vit-base-beans / README.md

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	---
	license: apache-2.0
	tags:
	- image-classification
	- generated_from_trainer
	datasets:
	- beans
	metrics:
	- accuracy
	widget:
	- src: https://huggingface.co/nateraw/vit-base-beans/resolve/main/healthy.jpeg
	example_title: Healthy
	- src: https://huggingface.co/nateraw/vit-base-beans/resolve/main/angular_leaf_spot.jpeg
	example_title: Angular Leaf Spot
	- src: https://huggingface.co/nateraw/vit-base-beans/resolve/main/bean_rust.jpeg
	example_title: Bean Rust
	base_model: google/vit-base-patch16-224-in21k
	model-index:
	- name: vit-base-beans
	results:
	- task:
	type: image-classification
	name: Image Classification
	dataset:
	name: beans
	type: beans
	args: default
	metrics:
	- type: accuracy
	value: 0.9849624060150376
	name: Accuracy
	- task:
	type: image-classification
	name: Image Classification
	dataset:
	name: beans
	type: beans
	config: default
	split: test
	metrics:
	- type: accuracy
	value: 0.96875
	name: Accuracy
	verified: true
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	- type: precision
	value: 0.9716312056737588
	name: Precision Macro
	verified: true
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	- type: precision
	value: 0.96875
	name: Precision Micro
	verified: true
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	- type: precision
	value: 0.9714095744680851
	name: Precision Weighted
	verified: true
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	- type: recall
	value: 0.9689922480620154
	name: Recall Macro
	verified: true
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	- type: recall
	value: 0.96875
	name: Recall Micro
	verified: true
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	- type: recall
	value: 0.96875
	name: Recall Weighted
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	- type: f1
	value: 0.9689250225835592
	name: F1 Macro
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	- type: loss
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	name: loss
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	---

	<!-- 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. -->

	# vit-base-beans

	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 beans dataset.
	It achieves the following results on the evaluation set:
	- Loss: 0.0505
	- Accuracy: 0.9850

	## 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: 8
	- mixed_precision_training: Native AMP

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \|
	\|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|
	\| 0.1166 \| 1.54 \| 100 \| 0.0764 \| 0.9850 \|
	\| 0.1607 \| 3.08 \| 200 \| 0.2114 \| 0.9398 \|
	\| 0.0067 \| 4.62 \| 300 \| 0.0692 \| 0.9774 \|
	\| 0.005 \| 6.15 \| 400 \| 0.0944 \| 0.9624 \|
	\| 0.0043 \| 7.69 \| 500 \| 0.0505 \| 0.9850 \|


	### Framework versions

	- Transformers 4.16.2
	- Pytorch 1.10.0+cu111
	- Datasets 1.18.3
	- Tokenizers 0.11.0