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---
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license: apache-2.0
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base_model: google/vit-base-patch16-224
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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: vit-base-patch16-224-RU5-10-8
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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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config: default
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split: validation
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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: 0.7833333333333333
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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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# vit-base-patch16-224-RU5-10-8
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This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.6773
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- Accuracy: 0.7833
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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: 5.5e-05
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- train_batch_size: 32
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- eval_batch_size: 32
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- seed: 42
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- gradient_accumulation_steps: 4
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- total_train_batch_size: 128
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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: 0.05
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- num_epochs: 10
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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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| 1.3605 | 0.95 | 14 | 1.2370 | 0.5167 |
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| 1.2314 | 1.97 | 29 | 1.0511 | 0.6833 |
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| 0.968 | 2.98 | 44 | 0.8919 | 0.65 |
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| 0.8135 | 4.0 | 59 | 0.7702 | 0.7667 |
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| 0.616 | 4.95 | 73 | 0.7533 | 0.75 |
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| 0.5167 | 5.97 | 88 | 0.6773 | 0.7833 |
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| 0.4063 | 6.98 | 103 | 0.6974 | 0.75 |
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| 0.3401 | 8.0 | 118 | 0.7438 | 0.75 |
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| 0.3007 | 8.95 | 132 | 0.6646 | 0.7833 |
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| 0.3154 | 9.49 | 140 | 0.6819 | 0.7833 |
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### Framework versions
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- Transformers 4.36.2
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- Pytorch 2.1.2+cu118
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- Datasets 2.16.1
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- Tokenizers 0.15.0
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