \| 0.9277 \|
	\| 0.1056 \| 2.4 \| 4100 \| 0.2817 \| 0.9077 \|
	\| 0.0696 \| 2.46 \| 4200 \| 0.2334 \| 0.9285 \|
	\| 0.0444 \| 2.52 \| 4300 \| 0.2142 \| 0.9363 \|
	\| 0.1046 \| 2.58 \| 4400 \| 0.2036 \| 0.9352 \|
	\| 0.066 \| 2.64 \| 4500 \| 0.2115 \| 0.9365 \|
	\| 0.0649 \| 2.7 \| 4600 \| 0.1730 \| 0.9448 \|
	\| 0.0513 \| 2.75 \| 4700 \| 0.2148 \| 0.9339 \|
	\| 0.0917 \| 2.81 \| 4800 \| 0.1810 \| 0.9438 \|
	\| 0.0879 \| 2.87 \| 4900 \| 0.1971 \| 0.9388 \|
	\| 0.1052 \| 2.93 \| 5000 \| 0.1602 \| 0.9508 \|
	\| 0.0362 \| 2.99 \| 5100 \| 0.1475 \| 0.9556 \|
	\| 0.041 \| 3.05 \| 5200 \| 0.1328 \| 0.9585 \|
	\| 0.0156 \| 3.11 \| 5300 \| 0.1389 \| 0.9571 \|
	\| 0.0047 \| 3.17 \| 5400 \| 0.1224 \| 0.9638 \|
	\| 0.0174 \| 3.22 \| 5500 \| 0.1193 \| 0.9651 \|
	\| 0.0087 \| 3.28 \| 5600 \| 0.1276 \| 0.9622 \|
	\| 0.0084 \| 3.34 \| 5700 \| 0.1134 \| 0.9662 \|
	\| 0.0141 \| 3.4 \| 5800 \| 0.1239 \| 0.9631 \|
	\| 0.0291 \| 3.46 \| 5900 \| 0.1199 \| 0.9645 \|
	\| 0.0049 \| 3.52 \| 6000 \| 0.1103 \| 0.9679 \|
	\| 0.0055 \| 3.58 \| 6100 \| 0.1120 \| 0.9662 \|
	\| 0.0061 \| 3.63 \| 6200 \| 0.1071 \| 0.9668 \|
	\| 0.0054 \| 3.69 \| 6300 \| 0.1032 \| 0.9697 \|
	\| 0.0041 \| 3.75 \| 6400 \| 0.0961 \| 0.9711 \|
	\| 0.0018 \| 3.81 \| 6500 \| 0.0930 \| 0.9718 \|
	\| 0.0032 \| 3.87 \| 6600 \| 0.0918 \| 0.9730 \|
	\| 0.0048 \| 3.93 \| 6700 \| 0.0906 \| 0.9732 \|
	\| 0.002 \| 3.99 \| 6800 \| 0.0902 \| 0.9731 \|


	### Framework versions

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

	---
	license: apache-2.0
	tags:
	- image-classification
	- generated_from_trainer
	metrics:
	- accuracy
	model-index:
	- name: modeversion1_m7_e4
	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. -->

	# modeversion1_m7_e4

	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_mesuem7 dataset.
	It achieves the following results on the evaluation set:
	- Loss: 0.0902
	- Accuracy: 0.9731

	## 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: 4
	- mixed_precision_training: Native AMP

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \|
	\|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|
	\| 4.073 \| 0.06 \| 100 \| 3.9370 \| 0.1768 \|
	\| 3.4186 \| 0.12 \| 200 \| 3.2721 \| 0.2590 \|
	\| 2.6745 \| 0.18 \| 300 \| 2.6465 \| 0.3856 \|
	\| 2.2806 \| 0.23 \| 400 \| 2.2600 \| 0.4523 \|
	\| 1.9275 \| 0.29 \| 500 \| 1.9653 \| 0.5109 \|
	\| 1.6958 \| 0.35 \| 600 \| 1.6815 \| 0.6078 \|
	\| 1.2797 \| 0.41 \| 700 \| 1.4514 \| 0.6419 \|
	\| 1.3772 \| 0.47 \| 800 \| 1.3212 \| 0.6762 \|
	\| 1.1765 \| 0.53 \| 900 \| 1.1476 \| 0.7028 \|
	\| 1.0152 \| 0.59 \| 1000 \| 1.0357 \| 0.7313 \|
	\| 0.7861 \| 0.64 \| 1100 \| 1.0230 \| 0.7184 \|
	\| 1.0262 \| 0.7 \| 1200 \| 0.9469 \| 0.7386 \|
	\| 0.8905 \| 0.76 \| 1300 \| 0.8184 \| 0.7756 \|
	\| 0.6919 \| 0.82 \| 1400 \| 0.8083 \| 0.7711 \|
	\| 0.7494 \| 0.88 \| 1500 \| 0.7601 \| 0.7825 \|
	\| 0.5078 \| 0.94 \| 1600 \| 0.6884 \| 0.8056 \|
	\| 0.7134 \| 1.0 \| 1700 \| 0.6311 \| 0.8160 \|
	\| 0.4328 \| 1.06 \| 1800 \| 0.5740 \| 0.8252 \|
	\| 0.4971 \| 1.11 \| 1900 \| 0.5856 \| 0.8290 \|
	\| 0.5207 \| 1.17 \| 2000 \| 0.6219 \| 0.8167 \|
	\| 0.4027 \| 1.23 \| 2100 \| 0.5703 \| 0.8266 \|
	\| 0.5605 \| 1.29 \| 2200 \| 0.5217 \| 0.8372 \|
	\| 0.2723 \| 1.35 \| 2300 \| 0.4805 \| 0.8565 \|
	\| 0.401 \| 1.41 \| 2400 \| 0.4811 \| 0.8490 \|
	\| 0.3419 \| 1.47 \| 2500 \| 0.4619 \| 0.8608 \|
	\| 0.301 \| 1.52 \| 2600 \| 0.4318 \| 0.8712 \|
	\| 0.2872 \| 1.58 \| 2700 \| 0.4698 \| 0.8573 \|
	\| 0.2451 \| 1.64 \| 2800 \| 0.4210 \| 0.8729 \|
	\| 0.2211 \| 1.7 \| 2900 \| 0.3645 \| 0.8851 \|
	\| 0.3145 \| 1.76 \| 3000 \| 0.4139 \| 0.8715 \|
	\| 0.2001 \| 1.82 \| 3100 \| 0.3605 \| 0.8864 \|
	\| 0.3095 \| 1.88 \| 3200 \| 0.4274 \| 0.8675 \|
	\| 0.1915 \| 1.93 \| 3300 \| 0.2910 \| 0.9101 \|
	\| 0.2465 \| 1.99 \| 3400 \| 0.2726 \| 0.9103 \|
	\| 0.1218 \| 2.05 \| 3500 \| 0.2742 \| 0.9129 \|
	\| 0.0752 \| 2.11 \| 3600 \| 0.2572 \| 0.9183 \|
	\| 0.1067 \| 2.17 \| 3700 \| 0.2584 \| 0.9203 \|
	\| 0.0838 \| 2.23 \| 3800 \| 0.2458 \| 0.9212 \|
	\| 0.1106 \| 2.29 \| 3900 \| 0.2412 \| 0.9237 \|
	\| 0.092 \| 2.34 \| 4000 \| 0.2232 \| 0.9277 \|
	\| 0.1056 \| 2.4 \| 4100 \| 0.2817 \| 0.9077 \|
	\| 0.0696 \| 2.46 \| 4200 \| 0.2334 \| 0.9285 \|
	\| 0.0444 \| 2.52 \| 4300 \| 0.2142 \| 0.9363 \|
	\| 0.1046 \| 2.58 \| 4400 \| 0.2036 \| 0.9352 \|
	\| 0.066 \| 2.64 \| 4500 \| 0.2115 \| 0.9365 \|
	\| 0.0649 \| 2.7 \| 4600 \| 0.1730 \| 0.9448 \|
	\| 0.0513 \| 2.75 \| 4700 \| 0.2148 \| 0.9339 \|
	\| 0.0917 \| 2.81 \| 4800 \| 0.1810 \| 0.9438 \|
	\| 0.0879 \| 2.87 \| 4900 \| 0.1971 \| 0.9388 \|
	\| 0.1052 \| 2.93 \| 5000 \| 0.1602 \| 0.9508 \|
	\| 0.0362 \| 2.99 \| 5100 \| 0.1475 \| 0.9556 \|
	\| 0.041 \| 3.05 \| 5200 \| 0.1328 \| 0.9585 \|
	\| 0.0156 \| 3.11 \| 5300 \| 0.1389 \| 0.9571 \|
	\| 0.0047 \| 3.17 \| 5400 \| 0.1224 \| 0.9638 \|
	\| 0.0174 \| 3.22 \| 5500 \| 0.1193 \| 0.9651 \|
	\| 0.0087 \| 3.28 \| 5600 \| 0.1276 \| 0.9622 \|
	\| 0.0084 \| 3.34 \| 5700 \| 0.1134 \| 0.9662 \|
	\| 0.0141 \| 3.4 \| 5800 \| 0.1239 \| 0.9631 \|
	\| 0.0291 \| 3.46 \| 5900 \| 0.1199 \| 0.9645 \|
	\| 0.0049 \| 3.52 \| 6000 \| 0.1103 \| 0.9679 \|
	\| 0.0055 \| 3.58 \| 6100 \| 0.1120 \| 0.9662 \|
	\| 0.0061 \| 3.63 \| 6200 \| 0.1071 \| 0.9668 \|
	\| 0.0054 \| 3.69 \| 6300 \| 0.1032 \| 0.9697 \|
	\| 0.0041 \| 3.75 \| 6400 \| 0.0961 \| 0.9711 \|
	\| 0.0018 \| 3.81 \| 6500 \| 0.0930 \| 0.9718 \|
	\| 0.0032 \| 3.87 \| 6600 \| 0.0918 \| 0.9730 \|
	\| 0.0048 \| 3.93 \| 6700 \| 0.0906 \| 0.9732 \|
	\| 0.002 \| 3.99 \| 6800 \| 0.0902 \| 0.9731 \|


	### Framework versions

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