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README.md
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---
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license: apache-2.0
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tags:
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- generated_from_trainer
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datasets:
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- imagefolder
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metrics:
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- accuracy
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model-index:
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- name: ViTFineTuned
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results:
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- task:
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name: Image Classification
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type: image-classification
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dataset:
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name: imagefolder
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type: imagefolder
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args: default
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metrics:
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- name: Accuracy
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type: accuracy
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value: 0.9976019184652278
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# ViTFineTuned
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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 imagefolder dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.0060
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- Accuracy: 0.9976
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 0.0005
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- train_batch_size: 16
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- eval_batch_size: 16
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- seed: 42
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- gradient_accumulation_steps: 4
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- total_train_batch_size: 64
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: linear
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- lr_scheduler_warmup_ratio: 0.1
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- num_epochs: 15
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Accuracy |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|
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| 0.2859 | 0.99 | 67 | 0.2180 | 0.9784 |
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| 0.293 | 1.99 | 134 | 0.3308 | 0.9185 |
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| 0.1444 | 2.99 | 201 | 0.1532 | 0.9568 |
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| 0.0833 | 3.99 | 268 | 0.0515 | 0.9856 |
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| 0.1007 | 4.99 | 335 | 0.0295 | 0.9904 |
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| 0.0372 | 5.99 | 402 | 0.0574 | 0.9808 |
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| 0.0919 | 6.99 | 469 | 0.0537 | 0.9880 |
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| 0.0135 | 7.99 | 536 | 0.0117 | 0.9952 |
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| 0.0472 | 8.99 | 603 | 0.0075 | 1.0 |
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| 0.0151 | 9.99 | 670 | 0.0048 | 1.0 |
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| 0.0052 | 10.99 | 737 | 0.0073 | 0.9976 |
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| 0.0109 | 11.99 | 804 | 0.0198 | 0.9952 |
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| 0.0033 | 12.99 | 871 | 0.0066 | 0.9976 |
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| 0.011 | 13.99 | 938 | 0.0067 | 0.9976 |
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| 0.0032 | 14.99 | 1005 | 0.0060 | 0.9976 |
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### Framework versions
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- Transformers 4.20.1
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- Pytorch 1.12.0+cu113
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- Datasets 2.3.2
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- Tokenizers 0.12.1
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